Name: multimodal cross-device and marker-free co-registration of preclinical imaging modalities
Uploaded: 2023-10-27T12:50:00.000+00:00
Duration: 7 min 13 s
Description: Read this Scientific Article Protocol about Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities at JoVE.com

The authors would like to thank the Federal Government of North-Rhine Westphalia, and the European Union (EFRE), the German Research Foundation (CRC1382 project ID 403224013 - SFB 1382, project Q1) for funding.

The software steps of the protocol are to be performed in Imalytics Preclinical, which is referred to as the &#34;analysis software&#34; (see Table of Materials). It can load volumes as two different layers called &#34;underlay&#34; and &#34;overlay&#34;18. The underlay rendering is usually used to inspect an anatomically detailed data set on which a segmentation may be based; the overlay, which can be rendered transparently, can be used to visualize additional information within the image. Usually, the signal distribution of a tracer-based modality is displayed in the overlay. The protocol requires switching the selected layer several times. This is the layer that will be affected by the editing operations. The currently selected layer is visible in the dropdown list in the top toolbar between the mouse and window icons. One can press&#160;tab to switch between underlay and overlay, or select the desired layer directly from the dropdown list. The protocol will refer to scans (or images) used to test self-consistency and determine a differential transformation as &#34;calibration scans&#34;, in contrast to &#34;production scans&#34;&#160;which are subsequently used for content-generating imaging. The modalities used in the protocol are CT and PET. However, as described earlier, this method applies to all preclinical imaging modalities capable of acquiring volumetric data.
1. Assembling the carrier and phantom
NOTE: A suitable multimodal carrier, e.g., a mouse bed, must be available on which the phantom can be fixed. See the discussion for suggestions, frequent problems, and troubleshooting regarding this assembly.
<ol>
	<li>Prepare the fiducial markers in the phantom. 
	NOTE: The specific preparation required varies depending on the modality and tracer used. For instance, many MRI phantoms contain cavities that need to be filled with water, whereas PET, as another example, requires a radioactive tracer.</li>
	<li>Place the phantom in the carrier and secure it with a material, such as tape, that will not impair image quality. 
	&#8203;NOTE: The requirements for the phantom are detailed in the Introduction section.</li>
</ol>
2. Performing calibration scans and checking self-consistency
NOTE: This step needs to be repeated for each imaging device.
<ol>
	<li>Acquire two scans with different fields of view.
	<ol>
		<li>Place the carrier in the imaging device. Ensure it is placed in a reliable and reproducible manner.</li>
		<li>Scan according to the device manufacturer&#39;s instructions, using a large field of view that covers the entire phantom. This image will be referred to as &#34;Image A&#34; in the following steps. 
		NOTE: It is important to include all fiducials, as this scan will also be used to calculate the differential transformation matrix.</li>
		<li>Remove the carrier from the imaging device and replace it. 
		NOTE: This step ensures the carrier&#39;s placement in the device is reliable.</li>
		<li>If the imaging device does not support a limited field of view, i.e., always scans the entire field of view, one can reasonably assume self-consistency. Proceed directly to step 3.</li>
		<li>Perform a second scan according to the device manufacturer&#39;s instructions, this time using a significantly smaller field of view. This image will be called &#34;Image B&#34; in the following steps. 
		NOTE: It is important to take two scans with different fields of view. The exact position of the field of view is not critical for image B, as long as some visible information, such as phantom structures or as many fiducials as possible, is included.</li>
	</ol>
	</li>
	<li>Load the underlay.
	<ol>
		<li>Open the analysis software.</li>
		<li>Load image A as underlay: Menu File &#62; Underlay &#62; Load underlay. In the following dialog, choose the image file and click on open.</li>
		<li>If the 3D view is not present, press [Alt + 3] to activate it.</li>
		<li>Adjust the windowing: Press [Ctrl + W] and adjust the left and right vertical bars in the following dialog so that the phantom, or depending on the modality, the tracers, can be clearly distinguished. Click on Okay to close the dialog.</li>
	</ol>
	</li>
	<li>Load the overlay.
	<ol>
		<li>Load image B as overlay: Menu File &#62; Overlay &#62; Load overlay. In the following dialog, choose the image file and click on open.</li>
		<li>Change the rendering method: Menu 3D-Rendering &#62; Overlay mode &#62; check Iso rendering. 
		NOTE: Though tracer-based modalities like PET or SPECT are usually viewed with volume rendering, Iso rendering, in this case, allows for easier comparison of positions. The underlay was, by default, opened in Iso rendering.</li>
		<li>Activate the view of bounding boxes: Menu View &#62; Show symbols &#62; Show bounding box &#62; Show underlay bounding box; Menu View &#62; Show symbols &#62; Show bounding box &#62; Show overlay bounding box.</li>
	</ol>
	</li>
	<li>Check image alignment.
	<ol>
		<li>Place the mouse pointer on the 3D view and use [Ctrl + mouse wheel] to zoom the view so that both bounding boxes are fully visible. Hold [Alt + left mouse button] while moving the mouse pointer to rotate the view.</li>
		<li>Switch the selected layer to overlay.</li>
		<li>Adjust the windowing and color table: Press [Ctrl + w]. In the dropdown list at the left of the following dialog, select Yellow. Adjust the range in the following dialog to a similar one that was chosen for the underlay, and then, change the setting in small steps until the yellow rendering is just visible within the white rendering. Click on Okay to close the dialog. 
		NOTE: The rendering of Image A (underlay) is now depicted in white and surrounded by a red bounding box. The rendering of Image B (overlay) is depicted in yellow and surrounded by a yellow bounding box.</li>
		<li>Visually check if the imaging device and the method of placing the phantom are self-consistent as required. The phantom (or, depending on the modality, the tracers) should be fully aligned in underlay and overlay. The yellow rendering should be a subset of the white rendering. 
		&#8203;NOTE: The yellow bounding box should be smaller and within the red bounding box. See the Representative Results section for visual examples. If the alignment does not match, refer to the discussion for common placement problems and troubleshooting.</li>
	</ol>
	</li>
</ol>
3. Calculation of the differential transformation
<ol>
	<li>Load images of both modalities.
	<ol>
		<li>Open the analysis software.</li>
		<li>Load the CT image A as underlay: Menu File &#62; Underlay &#62; Load underlay. In the following dialog, choose the image file and press open.</li>
		<li>Load the PET image A as overlay: Menu File &#62; Overlay &#62; Load Overlay. In the following dialog, choose the image file and press open.</li>
		<li>Show multiple slice views: Press [Alt + A], [Alt + S], and [Alt + C] to show axial, sagittal, and coronal slice views. 
		NOTE: While technically, one plane would be sufficient to find the fiducials, the simultaneous view of all the planes allows for better orientation and quicker navigation.</li>
	</ol>
	</li>
	<li>Perform marker-based fusion. 
	NOTE: Step 3.2 and step 3.3 are alternate methods to align underlay and overlay. Try step 3.2 first because it is easier to reproduce and potentially more accurate. Step 3.3 is a fallback if not enough markers are clearly discernible.
	<ol>
		<li>Switch the view to only show the underlay: Menu View &#62; Layer Settings &#62; Layer Visibility &#62; uncheck overlay; Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check underlay.</li>
		<li>Switch the selected layer to the underlay.</li>
		<li>If necessary, adjust the windowing: Press [Ctrl + W] and adjust the left and right vertical bars in the following dialog to better see the fiducials. Click on Okay to close the dialog.</li>
		<li>Activate the mouse action mode &#34;create marker&#34; by clicking the marker symbol on the vertical toolbar on the left side. The mouse pointer shows a marker symbol.</li>
		<li>Perform for each fiducial of the phantom: Navigate to a fiducial. To this end, place the mouse pointer over the view of a plane and use [Alt + mouse wheel] to slice through the planes. Place the mouse pointer on the center of the fiducial and left-click.
		<ol>
			<li>This opens a dialog in which the software will suggest a name with consecutive numbers. Keep the suggested name, e.g., &#34;Marker001,&#34; and click on ok to save the marker. 
			NOTE: It is possible to use different names if you use the same marker names again for the overlay.</li>
		</ol>
		</li>
		<li>Adjust the viewing settings to show the overlay: Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check overlay. 
		NOTE: It is suggested to keep the view of the underlay activated, as it is helpful to stay orientated and be sure to identify the right marker in both modalities. If the two modalities are far out of sync or if the overlay is confusing, deactivate it: Menu View &#62; Layer Settings &#62; Layer Visibility &#62; uncheck underlay.</li>
		<li>Switch the selected layer to overlay.</li>
		<li>Adjust the windowing: If the fiducial markers are not clearly visible, press [Ctrl + W] and adjust the left and right vertical bars in the following dialog so that the fiducials can be located as best as possible. Click on Okay to close the dialog.</li>
		<li>Perform for each fiducial of the phantom: Navigate to a fiducial. To this end, place the mouse pointer over the view of a plane and use [Alt + mouse wheel] to slice through the planes. Place the mouse pointer on the center of the fiducial and left-click.
		<ol>
			<li>This opens a dialog in which the software will suggest a name with consecutive numbers. Keep the suggested name and click on ok to save the marker. 
			NOTE: It is important to have the same name for matching software markers in underlay and overlay. This is ensured if you keep the suggested names and use the same order to create the markers in both modalities. If you change the names, make sure they match.</li>
		</ol>
		</li>
		<li>Activate the views of both layers: Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check underlay; Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check overlay.</li>
		<li>Align the markers of underlay and overlay: Menu Fusion &#62; Register overlay to underlay &#62; Compute rotation and translation (markers). The following dialog shows the residual of fusion. Note this measurement and click on ok.</li>
		<li>Check the result of the alignment: The markers in the underlay and overlay should visually match. Check the discussion section for troubleshooting and notes on accuracy regarding the residual of fusion. 
		NOTE: The transformation of the overlay has been changed. To display the details of the new overlay transformation, press [Ctrl + I].</li>
	</ol>
	</li>
	<li>If marker-based fusion is not possible, perform interactive fusion. If step 3.2 is completed, directly proceed to step 3.4.
	<ol>
		<li>Activate the views of both layers: Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check underlay; Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check overlay.</li>
		<li>Activate the mouse mode &#34;interactive image fusion&#34; by clicking on the symbol on the vertical toolbar on the left side. The symbol consists of three offset ellipses with a point in the common center. The mouse pointer now shows this symbol.</li>
		<li>Ensure that the settings toolbar for the mouse mode appears in the upper area below the permanent toolbar. There are three checkboxes for underlay, overlay, and segmentation. Check overlay. Uncheck underlay and segmentation.</li>
		<li>Interactively align the overlay to the underlay: Perform rotations and translations on the different views until the underlay and overlay are aligned as best as possible:
		<ol>
			<li>Rotation: Place the mouse pointer near the edge of a view (axial, coronal or sagittal); the mouse pointer symbol is now encircled by an arrow. Hold the left mouse button and move the mouse to rotate the overlay.</li>
			<li>Translation:&#160;Place the mouse pointer near the center of a view. The mouse pointer is not encircled. Hold the left mouse button and move the mouse to move the overlay.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Create and save the differential transformation: Menu Fusion &#62; Overlay transformation &#62; Create and save differential transformation. In the following dialog, select the original overlay file and click on Open. In the second dialog, enter a file name for the differential transformation and press Save. 
	&#8203;NOTE: The software needs the original overlay file to read the original transformation and then compute the differential transformation. We suggest saving the differential transformation matrix with a filename that specifies the imaging devices used.</li>
</ol>
4. Production imaging
<ol>
	<li>Scan in both imaging devices.
	<ol>
		<li>Fix the sample (e.g., a sedated laboratory animal) on the carrier. 
		NOTE: It is important to ensure that the position of the sample within the carrier does not change between the two scans.&#160;</li>
		<li>Place the carrier in the CT device. Ensure to place the carrier in the same way as was done during the calibration scan.</li>
		<li>Scan according to the instructions of the device&#39;s manufacturer.</li>
		<li>Place the carrier in the PET device. Ensure to place the carrier in the same way as was done during the calibration scan.</li>
		<li>Scan according to the instructions of the device&#39;s manufacturer.</li>
	</ol>
	</li>
	<li>Perform application of the differential transformation.
	<ol>
		<li>Open the analysis software.</li>
		<li>Load CT file as underlay: Menu File &#62; Underlay &#62; Load underlay. In the following dialog, choose the CT image file and press ok.</li>
		<li>Load PET file as overlay: Menu File &#62; Overlay &#62; Load overlay. In the following dialog, choose the PET image file and press ok.</li>
		<li>Activate the views of both layers: Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check underlay; Menu View &#62; Layer Settings &#62; Layer Visibility &#62; check overlay.</li>
		<li>Load and apply the previously saved differential transformation matrix: Menu &#62; Fusion &#62; Overlay transformation &#62; Load and apply transform. Select the file containing the differential transformation matrix you saved in the calibration process and press open. 
		NOTE: This step changes the metadata of the overlay.</li>
		<li>Save the altered overlay: Menu &#62; File &#62; Overlay &#62; Save overlay. In the following dialog, enter a name and click on save. 
		NOTE: It is recommended to keep the unaltered original data and, therefore, save the overlay under a new name.</li>
	</ol>
	</li>
</ol>

Effective Co-Registration of Two Preclinical Imaging Modalities Across Devices

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FG is the owner of Gremse-IT GmbH, a spin-out of the RWTH Aachen University, which commercializes software for biomedical image analysis. J. J is co-owner of Phantech LLC, which commercializes phantoms for molecular imaging. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. M. T wrote the original manuscript. J. J performed the CT/SPECT scans, which are exemplary, as shown in the article. B. S and Y. Z performed the CT/MPI scans, which are exemplary in the article. F. G supervised the study and revised the article. All authors contributed to the article and approved the submitted version.

A method for multimodal image co-registration that does not require fiducial markers for production scans is presented. The phantom-based approach generates a differential transformation between the coordinate systems of two imaging modalities.
Residual of fusion and validating the differential transformation 
Upon calculating the differential transformation, the software displays a residual of fusion in millimeters, representing the root mean square error19 of the transformation. If this residual exceeds the order of magnitude of the voxel size, it is advisable to inspect the datasets for general issues. However, as all images have slight distortions, the residual cannot become arbitrarily small; it only reflects the fit of the markers used. For example, a co-registration with three markers may result in a smaller residual on the same datasets than a transformation with four well-distributed markers. This occurs because the markers themselves may be overfitted when fewer fiducials are employed. The accuracy across the entire dataset improves with a greater number of markers.
The quantitative accuracy of the method depends on the specific pair of devices used. The calculated differential transformation between the coordinate systems of two devices can be validated following these steps: Adhering to step 4 of the protocol, but using the phantom with fiducial markers as the &#34;sample&#34; again. Placing the phantom in any position, ensuring it&#39;s different from the one used for estimating the differential transformation. It&#39;s also possible to use a different phantom suitable for the respective modalities if one is available. Next, applying the differential transformation determined earlier (step 4.2.5) to align the two modalities. Then, placing markers on the images from both modalities as per step 3.2 of the protocol. To calculate the fusion residual for these markers, click on Menu Fusion &#62; Register Overlay to Underlay &#62; Showing Residual Score.
The residual error describes the average misplacement of the signal and should be in the order of the voxel size. Concrete acceptance thresholds are application-dependent and may depend on several factors, such as the stiffness and accuracy of the imaging systems but can also be affected by image reconstruction artifacts.
Troubleshooting self-consistency 
Often, difficulties with self-consistency arise from unreliable placement. A common error is placing the carrier in a laterally reversed position. Ideally, it should be mechanically inserted into the imaging device in only one direction. If this is not feasible, comprehensible markings should be added for the user. Another frequent issue is the possibility of movement in the longitudinal axis, making axial positioning unreliable. Using a spacer that can be attached at one end to secure the mouse bed in place is recommended. Custom spacers can, for example, be quickly and easily created by 3D printing them. However, some devices cannot provide self-consistency with varying fields of view. In such cases, contacting the vendor is advised, who should confirm the incompatibility and potentially address it in a future update. Otherwise, the method remains reliable if an identical field of view is maintained for all scans, including calibration and production imaging.
For some production scans with deviating placement, transformation to the calibrated position is possible, if sufficient carrier structure is discernible. For in vivo imaging, the sedated animal must remain in one carrier, and constructing a single carrier that fits securely in both devices is not always achievable. Often, a mouse bed for a tracer-based modality is used, and then placement is improvised into a CT device. For instance, in Figure 5A, an MPI mouse bed was placed atop a CT mouse bed due to mechanical constraints. Axial leeway and the possibility of rolling make this positioning unreliable. In such cases, it is recommended to design an adapter that replaces the lower mouse bed and allows for an interlocking fit. It may, for example, use trunnions attached to the lower part and additional holes in the bottom of the upper mouse bed.
However, retrospective correction for existing images is possible, as the mouse bed is detectable in the CT image. The protocol necessitates calibration scans, followed by calculating a differential transformation of the overlay to the underlay. The procedure is similar but must also map each individual production CT scan to the calibration scan, using the mouse bed structures as fiducials.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65701/65701fig05.jpg" /> 
Figure 5: Troubleshooting placement. (A) An MPI mouse bed is placed on top of a CT mouse bed. Hence, the position in the CT cannot be reliably reproduced. Self-consistency can be achieved by fusing each CT image to the reference CT image used for estimating the differential transformation. (B-D) Simplified to 2D. (B) Each production CT image is loaded as an overlay and registered to the reference CT image (underlay) using structures of the mouse bed visible in the CT. The corrected production CT image is now consistent with the reference CT and can be used with the differential transformation T. (C) An MPI&#160;overlay is registered to the reference CT image using the fiducial markers of a phantom. (D) The multimodal images are assembled. For this purpose, each CT image is mapped to the reference position with its individual differential transformation. Subsequently, the MPI overlay is also registered to the reference position using the differential transformation, which is valid for all images of the device. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65701/65701fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To map the production CT scans to the calibration scan, refer to section 3 of the protocol, incorporating the following modifications. For clarity, the description continues using the example of a CT underlay and MPI overlay: In step 3.1, load&#160;the CT calibration scan (image A) as the underlay and the CT scan to be corrected as the overlay. Utilize&#160;structures of the MPI mouse bed either as markers for step 3.2 or as visual references for step 3.3. Bypass&#160;step 3.4, but save&#160;the overlay represents the corrected CT volume (Menu File &#62; Overlay &#62; Saving overlay as). In the subsequent dialog box, input a new name and click on save. Close&#160;the overlay by navigating to Menu File &#62; Overlay &#62; Closing overlay. Load&#160;the next CT scan that requires correction as the overlay and resume&#160;the procedure from step 3.2 of the protocol. The concept underlying this step is illustrated in Figure 5B.
The mouse bed is now virtually aligned identically to the calibration scan in all the recently saved CT volumes. As part of the standard procedure, the calibration scan is registered to the MPI images using the differential transformation T (Figure 5C). To subsequently merge the CT image with MPI, always use the corrected CT volume (Figure 5D).
Troubleshooting flipped images and scaling 
The registration method introduced here assumes reasonably accurate image quality and only adjusts rotation and translation. It does not correct for flipped images or incorrect scaling. However, these two issues can be addressed manually prior to calculating the differential transformation.
Inconsistencies between data formats from different manufacturers may cause some datasets, particularly those in DICOM format, to be displayed as mirror-inverted in the software. As phantoms and mouse beds are often symmetrical, this issue may not be immediately apparent. Detecting flipped images is easier when the scan contains recognizable lettering in the respective modality, such as the raised lettering in the correct orientation seen in the phantom in Figure 3H. In the example illustrated in Figure 6, CT data is loaded as the underlay, and MPI data is loaded as the overlay. It is an in vivo scan of a mouse placed in an MPI mouse bed with attached fiducial markers. The MPI mouse bed is situated on top of a &#181;CT mouse bed (Figure 6A). By adhering to the protocol and marking the fiducials in both the underlay and overlay in a consistent direction of rotation, a visibly incongruous result is produced (Figure 6B). Upon closer inspection, however, the problem can be identified. The fiducials form an asymmetrical triangle. Observing the sides of the triangle in the axial view (Figure 6C, D) from the shortest to the middle to the longest, a clockwise rotation is evident in the CT data, while a counterclockwise rotation is apparent in the MPI data. This demonstrates that one of the images is laterally inverted. In this instance, we assume the CT data to be accurate. To rectify the MPI overlay, the image is flipped: to do that, switch the selected layer to overlay and click on Menu Edit &#62; Flip &#62; Flip X. The differential transformation calculated by the software encompasses all necessary rotations, so &#34;Flip X&#34; is sufficient even if the image appears flipped in another direction.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65701/65701fig06.jpg" /> 
Figure 6: Troubleshooting transformation. CT data are loaded as underlay with a voxel size of 0.240 mm, and MPI data as overlay with a voxel size of 0.249 mm. The mouse bed contains fiducial markers. (A) 3D view of the uncorrected overlay image. The fiducials in the CT underlay are indicated by arrows. The fiducials in the MPI overlay are visible as spheres in the NIH color table. (B) Mismatched result of a transformation performed without appropriate corrections. Residual of fusion = 6.94 mm. (C) Measurement of the distances between the fiducials in CT. Clockwise rotation from the shortest to the longest distance. (D) Measurement of the distances between the fiducials in MPI. Counter-clockwise rotation from the shortest to the longest distance. Comparison with the CT measurements results in a scaling factor of 0.928774. (E) Corrected overlay after flipping and scaling. (F) Transformation with matching results in 3D view. (G) Transformation with matching results in axial view. Residual of fusion = 0.528 mm. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65701/65701fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Datasets with incorrect voxel sizes can also be corrected manually. Since the dimensions of the phantom should be known, this can be verified in the image. The simplest method is using an edge of known length. Press [Ctrl + right mouse button] at one end of an edge, and while holding the button down, move the mouse pointer to the other end of the edge and release the button. In the subsequent dialog, the software displays the length of the measured distance in the image. In the example illustrated in Figure 6, it is apparent that the sizes are not congruent when comparing the distances between the fiducials in both modalities (Figure 6C,D). Again, the CT data is assumed to be accurate. To modify the scaling, a scaling factor (SF) is computed. As the ratio of the lengths (CT/MPI) is not precisely identical for each side of the triangle, the mean quotient is calculated: SF = ((l1CT/l1MPI) + (l2CT/l2MPI) + (l2CT/l2MPI)) / 3.
Subsequently, adjust the voxel size of the overlay by multiplying each dimension by SF. To achieve this, switch the selected layer to overlay&#160;and open Menu Edit &#62; Change Voxel Sizes. Compute each dimension, enter the value, and then click on OK. The result of both corrections is shown in Figure 6E. Following this, the overlay is registered to the underlay according to the protocol. The resulting alignment is displayed in Figure 6F,G. While this provides a quick solution for correcting an existing scan, we recommend calibrating the imaging device for production use.
Limitations 
This method is limited to spatial co-registration of existing volumetric data composed of cube-shaped voxels. It does not include a reconstruction process that computes the volume from raw data generated by the imaging device (e.g., projections in CT). Various image enhancement techniques are associated with this step, such as iterative methods20,21 and the application of artificial intelligence21. Although the described method is, in principle, applicable to all modalities that produce 3D images with cube-shaped voxels, it cannot be employed for fusing 3D data with 2D data, like an MRI volume combined with 2D infrared thermography22 or fluorescence imaging, which may be relevant in image-guided surgery applications. The registration of 3D data does not correct for distortions, such as those that occur in MRI images at the coil edge. While not mandatory, optimal results are achieved when distortions are corrected during the reconstruction process. The automated transformation also does not address flipped images or incorrect scaling. However, these two issues can be manually resolved as outlined in the troubleshooting section.
Significance of the method 
The proposed method eliminates the need for fiducial markers in production scans, offering several advantages. It benefits modalities for which marker maintenance or frequent replacement is required. For example, most MRI markers are based on moisture but tend to dry out over time, and radioactive PET markers decay. By removing the necessity for fiducials in production scans, the field of view can be reduced, leading to shorter acquisition times. This is helpful in high-throughput settings to reduce costs and to minimize x-ray dose in CT scanning. A decreased dose is desirable because radiation can impact the biological pathways of test animals in longitudinal imaging studies23.
Moreover, the method is not limited to specific modalities. The trade-off for this versatility is that fewer steps are automated. A previously published method for fusing &#181;CT and FMT data employs built-in markers in a mouse bed for every scan and can perform automated marker detection and distortion correction during reconstruction24. Other methods eliminate the need for markers by utilizing image similarity. While this approach yields good results and can also correct distortions25, it is only applicable if the two modalities provide similar enough images. This is usually not the case in the combination of an anatomically detailed modality and a tracer-based modality. However, these combinations are necessary for assessing the pharmacokinetics of targeted agents26, which have applications in areas such as anticancer nanotherapy27, 28.
Because quality control is less rigorous in preclinical compared to clinical applications, misalignment of combined imaging devices is a recognized issue29. Data affected by this misalignment could be improved retrospectively by scanning a phantom and determining the differential transformation, potentially reducing costs and minimizing animal harm. In addition to the demonstrated method that employs fiducial markers to calculate a differential transformation, which is then applied to production scans, further possibilities for image fusion are described and used. An overview, which includes references to various available software, can be found in Birkfellner et al.30.
In conclusion, the presented method offers an effective solution for multimodal image co-registration. The protocol is readily adaptable for various imaging modalities, and the provided troubleshooting techniques enhance the method&#39;s robustness against typical issues.

Different preclinical imaging modalities have distinct advantages and disadvantages. For instance, X-ray computed tomography (CT) is well-suited for examining anatomical structures with different radio densities, such as bones and lungs. It is widely used due to its rapid acquisition speed, high three-dimensional resolution, relative ease of image assessment, and versatility with or without contrast agents1,2,3. Magnetic resonance imaging (MRI) provides the most versatile soft tissue contrast without ionizing radiation4. On the other hand, tracer-based modalities like positron emission tomography (PET), single-photon emission computed tomography (SPECT), fluorescence-mediated tomography (FMT), and magnetic particle imaging (MPI) are established tools for quantitatively assessing molecular processes, metabolism, and the biodistribution of radiolabeled diagnostic or therapeutic compounds with high sensitivity. However, they lack resolution and anatomical information5,6. Therefore, more anatomy-oriented modalities are typically paired with highly sensitive ones having their strength in tracer detection7. These combinations make the quantification of tracer concentrations within a specific region of interest possible8,9. For combined imaging devices, modality co-registration is usually a built-in feature. However, it is also useful to co-register scans from different devices, e.g., if the devices were purchased separately or if a hybrid device is not available.
This article focuses on cross-modality fusion in small animal imaging, which is essential for basic research and drug development. A previous study10 points out that this can be achieved with feature recognition, contour mapping, or fiducials markers (fiducials). Fiducials are reference points for accurately aligning and correlating images from different imaging modalities. In special cases, fiducials can even be dots of Chinese ink on the skin of nude mice11; however, often, an imaging cartridge with built-in fiducial markers is used. While this is a robust and well-developed method10, using it for every scan presents practical problems. MRI-detectable fiducials are often liquid-based and tend to dry out during storage. PET requires radioactive markers, the signal of which decays according to the emitter&#39;s half-life period, which is usually short for biomedical applications, necessitating preparation shortly before the scan. Other issues, such as the mismatch in the dynamic range of the signal from the fiducial marker and the examined object, strongly impact in vivo imaging. The wide range of dynamic contrast requires frequent adaptation of the marker signal strength to the object being examined. Consequently, while a weak marker signal may not be detected in the analysis, a strong marker signal may create artifacts that impair image quality. Additionally, to consistently include the markers, the field of view must be unnecessarily large for many applications, potentially leading to higher radiation exposure, larger data volumes, longer scan times, and, in some cases, lower resolution. This may affect the health of laboratory animals and the quality of the generated data.
Transformation and differential transformation 
An image dataset consists of voxel data and metadata. Each voxel is associated with an intensity value (Figure 1A). The metadata includes a transformation specifying the dataset placement in the imaging device&#39;s coordinate system (Figure 1B) and the voxel size used to scale the coordinate system. Additional information, such as device type or scan date, can be optionally stored in the metadata. The mentioned transformation is mathematically called a rigid body transformation. Rigid body transformations are used to change the orientation or position of objects in an image or geometric space while preserving the distance between each pair of points, meaning the transformed object retains its size and shape while being rotated and translated in space. Any series of such transformations can be described as a single transformation consisting of a rotation followed by a translation. The formula used by the software to move from the data coordinate to the metric target coordinate is shown in Figure 1C, where R is an orthonormal rotation matrix, d and v are voxel indices and sizes, and t is a 3 x 1 translation vector12. The rotation is detailed in Figure 1D.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65701/65701fig01.jpg" /> 
Figure 1: 2D representation of an image dataset structure and placement in a global coordinate system. (A) An image dataset consists of voxel data and metadata. The transformation specifying the placement and voxel size are essential metadata components. (B) The image is rendered into the device&#39;s coordinate system. The necessary transformation to place the object consists of a rotation (blue) followed by a translation (green). (C) To move from the data coordinate to the target coordinate, the software uses this formula where R is an orthonormal rotation matrix, d, and v are voxel indices and sizes, and t is a 3 x 1 translation vector. (D) A rotation matrix (blue in plane A) represents the linear transformation of rotating points. Multiplying a point&#39;s coordinates by this matrix results in the new, rotated coordinates. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65701/65701fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
A differential transformation is a rigid body transformation converting coordinates from one coordinate system to another, e.g., from PET to X-ray microtomography (&#181;CT), and it can be computed using fiducial markers. At least three common points - the fiducials - are selected in both coordinate systems. From their coordinates, a mathematical transformation can be derived that converts the coordinates. The software uses the least squares method, which provides a best-fit solution to a system of equations with errors or noise in the measured data. This is called the Procrustes Problem13 and is solved using singular value decomposition. The method is reliable and robust because it leads to a unique and well-defined solution (if at least three non-collinear markers are given). Six free parameters are calculated: three for translation and three for rotation. In the following, we will use the term transformation matrix even though it technically consists of a rotation matrix and translation vector.
Each imaging device has its own coordinate system, and the software calculates a differential transformation to align them. Figure 2A,B describe how the differential transformation is determined, while Figure 2C,D describe how it is applied. The images of both modalities can have different dimensions and keep them in the process, as is shown in the example image with the fusion of CT and PET in Figure 2E.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65701/65701fig02.jpg" /> 
Figure 2: Differential transformation. (A-D) Simplified to 2D. While applicable to other modalities, it is assumed that the modalities are CT and PET for this example. (A,C) A CT image with a red bounding box is positioned in the coordinate system. Applied to the same coordinate system, the PET image with a yellow bounding box is positioned deviating.(B) Using fiducial markers that can be located in both CT and PET, a differential transformation T can be determined. This is symbolized by the arrow. The differential transformation matrix is stored. (D) The previously saved differential transformation matrix T can then be applied to each PET image. This results in a new transformation that replaces the original transformation in the metadata. (E) A CT image fused with a PET image. The transformations in the metadata of both images refer to the same coordinate system. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65701/65701fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Method and requirements 
For the presented method, a phantom containing markers visible in both modalities is scanned in both devices. It is then sufficient to mark these fiducials in the proposed software to calculate a differential transformation between the two modalities. The differential transformation must be created individually for each pair of devices. It can be saved and later applied to any new image, thereby eliminating the need for fiducial markers in subsequent scans. The final placement of the image in the coordinate system of another device can again be described as a transformation and be stored in the metadata of the image, replacing the original transformation there.
Four requirements for this method can be formulated: (1) Multimodal phantom: A phantom containing markers visible in both modalities must be available. A large selection of phantoms is commercially obtainable, and the use of 3D printing for phantom construction has been widely described14, even including the incorporation of radioisitopes15. The phantoms utilized in the following examples are listed in the Table of Materials. At least three non-collinear points are required16. The markers might be cavities that can be filled with an appropriate tracer, small objects made of a material that is easily detectable in each modality, or simply holes, cuts, or edges in the phantom itself as long as they can be identified in both modalities. (2) Multimodal carrier: A carrier, such as a mouse bed, is needed that can be fixed in a reproducible position in both devices. Ideally, it should not be possible to use it in a reversed position to avoid errors. The carrier is particularly important for in vivo imaging because it is needed to transport a sedated animal from one imaging device to the other without changing its position. Based on our experience, sedated mice are more likely to change their position in a flat mouse bed compared to a concave-shaped one. Additionally, a custom 3D-printed jig for holding the mouse&#39;s tibia to minimize motion has been previously suggested17. (3) Self-consistency: Each imaging device must provide the rotation and translation of the reconstructed volume in its reference frame in a reproducible and coherent manner. This also means that a coordinate system for the entire device is preserved when only a small region is scanned. It is part of the protocol to test an imaging device for its self-consistency. (4) Software support: The proposed software must be able to interpret the metadata (voxel size, translation, orientation) stored with the reconstructed volume provided by the device. The volume can be in DICOM, NIfTI, Analyze, or GFF file format. For an overview of various file formats, see Yamoah et al.12.
While the co-registration of two modalities is described, the procedure is also applicable to three or more modalities, for example, by co-registering two modalities to one reference modality.

<table><tbody><tr><td>177Lu</td><td></td><td></td><td>radiotracer</td></tr><tr><td>Custom-build MPI mousebed</td><td></td><td></td><td></td></tr><tr><td>Hot Rod Derenzo&amp;nbsp;</td><td>Phantech LLC. Madison, WI, USA</td><td>D271626</td><td>linearly-filled channel derenzo phantom</td></tr><tr><td>Imalytics Preclinical 3.0</td><td>Gremse-IT GmbH, Aachen, Germany</td><td></td><td>Analysis software</td></tr><tr><td>Magnetic Insight</td><td>Magnetic Insight Inc., Alameda, CA, USA</td><td></td><td>MPI Imaging device</td></tr><tr><td>Quantum GX microCT</td><td>PerkinElmer</td><td></td><td>&amp;micro;CT Imaging device</td></tr><tr><td>U-SPECT/CT-UHR</td><td>MILabs B.V., CD&amp;nbsp; Houten, The Netherlands</td><td></td><td>CT/SPECT Imaging device</td></tr><tr><td>VivoTrax (5.5 Fe mg/mL)</td><td>Magnetic Insight Inc., Alameda, CA, USA</td><td>MIVT01-LOT00004</td><td>MPI Markers</td></tr></tbody></table>

multimodal cross-device and marker-free co-registration of preclinical imaging modalities

Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic resonance imaging (MRI) combined with PET, are widely available and typically provide robustly co-registered volumes. However, separate devices are often needed to combine a standalone MRI with an existing PET-CT or to incorporate additional data from optical tomography or high-resolution X-ray microtomography. This necessitates image co-registration, which involves complex aspects such as multimodal mouse bed design, fiducial marker inclusion, image reconstruction, and software-based image fusion. Fiducial markers often pose problems for in vivo data due to dynamic range issues, limitations on the imaging field of view, difficulties in marker placement, or marker signal loss over time (e.g., from drying or decay). These challenges must be understood and addressed by each research group requiring image co-registration, resulting in repeated efforts, as the relevant details are rarely described in existing publications.
This protocol outlines a general workflow that overcomes these issues. Although a differential transformation is initially created using fiducial markers or visual structures, such markers are not required in production scans. The requirements for the volume data and the metadata generated by the reconstruction software are detailed. The discussion covers achieving and verifying requirements separately for each modality. A phantom-based approach is described to generate a differential transformation between the coordinate systems of two imaging modalities. This method showcases how to co-register production scans without fiducial markers. Each step is illustrated using available software, with recommendations for commercially available phantoms. The feasibility of this approach with different combinations of imaging modalities installed at various sites is showcased.

Figure 3&#160;and Figure 4 provide examples of a phantom that is visible in CT and contains tubular cavities filled with a tracer, in this case, for SPECT. The phantom and the tracer used are listed in the Table of Materials.
Step 2 of the protocol outlines the calibration scans and verifies the self-consistency of each imaging device. The renderings from the two scans with different fields of view should be congruent for each device. Consequently, image B, depicted in yellow, should be a subset of image A, depicted in white. An example using CT is presented in Figure 3A. Tracer-based modalities like PET or SPECT are typically visualized with volume rendering (Figure 3B). However, Iso rendering facilitates easier position comparisons. Therefore, the protocol instructs users to switch the underlay and overlay to Iso rendering, regardless of the modality used. Thus, in the SPECT example, the yellow rendering should also be a subset of the white rendering (Figure 3C). In every instance, the yellow bounding box should be smaller and positioned within the red bounding box. If the alignment does not match, the discussion highlights common placement issues and provides troubleshooting suggestions.
Step 3 of the protocol describes how to determine the differential transformation between two modalities using fiducial markers. Since the tracer in tracer-based modalities is present as a volume, the user must determine appropriate points to use as a (point-shaped) fiducial marker. In Figure 4, a CT image of the phantom is loaded as underlay, and a SPECT image is loaded as underlay. The center of a curve of a tube inside the phantom is chosen as a fiducial marker for the CT underlay, as shown by Figure 4A-C in axial, coronal, and sagittal views. The corresponding point needs to be marked in the overlay, which is illustrated in Figure 4D-F in axial, coronal, and sagittal views. The software can now compute and apply the differential transformation to the overlay. This aligns the markers in both modalities, as is shown in Figure 4G,H.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65701/65701fig03.jpg" /> 
Figure 3: Images demonstrating self-consistency. (A) CT volume. Step 2.4 of the protocol requires checking the image alignment. According to the steps in the protocol, the underlay is rendered white, while the overlay and the bounding box of the overlay are rendered yellow. Both layers are in alignment (here, the second scan is simulated by a cropped copy of the first scan). (B) SPECT imaging of the phantom with tracer-filled tubes. Volume rendering with NIH color table. (C) SPECT image in ISO rendering. The underlay is rendered white, while the overlay and the bounding box of the overlay are rendered yellow. Both layers are in alignment (here, the second scan is simulated by a cropped copy of the first scan). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65701/65701fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65701/65701fig04.jpg" /> 
Figure 4: Placement of markers in CT and SPECT images. A CT image of the phantom is loaded as underlay. A SPECT image is loaded as an overlay and rendered using the NIH color table.&#160;(A-C) Step 3.2 of the protocol requires placing markers in the underlay. The center of a curve of a tube inside the phantom is chosen as fiducial, and Marker001 is placed there, as shown by a red dot in axial, coronal, and sagittal views. (D-F)&#160;The matching marker is placed in the overlay. (G) Axial view after the transformation.&#160;(H) 3D view of the fused modalities. Maximum intension projection rendering is used to make the SPECT tracer visible within the phantom. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65701/65701fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Read this Scientific Article Protocol about Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities at JoVE.com

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic ...

multimodal-cross-device-marker-free-co-registration-preclinical

Nanyang Technological University

Research

JoVE Journal

Bioengineering

980 Views.  RWTH Aachen University. We ontwikkelen softwaretools voor preklinische beeldvorming, waaronder beeldreconstructie, fusie, segmentatie en kwantificering. Onze software is bedoeld om gebruiksvriendelijk, efficiënt en compatibel te zijn met elk beeldvormingsapparaat voor kleine dieren om reproduceerbare kwantitatieve analyse van de verkregen multimodale beeldgegevens mogelijk te maken. Automatisering is een belangrijk onderwerp in ons vakgebied omdat handmatige analyse vaak te tijdrovend, foutgevoelig en nauwelijks re...

Video: Author Spotlight: een efficiënte en robuuste software voor geautomatiseerde samensmelting van meerdere preklinische beeldvormingsmodaliteiten

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Optical sensors of ultrasound are a promising alternative to piezoelectric techniques, as has been recently demonstrated in the field of optoacoustic imaging. In medical applications, one of the major limitations of optical sensing technology is its susceptibility to environmental conditions, e.g. changes in pressure and temperature, which may saturate the detection. Additionally, the clinical environment often imposes stringent limits on the size and robustness of the sensor. In this work, the combination of pulse interferometry and fiber-based optical sensing is demonstrated for ultrasound detection. Pulse interferometry enables robust performance of the readout system in the presence of rapid variations in the environmental conditions, whereas the use of all-fiber technology leads to a mechanically flexible sensing element compatible with highly demanding medical applications such as intravascular imaging. In order to achieve a short sensor length, a pi-phase-shifted fiber Bragg grating is used, which acts as a resonator trapping light over an effective length of 350 &#181;m. To enable high bandwidth, the sensor is used for sideway detection of ultrasound, which is highly beneficial in circumferential imaging geometries such as intravascular imaging. An optoacoustic imaging setup is used to determine the response of the sensor for acoustic point sources at different positions.

Optical detection of ultrasound is impractical in many imaging scenarios because it often requires stable environmental conditions. We demonstrate an optical technique for ultrasound sensing in volatile environments with miniaturization and sensitivity levels appropriate for optoacoustic imaging in restrictive scenarios, e.g. intravascular applications.

Optical sensors of ultrasound are a promising alternative to piezoelectric techniques, as has been recently demonstrated in the field of optoacoustic ...

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

The exclusive combination of high optical contrast and excellent spatial resolution makes optoacoustics (photoacoustics) ideal for simultaneously attaining anatomical, functional and molecular contrast in deep optically opaque tissues. While enormous potential has been recently demonstrated in the application of optoacoustics for small animal research, vast efforts have also been undertaken in translating this imaging technology into clinical practice. We present here a newly developed optoacoustic tomography approach capable of delivering high resolution and spectrally enriched volumetric images of tissue morphology and function in real time. A detailed description of the experimental protocol for operating with the imaging system in both hand-held and stationary modes is provided and showcased for different potential scenarios involving functional and molecular studies in murine models and humans. The possibility for real time visualization in three dimensions along with the versatile handheld design of the imaging probe make the newly developed approach unique among the pantheon of imaging modalities used in today&#8217;s preclinical research and clinical practice.

We provide herein a detailed description of the experimental protocol for imaging with a newly developed hand-held optoacoustic (photoacoustic) system for three-dimensional functional and molecular imaging in real time. The demonstrated powerful performance and versatility may define new application areas of the optoacoustic technology in preclinical research and clinical practice.

The exclusive combination of high optical contrast and excellent spatial resolution makes optoacoustics (photoacoustics) ideal for simultaneously ...

Biofunctionalized Prussian Blue Nanoparticles for Multimodal Molecular Imaging Applications

Multimodal, molecular imaging allows the visualization of biological processes at cellular, subcellular, and molecular-level resolutions using multiple, complementary imaging techniques. These imaging agents facilitate the real-time assessment of pathways and mechanisms in vivo, which enhance both diagnostic and therapeutic efficacy. This article presents the protocol for the synthesis of biofunctionalized Prussian blue nanoparticles (PB NPs) - a novel class of agents for use in multimodal, molecular imaging applications. The imaging modalities incorporated in the nanoparticles, fluorescence imaging and magnetic resonance imaging (MRI), have complementary features. The PB NPs possess a core-shell design where gadolinium and manganese ions incorporated within the interstitial spaces of the PB lattice generate MRI contrast, both in T1 and T2-weighted sequences. The PB NPs are coated with fluorescent avidin using electrostatic self-assembly, which enables fluorescence imaging. The avidin-coated nanoparticles are modified with biotinylated ligands that confer molecular targeting capabilities to the nanoparticles. The stability and toxicity of the nanoparticles are measured, as well as their MRI relaxivities. The multimodal, molecular imaging capabilities of these biofunctionalized PB NPs are then demonstrated by using them for fluorescence imaging and molecular MRI in vitro.

This protocol describes the synthesis of biofunctionalized Prussian blue nanoparticles and their use as multimodal, molecular imaging agents. The nanoparticles have a core-shell design where gadolinium or manganese ions within the nanoparticle core generate MRI contrast. The biofunctional shell contains fluorophores for fluorescence imaging and targeting ligands for molecular targeting.

Multimodal, molecular imaging allows the visualization of biological processes at cellular, subcellular, and molecular-level resolutions using multiple, ...

Hybrid &#181;CT-FMT imaging and image analysis

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a group of patients. With recent advances in brain imaging and image processing, LSM has become a popular tool in cognitive neuroscience. LSM can provide fundamental insights into the functional architecture of the human brain for a variety of cognitive and non-cognitive functions. A crucial step in performing LSM studies is the segmentation of lesions on brains scans of a large group of patients and registration of each scan to a common stereotaxic space (also called standard space or a standardized brain template). Described here is an open-access, standardized method for infarct segmentation and registration for the purpose of LSM, as well as a detailed and hands-on walkthrough based on exemplary cases. A comprehensive tutorial for the manual segmentation of brain infarcts on CT scans and DWI or FLAIR MRI sequences is provided, including criteria for infarct identification and pitfalls for different scan types. The registration software provides multiple registration schemes that can be used for processing of CT and MRI data with heterogeneous acquisition parameters. A tutorial on using this registration software and performing visual quality checks and manual corrections (which are needed in some cases) is provided. This approach provides researchers with a framework for the entire process of brain image processing required to perform an LSM study, from gathering of the data to final quality checks of the results.

Provided here is a practical tutorial for an open-access, standardized image processing pipeline for the purpose of lesion-symptom mapping. A step-by-step walkthrough is provided for each processing step, from manual infarct segmentation on CT/MRI to subsequent registration to standard space, along with practical recommendations and illustrations with exemplary cases.

In lesion-symptom mapping (LSM), brain function is inferred by relating the location of acquired brain lesions to behavioral or cognitive symptoms in a ...

Behavior

Guided Tumor Inoculation and Multimodal Imaging in Mice

A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research

Preclinical intravital imaging such as microscopy and optical coherence tomography have proven to be valuable tools in cancer research for visualizing the tumor microenvironment and its response to therapy. These imaging modalities have micron-scale resolution but have limited use in the clinic due to their shallow penetration depth into tissue. More clinically applicable imaging modalities such as CT, MRI, and PET have much greater penetration depth but have comparatively lower spatial resolution (mm scale). 
To translate preclinical intravital imaging findings into the clinic, new methods must be developed to bridge this micro-to-macro resolution gap. Here we describe a dorsal skinfold window chamber tumor mouse model designed to enable preclinical intravital and clinically applicable (CT and MR) imaging in the same animal, and the image analysis platform that links these two disparate visualization methods. Importantly, the described window chamber approach enables the different imaging modalities to be co-registered in 3D using fiducial markers on the window chamber for direct spatial concordance. This model can be used for validation of existing clinical imaging methods, as well as for the development of new ones through direct correlation with "ground truth" high-resolution intravital findings. 
Finally, the tumor response to various treatments-chemotherapy, radiotherapy, photodynamic therapy-can be monitored longitudinally with this methodology using preclinical and clinically applicable imaging modalities. The dorsal skinfold window chamber tumor mouse model and imaging platforms described here can thus be used in a variety of cancer research studies, for example, in translating preclinical intravital microscopy findings to more clinically applicable imaging modalities such as CT or MRI.

Author Spotlight: Integrating High-Resolution Intravital Imaging and MRI to Enhance Stereotactic Body Radiation Therapy Planning

Translation of Intravital microscopy findings is challenged by its shallow depth penetration into tissue. Here we describe a dorsal window chamber mouse model that enables co-registration of intravital microscopy and clinically applicable imaging modalities (e.g., CT, MRI) for direct spatial correlation, potentially streamlining clinical translation of intravital microscopy findings.

Preclinical intravital imaging such as microscopy and optical coherence tomography have proven to be valuable tools in cancer research for visualizing the ...

Cancer Research

PET Imaging and Biodistribution Analysis in Mice Using BCAMs and SaaS Tools

Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis in Mice

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as biomarker modulation, receptor occupancy, and biodistribution of compounds of interest. In biodistribution studies, experimental subjects are often longitudinally imaged after receiving the test article. The images are then analyzed to derive the compound&#39;s distribution profile in various organs at different timepoints. This constitutes a crucial step in drug development to understand the distribution and potentially binding profile of an investigative compound. Standard/manual methods of PET imaging-based biodistribution analyses, however, are labor-intensive and time-consuming and are often associated with high inter-operator variability. Further, it is challenging to keep the animals&#39; positions consistent across different timepoints. To address these shortcomings, a series of mouse Body Conforming Animal Molds (BCAMs) were used to enable rigid and consistent positioning of animals during PET/CT imaging acquisition. Further, a Software-as-a-Service (SaaS) platform consisting of a cloud-based Organ Probability Map (OPM) and an artificial intelligence-powered segmentation tool were employed to enable reliable and automated quantitation of in vivo PET imaging data. The workflow presented here includes (1) prepping mice for imaging with the BCAMs, including the proper implantation of subcutaneous tumors to be compatible with the molds, (2) acquiring PET/CT images with BCAMs using the G8 scanner, and 3) performing automated organ segmentation and biodistribution analysis using the cloud-based SaaS. [18F]FDG was used as an exemplar tracer here, but other biomarkers and/or radio-labeled compounds can be readily adapted into the workflow. This procedure can be executed accurately and effectively with minimal training, and the automated PET data analysis yielded satisfactory results consistent with the manual method.

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

This protocol describes the procedure for performing in vivo PET imaging on mice using Body Conforming Animal Molds (BCAMs) in the G8 PET/CT scanner. Technical details on mouse preparation, including proper tumor implantation, optimal positioning, BCAM-assisted PET/CT image acquisition, and data analysis, are provided.

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as ...

4D Ultrasound and MALDI Imaging for Cardiac and Vascular Analysis in Mice

Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

Cardiovascular disease (CVD) is the leading cause of death in the United States. Damage in&#160;the cardiovascular system can be due to environmental exposure, trauma, drug toxicity, or numerous other factors.&#160;As a result, cardiac tissue and vasculature undergo structural changes and display diminished function. The damage and the resulting remodeling can be detected and quantified with ultrasound (US) imaging at the organ level and mass spectrometry imaging (MSI) at the molecular level. This manuscript describes an innovative methodology for studying murine cardiac pathophysiology, coupling in vivo four-dimensional (4D) ultrasound imaging and analysis with ex vivo matrix-assisted laser desorption/ionization (MADLI) MSI of the heart. 4D ultrasound can provide dynamic volumetric measurements, including radial displacement, surface area strain, and longitudinal strain throughout an entire cardiac cycle. In the vasculature, MSI and ultrasound are used to assess vessel wall compositions, hemodynamics, and vessel wall dynamics. The methodology can be tailored to study a myriad of CV diseases by adjusting functional metrics of interest and/or varying MALDI MSI protocol to target specific molecules. MALDI MSI can be used to study lipids, small metabolites, peptides, and glycans. This protocol outlines the use of MALDI MSI for untargeted lipidomic analysis and the use of ultrasound imaging for cardiovascular hemodynamics and biomechanics.

Here, we describe a protocol to use in vivo four-dimensional ultrasound imaging and ex vivo mass spectrometry imaging to assess biomechanical and biomolecular alterations in the murine cardiovascular system. This technique is applied to analyze cardiac remodeling in surgically induced myocardial infarction and vascular changes in aging animals.

Cardiovascular disease (CVD) is the leading cause of death in the United States. Damage in&#160;the cardiovascular system can be due to environmental ...

Multilayer Imaging with Microprism in Head-Fixed and Freely Moving Animals

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

With the advancement of multi-photon microscopy and molecular technologies, fluorescence imaging is rapidly growing to become a powerful approach for studying the structure, function, and plasticity of living brain tissues. In comparison to conventional electrophysiology, fluorescence microscopy can capture the neural activity as well as the morphology of the cells, enabling long-term recordings of the identified neuron populations at single-cell or subcellular resolution. However, high-resolution imaging typically requires a stable, head-fixed setup that restricts the movement of the animal, and the preparation of a flat surface of transparent glass allows visualization of neurons at one or more horizontal planes but is limited in studying the vertical processes running across different depths. Here, we describe a procedure to combine a head plate fixation and a microprism that gives multilayer and multimodal imaging. This surgical preparation not only gives access to the entire column of the mouse visual cortex but allows two-photon imaging in a head-fixed position and one-photon imaging in a freely moving paradigm. Using this approach, one can sample identified cell populations across different cortical layers, register their responses under head-fixed and freely moving states, and track the long-term changes over months. Thus, this method provides a comprehensive assay of the microcircuits, enabling direct comparison of neural activities evoked by well-controlled stimuli and under a natural behavioral paradigm.

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

When integrated with a head-plate and an optical design compatible with both single- and two-photon microscopes, the microprism lens presents a significant advantage in measuring neural responses in a vertical column under diverse conditions, including well-controlled experiments in head-fixed states or natural behavioral tasks in freely moving animals.

With the advancement of multi-photon microscopy and molecular technologies, fluorescence imaging is rapidly growing to become a powerful approach for ...

Neuroscience

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Hyperpolarized 129Xe gas MRI is an emerging technique to evaluate and measure regional lung function including pulmonary gas distribution and gas exchange. Chest computed tomography (CT) still remains the clinical gold standard for imaging of the lungs, though, in part due to the rapid CT protocols that acquire high-resolution images in seconds and the widespread availability of CT scanners. Quantitative approaches have enabled the extraction of structural lung parenchymal, airway and vascular measurements from chest CT that have been evaluated in many clinical research studies. Together, CT and 129Xe MRI provide complementary information that can be used to evaluate regional lung structure and function, resulting in new insights into lung health and disease. 129Xe MR-CT image registration can be performed to measure regional lung structure-function to better understand lung disease pathophysiology, and to perform image-guided pulmonary interventions. Here, a method for 129Xe MRI-CT registration is outlined to support implementation in research or clinical settings. Registration methods and applications that have been employed to date in the literature are also summarized, and suggestions are provided for future directions that may further overcome technical challenges related to 129Xe MR-CT image registration and facilitate broader implementation of regional lung structure-function evaluation.

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

CT and 129Xe MRI provide complementary lung structure-function information that can be exploited for regional analysis using image registration. Here, we provide a protocol that builds from the existing literature for 129Xe MR to CT image registration using open-source platforms.

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

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