Name: construction and application of cerebral functional region-based cerebral blood flow atlas using magnetic resonance imaging-arterial spin labeling
Uploaded: 2024-05-31T14:35:00.000+00:00
Duration: 5 min 23 s
Description: Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately ...

This study received significant support and modeling assistance from the R&#38;D department of Beijing Intelligent Entropy Science &#38; Technology Co Ltd., Beijing, China.

This study was approved by the Institutional Review Board of Beijing Dongzhimen Hospital, Beijing, China. An MRI scanner was used with pulsed ASL (PASL) sequence based on turbo gradient spin echo (TGSE) for 3D arterial spin labeling (3D-ASL) with the following parameters: TR 4000 ms, TE 25 ms, bolus duration 700 ms, inversion time 1990 ms. The software tools used in this research are listed in the Table of Materials.
1. Data collection and preparation
NOTE: The variance in parameters remains unaffected by the research approach. Both DICOM and NIFTI formats are used for storing digital medical imaging data, with DICOM being the usual output from clinical imaging devices. However, the NIFTI format is often preferred for computational convenience in research activities. Converting from DICOM to NIFTI is a straightforward and common practice17. In this study, authentic DICOM data were acquired and converted into NIFTI format. The data was acquired using a 1.5 Tesla MRI scanner. In the cross-image registration process of this study, the fluid-attenuated inversion recovery (FLAIR) sequence was mainly used and fused with CBF images. The CBF Atlas tool (Table of Materials) used in this study is commercial software.
<ol>
	<li>Copy data to the designated working directory.
	<ol>
		<li>Copy all NIFTI data to a customized working directory. 
		NOTE: The working directory is the same in the operating system and MATLAB. The research follows the Right Anterior Superior (RAS) orientation standard.</li>
		<li>Go to the directory housing the data within MATLAB&#39;s current working directory, and use the niftiread function to load FLAIR data into the workspace. Use the size function to check the dimensions of the FLAIR sequence. Call the Flair_Slice command to view the FLAIR sequence (as shown in Figure 1). Use the specific commands as follows: 
		FLAIR_XLF = niftiread(&#39;FLAIR_XFL.nii&#39;); 
		size(FLAIR_XLF) 
		&#8203;Flair_Slice(FLAIR_XLF);</li>
		<li>Refer to Figure 1 for an image of the interactive graphic user interface (GUI) for the FLAIR sequence. Use the bottom scroll bar to quickly browse the different sequences.</li>
	</ol>
	</li>
	<li>Quickly check images of CBF.
	<ol>
		<li>Use the niftiread function to load CBF data into the workspace. Use the size function to check the dimensions of the CBF sequence. Call the CBF_Slice command to view the CBF sequence (as shown in Figure 2). Use the specific commands as follows: 
		CBF_XLF = niftiread(&#39;CBF_XFL.nii&#39;); 
		size(CBF_XLF) 
		CBF_Slice(CBF_XLF);</li>
		<li>Refer to Figure 2 for an interactive GUI screenshot of the CBF sequence. Use the bottom scroll bar to quickly browse the different sequences. 
		NOTE: In Figure 2, the CBF value range is usually 0-120 mL/100 g/min. Figure 2 uses a color map to represent different CBF levels in different colors.</li>
	</ol>
	</li>
</ol>
2. Segmentation of cerebral functional regions from FLAIR sequence
NOTE: The FLAIR sequence serves as both structural imaging and provides excellent pathological diagnostic capabilities. Therefore, fusing FLAIR with CBF holds important diagnostic value in clinics. This study segments major cerebral functional regions from the FLAIR sequence.
<ol>
	<li>In the workspace, call the FLAIR_Segment function and run the pretrained 3-D U-Net-based image segmentation program to automatically generate triplanar views of cerebral functional regions segmentation, as shown in Figure 3. Each color in Figure 3 represents a distinct functional region.</li>
	<li>For real-time inspection of different cerebral functional regions, use the crosshair interaction (Figure 3). Click and drag the center of the crosshair for an arbitrary 3D examination of the reconstructed brain anatomy. 
	NOTE: The GUI in Figure 3 also enables adjustment of the grayscale intensity range, contrast, and brightness of the triplanar views.</li>
	<li>Press and drag the left mouse button over any region of the images for real-time modification of brightness and contrast levels. Release the mouse button to confirm and finalize the adjustments.</li>
</ol>
3. Triplanar views of CBF distribution across cerebral functional regions
NOTE: Examining CBF distribution across different cerebral functional regions facilitates precise clinical judgments of patient conditions. Under the functional region framework from Figure 3, incorporating the exact CBF values from the CBF sequence and presenting them in triplanar views allows comprehensive physician inspection.
<ol>
	<li>Call the CBF_triplanar function to generate the triplanar GUI view shown in Figure 4, displaying CBF spatial distribution across functional regions. Move the crosshair to allow examination of CBF distribution in regions of interest.</li>
	<li>Click the Data Tips button on the top right corner of the GUI to display the CBF values at any position.</li>
	<li>Press and drag the left mouse button over any region of the images for real-time modification of brightness and contrast levels. Release the mouse button to confirm and finalize the adjustments.</li>
</ol>
4. CBF atlas across major cerebral functional regions
NOTE: Normalizing the CBF probability distributions across different functional regions generates the Cerebral Functional Regions CBF Atlas, expressing the CBF levels across cerebral functional regions of the subject.
<ol>
	<li>Call the CBF_Atlas function to convert the CBF spatial distribution shown in Figure 4 into a CBF Atlas (as shown in Figure 5). 
	NOTE: In Figure 5, the x-axis represents different cerebral functional regions, and the y-axis represents different CBF levels; the different colors denote different probability levels (the redder the color, the more voxels present).</li>
	<li>Click the Zoom In/Out button on the top right of the GUI shown in Figure 5 to scale partial images. 
	NOTE: The curve in Figure 5 connects the average CBF across regions.</li>
</ol>
5. Significant differences in CBF_Atlas between healthy subjects and CCI patients
NOTE: Using the same process described in sections 1-4, the average CBF values across different cerebral functional regions can be obtained for CCI patients.
<ol>
	<li>Utilize the CBF_Compare function to generate a comparative plot of the CBF curve from a healthy subject versus a CCI patient (Figure 6).</li>
	<li>Observe the significant differences between the patient&#39;s CBF curve (colored in black) and the healthy curve (colored in red) (Figure 6). Identify the functional regions with more pronounced CBF drops in the patient. Integrating step 3.3, re-examine the regions where the patient performed worse compared to the other regions. 
	NOTE: Step 3.3 allows for the viewing of the patient&#39;s CBF levels in three-dimensional space, meaning doctors can utilize the triplanar GUI view shown in Figure 4 to examine the CBF at any position of the patient. Here, the degree of CBF decline varies in different locations for CCI patients. Doctors can revisit Figure 4 to focus on brain regions with significant CBF decline.</li>
	<li>Use the icons in the GUI&#39;s upper right corner (Figure 5) to access functions such as zooming out, zooming in, returning to the global view, and marking the coordinates of the selected pixel. 
	NOTE: Using the same principle, pre-treatment and post-treatment results from the same patient can also be compared using the approach in Figure 6 to evaluate clinical efficacy over time.</li>
</ol>

MRI-Arterial Spin Labeling Integration to Detect and Monitor Cerebral Blood Flow 

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The software tool CBF Atlas V1.0 is a product of Beijing Intelligent Entropy Science &amp; Technology Co., Ltd. The intellectual property rights of this software tool belong to the company. The authors have no conflicts of interest to declare.

The key steps (sections 3 and 4) constitute the basis for constructing the CBF Atlas, quantifying CBF distribution across cerebral functional regions. Step 4.2 explicitly delineates CBF levels for each brain area, pioneering a new technique. This not only provides physicians with a global view of patient CBF but also quantitative measurements of individual functional regions. Step 5.1&#160;demonstrates that the CBF Atlas holds substantial clinical diagnostic utility distinguishing CCI from healthy controls.
Several technical refinements could enhance this promising protocol, mainly by utilizing MRI-ASL imaging. Standardization regarding scanning accuracy, CBF normalization, and synchronization of structural and CBF images is needed. Additionally, constructing a multi-subject healthy CBF Atlas as a diagnostic baseline via expanded cohorts warrants further investigations. Accumulating CBF atlas patterns across diverse cerebral pathophysiologies through clinical collaborations also represents important future work.
A current limitation is CBF, though invaluable, is not the sole biomarker for brain conditions18,19.&#160;Integrating other vital clinical markers such as phosphorylated tau protein (p-tau) in cerebrospinal fluid, electroencephalography (EEG), and cerebral metabolic rates (e.g., glucose metabolic rate) could augment diagnostics.
Accurately calculating the CBF distribution for each functional brain region and presenting it in the form of an atlas necessitates aligning brain functional structural imaging with CBF sequences in three-dimensional space and calculating the CBF distribution for each region. This effort represents the main innovation of our study. The resulting CBF atlas, with the x-axis representing brain functional regions and the y-axis showing the CBF distribution for each region, allows for comparing pre- and post-treatment outcomes in patients and enables quantitative comparisons between healthy and diseased groups20. This makes the assessment of CBF more precise (enabling accurate comparison of CBF distribution across regions) rather than relying on experiential judgement21.
Furthermore, the potential of longitudinal CBF atlases to monitor treatment efficacy and track the progression of neurological conditions over time offers a promising avenue for personalized medicine. By establishing a baseline of CBF distribution within healthy cerebral functional regions, clinicians can more accurately gauge the impact of therapeutic interventions and make informed decisions regarding patient care.
In conclusion, developing the CBF Atlas utilizing MRI-ASL imaging is a promising step toward improving the diagnostic and therapeutic landscape for cerebral conditions. This technique has the potential to contribute to a more comprehensive understanding of cerebral blood flow dynamics and may support the development of precision medicine approaches that aim to tailor interventions based on individual patient&#39;s cerebral perfusion patterns. Future directions for this research include refining the methodology, standardizing protocols, and expanding clinical validations to fully realize the potential of CBF atlases in neurology and beyond.

In the realm of neuroimaging, the quest for precise, noninvasive tools to assess cerebral function and pathology remains paramount. Among these, cerebral blood flow (CBF) stands as a vital indicator, reflecting the metabolic demands and health status of brain tissue1. Traditional approaches often entail empirical assessments, relying heavily on the expertise of clinicians to interpret images and discern pathological changes2. However, advancements in magnetic resonance imaging (MRI) techniques, particularly arterial spin labeling (ASL)3, offer a promising avenue for quantifying CBF with greater accuracy and objectivity4,5.
This study presents a pioneering methodology that integrates three-dimensional ASL (3D-ASL) with structural MRI to construct a comprehensive CBF atlas across cerebral functional regions6. By leveraging this novel approach, clinicians can not only obtain a global perspective of CBF but also delve into specific functional areas, allowing for a nuanced understanding of cerebral perfusion patterns7,8. This improvement in resolution is a direct result of technological progress in imaging equipment rather than the use of interpolated voxels. It is worth noting that the majority of mainstream MRI devices available on the market today typically offer imaging precision better than 1.5 mm9. These advancements in imaging technology have paved the way for more detailed and accurate CBF assessments. This represents a paradigm shift from conventional imaging, which often lacks the resolution to detect subtle changes in CBF associated with early-stage pathologies10.
The genesis of this methodology lies in the imperative to address the diagnostic challenges posed by cerebral conditions, including chronic cerebral ischemia (CCI) and other neurological disorders11,12. These conditions necessitate precise and timely assessments to guide therapeutic interventions effectively13,14. By comparing CBF atlases between healthy individuals and patients with CCI, this study unveils significant disparities in regional CBF distributions, offering insights into disease pathology and potential treatment avenues.
The utility of this MRI-ASL approach extends beyond diagnosis, encompassing therapeutic evaluation and monitoring of disease progression15. Longitudinal CBF atlases hold promise in tracking treatment responses and rehabilitation outcomes, providing clinicians with invaluable tools for personalized patient management. Moreover, the ability to discern subtle CBF changes may serve as an early biomarker for impending tissue abnormalities, enabling proactive interventions to mitigate neurological damage before it becomes irreversible16.
While this methodology represents an advanced tool, several avenues for refinement and expansion merit consideration. Standardizing scanning protocols, CBF normalization techniques, and constructing multi-subject healthy CBF atlases are crucial steps toward enhancing diagnostic accuracy and clinical utility. Collaborative efforts across diverse cerebral pathologies are essential to validate and refine this approach for widespread clinical adoption.
This study introduces a novel approach whereby MRI-derived CBF atlases offer clinicians deep insights into cerebral function and pathology. By bridging the gap between imaging group and clinical interpretation, this methodology has the potential to revolutionize the diagnosis and management of a myriad of neurological conditions, ushering in a future of precision medicine tailored to the unique needs of each patient.

<table><tbody><tr><td>CBF Atlas</td><td>Intelligent Entropy</td><td>CBF Atlas V1.0</td><td>Beijing Intelligent Entropy Science &amp;amp; Technology Co Ltd.&lt;br/&gt; Modeling for Thyroid Disease</td></tr><tr><td>MATLAB</td><td>MathWorks</td><td>2023B</td><td>Computing and visualization</td></tr><tr><td>MRI Device</td><td>Siemens</td><td>Amria 1.5 T</td><td>MRI scanner</td></tr></tbody></table>

construction and application of cerebral functional region-based cerebral blood flow atlas using magnetic resonance imaging-arterial spin labeling

Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately detect early signs of reversible tissue damage, underlining the need for innovative diagnostic tools that can quantify changes in cerebral blood flow (CBF) with high specificity and sensitivity.&#160;This study integrates three-dimensional arterial spin labeling (3D-ASL) with structural MRI to develop comprehensive CBF atlases that cover all main functional regions of the brain. This innovative magnetic resonance imaging- arterial spin labeling (MRI-ASL) methodology provides a rapid and noninvasive means of quantifying region-specific CBF, offering a detailed view of CBF levels across different functional regions.The comparison between chronic cerebral ischemia (CCI) patients and healthy subjects revealed significantly diminished CBF across the cerebral functional regions in the constructed CBF atlases for the former. This approach not only allows for the efficient identification of CCI by analyzing concurrent decreases in CBF across critical areas relative to healthy distributions but also enables the tracking of treatment responses and rehabilitation progress through longitudinal CBF atlases.The CBF atlas developed using the MRI-ASL technique represents a novel advancement in the field of cerebral diagnostics and patient care. By comparing regional CBF levels against normative standards, this method enhances diagnostic capabilities, enabling clinicians to provide personalized care to patients with cerebral conditions.

This investigation utilizes actual patient data acquired using a 1.5 T MRI scanner to validate the cerebral blood flow (CBF) quantification and atlas construction methodology. The preprocessing steps included FLAIR structural images (Figure 1), CBF images (Figure 2), and triplanar fused images (Figure 3 and Figure 4).
Cerebral functional regions were segmented from FLAIR scans using a pre-trained deep learning model (Figure 3). The ASL-CBF images were then registered to align with the FLAIR space. Fusing the region labels with corresponding CBF values generated triplanar views exhibiting CBF distribution across segmented areas (Figure 4).
Further processing constructed a CBF atlas for the healthy subject (Figure 5) and CCI patient (Figure 6). The x-axis denotes regions, the y-axis denotes CBF levels, and color intensity reflects probability. The curve indicates the mean regional CBF. Comparing the two CBF atlases revealed significantly reduced CBF across all cerebral functional regions in the CCI patient versus the healthy subject.
This technique successfully differentiated normal versus abnormal CBF distribution at global and regional resolution levels. The construction of 3D cerebral functional regions CBF atlases based on MRI-ASL imaging holds substantial potential for assisted diagnosis of various cerebral pathophysiologies.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66853/66853fig01.jpg" /> 
Figure 1: Interactive graphical user interface for FLAIR slice view. The figure shows the GUI for viewing FLAIR slices. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66853/66853fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66853/66853fig02.jpg" /> 
Figure 2: Interactive graphical user interface for CBF slice view. The figure shows the GUI for viewing CBF slices. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66853/66853fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66853/66853fig03.jpg" /> 
Figure 3: Segmentation of cerebral functional regions based on FLAIR imaging sequence. Cerebral functional regions extraction and triplanar views based on FLAIR sequence. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66853/66853fig03large.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/66853/66853fig04.jpg" /> 
Figure 4: Triplanar views of CBF spatial distribution across cerebral functional regions. Triplanar views showing CBF distribution across cerebral regions. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66853/66853fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66853/66853fig05.jpg" /> 
Figure 5: Cerebral Functional Regions CBF Atlas. CBF atlas constructed by the probability distribution of region-specific CBF levels. This study adopts a commonly used segmentation of 32 regions. These regions include background, left cerebral white matter, left cerebral cortex, left lateral ventricle, left inferior lateral ventricle, left cerebellum white matter, left cerebellum cortex, left thalamus, left caudate, left putamen, left pallidum, third ventricle, fourth ventricle, brain stem, left hippocampus, left amygdala, left accumbens area, left ventral DC, right cerebral white matter, right cerebral cortex, right lateral ventricle, right inferior lateral ventricle, right cerebellum white matter, right cerebellum cortex, right thalamus, right caudate, right putamen, right pallidum, right hippocampus, right amygdala, right accumbens area, and right ventral DC. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66853/66853fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66853/66853fig06.jpg" /> 
Figure 6: Comparison of Cerebral Functional Regions CBF Atlas. Comparison reveals significantly reduced CBF across cerebral functional regions in a CCI patient vs a healthy subject. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66853/66853fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

This study integrated magnetic resonance imaging- arterial spin labeling images to derive cerebral blood flow (CBF) atlas for cerebral functional regions. Comparing typical healthy and chronic cerebral ischemia CBF atlases revealed significant differences in regional CBF distributions, enabling rapid, noninvasive assessments of functional CBF to assist in diagnosis and evaluate therapeutics.

Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling

Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately ...

construction-application-cerebral-functional-region-based-cerebral

Nanyang Technological University

Research

JoVE Journal

Medicine

471 Views.  Beijing University of Chinese Medicine. De CBF-atlas op basis van MRI biedt een niet-invasieve manier om de cerebrale bloedstroomverdeling in het gebied van het functionele hersengebied te geven, en kan mogelijk dienen als een belangrijk hulpmiddel op het gebied van hersenaandoeningen. Het integreren van hoogwaardige canonieke indicatoren uit beeldvorming met fysiologische structuren en biochemische markers biedt artsen nauwkeurigere diametrische informatie. Multimodale fusie per scherm, op AI gebaseerde beeldvormingsherkenning en ...

Video: Auteur in de schijnwerpers: niet-invasieve bepaling van de cerebrale bloedstroom in het menselijke functionele hersengebied voor de diagnose van neurologische aandoeningen

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

This demonstrates a MR imaging method to measure the spatial distribution of pulmonary blood flow in healthy subjects 
during normoxia (inspired O2, fraction (FIO2) = 0.21) hypoxia (FIO2 = 0.125), and hyperoxia 
(FIO2 = 1.00). In addition, the physiological responses of the subject are monitored in the MR scan environment. MR images 
were obtained on a 1.5 T GE MRI scanner during a breath hold from a sagittal slice in the right lung at functional residual capacity. An arterial 
spin labeling sequence (ASL-FAIRER) was used to measure the spatial distribution of pulmonary blood flow 1,2 and a multi-echo fast 
gradient echo (mGRE) sequence 3 was used to quantify the regional proton (i.e. H2O) density, allowing the quantification 
of density-normalized perfusion for each voxel (milliliters blood per minute per gram lung tissue). 
With a pneumatic switching valve and facemask equipped with a 2-way non-rebreathing valve, different oxygen concentrations 
were introduced to the subject in the MR scanner through the inspired gas tubing. A metabolic cart collected expiratory gas via expiratory tubing. Mixed expiratory O2 and CO2 concentrations, oxygen consumption, carbon dioxide production, respiratory exchange ratio, 
respiratory frequency and tidal volume were measured. Heart rate and oxygen saturation were monitored using pulse-oximetry. 
Data obtained from a normal subject showed that, as expected, heart rate was higher in hypoxia (60 bpm) than during normoxia (51) or hyperoxia (50) and the arterial oxygen saturation (SpO2) was reduced during hypoxia to 86%. Mean ventilation was 8.31 L/min BTPS during hypoxia, 7.04 L/min during normoxia, and 6.64 L/min during hyperoxia. Tidal volume was 0.76 L during hypoxia, 0.69 L during normoxia, and 0.67 L during hyperoxia. 
Representative quantified ASL data showed that the mean density normalized perfusion was 8.86 ml/min/g during hypoxia, 8.26 ml/min/g during normoxia and 8.46 ml/min/g during hyperoxia, respectively. In this subject, the relative dispersion4, an index of global heterogeneity, was increased in hypoxia (1.07 during hypoxia, 0.85 during normoxia, and 0.87 during hyperoxia) while the fractal dimension (Ds), another index of heterogeneity reflecting vascular branching structure, was unchanged (1.24 during hypoxia, 1.26 during normoxia, and 1.26 during hyperoxia). 
Overview. This protocol will demonstrate the acquisition of data to measure the distribution of pulmonary perfusion noninvasively under conditions of normoxia, hypoxia, and hyperoxia using a magnetic resonance imaging technique known as arterial spin labeling (ASL). 
Rationale: Measurement of pulmonary blood flow and lung proton density using MR technique offers high spatial resolution images which can be quantified and the ability to perform repeated measurements under several different physiological conditions. In human studies, PET, SPECT, and CT are commonly used as the alternative techniques. However, these techniques involve exposure to ionizing radiation, and thus are not suitable for repeated measurements in human subjects.

A MR imaging method to study the distribution of pulmonary blood flow under a variety of physiological conditions, in this case exposure to three different inspired oxygen concentrations: hypoxia, normoxia, and hyperoxia, is described. This technique utilizes human pulmonary physiology research techniques in an MR scanning environment.

This demonstrates a MR imaging method to measure the spatial distribution of pulmonary blood flow in healthy subjects 
during normoxia (inspired O2, ...

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

Neuroscience

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when studying the brain, as the functional role that a brain area fulfills may be largely defined by its connections to other brain areas. In this report, we describe a novel, non-invasive approach for relating brain structure and function using magnetic resonance imaging (MRI). This approach, a combination of structural imaging of long-range fiber connections and functional imaging data, is illustrated in two distinct cognitive domains, visual attention and face perception. Structural imaging is performed with diffusion-weighted imaging (DWI) and fiber tractography, which track the diffusion of water molecules along white-matter fiber tracts in the brain (Figure 1). By visualizing these fiber tracts, we are able to investigate the long-range connective architecture of the brain. The results compare favorably with one of the most widely-used techniques in DWI, diffusion tensor imaging (DTI). DTI is unable to resolve complex configurations of fiber tracts, limiting its utility for constructing detailed, anatomically-informed models of brain function. In contrast, our analyses reproduce known neuroanatomy with precision and accuracy. This advantage is partly due to data acquisition procedures: while many DTI protocols measure diffusion in a small number of directions (e.g., 6 or 12), we employ a diffusion spectrum imaging (DSI)1, 2 protocol which assesses diffusion in 257 directions and at a range of magnetic gradient strengths. Moreover, DSI data allow us to use more sophisticated methods for reconstructing acquired data. In two experiments (visual attention and face perception), tractography reveals that co-active areas of the human brain are anatomically connected, supporting extant hypotheses that they form functional networks. DWI allows us to create a "circuit diagram" and reproduce it on an individual-subject basis, for the purpose of monitoring task-relevant brain activity in networks of interest.

We describe a novel approach for simultaneous analysis of brain function and structure using magnetic resonance imaging (MRI). We assess brain structure with high-resolution diffusion-weighted imaging and white-matter fiber tractography. Unlike standard structural MRI, these techniques allow us to directly relate anatomical connectivity to functional properties of brain networks.

The study of complex computational systems is facilitated by network maps, such as circuit diagrams. Such mapping is particularly informative when ...

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

MRI Mapping of Cerebrovascular Reactivity via Gas Inhalation Challenges

The brain is a spatially heterogeneous and temporally dynamic organ, with different regions requiring different amount of blood supply at different time. Therefore, the ability of the blood vessels to dilate or constrict, known as Cerebral-Vascular-Reactivity (CVR), represents an important domain of vascular function. An imaging marker representing this dynamic property will provide new information of cerebral vessels under normal and diseased conditions such as stroke, dementia, atherosclerosis, small vessel diseases, brain tumor, traumatic brain injury, and multiple sclerosis. In order to perform this type of measurement in humans, it is necessary to deliver a vasoactive stimulus such as CO2 and/or O2 gas mixture while quantitative brain magnetic resonance images (MRI) are being collected. In this work, we presented a MR compatible gas-delivery system and the associated protocol that allow the delivery of special gas mixtures (e.g., O2, CO2, N2, and their combinations) while the subject is lying inside the MRI scanner. This system is relatively simple, economical, and easy to use, and the experimental protocol allows accurate mapping of CVR in both healthy volunteers and patients with neurological disorders. This approach has the potential to be used in broad clinical applications and in better understanding of brain vascular pathophysiology. In the video, we demonstrate how to set up the system inside an MRI suite and how to perform a complete experiment on a human participant.

Non-invasive imaging of the brain vasculature&#8217;s ability to dilate or constrict may allow a better understanding of cerebrovascular pathophysiology in various neurological diseases. The present report describes a reproducible and patient-comfortable protocol to perform vascular reactivity imaging in humans using magnetic resonance imaging (MRI).

The brain is a spatially heterogeneous and temporally dynamic organ, with different regions requiring different amount of blood supply at different time. ...

Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia

Dynamic changes in tissue water diffusion and glucose metabolism occur during and after hypoxia in cerebral hypoxia-ischemia reflecting a bioenergetics disturbance in affected cells. Diffusion weighted magnetic resonance imaging (MRI) identifies regions that are damaged, potentially irreversibly, by hypoxia-ischemia. Alterations in glucose utilization in the affected tissue may be detectable by positron emission tomography (PET) imaging of 2-deoxy-2-(18F)fluoro-&#7429;-glucose ([18F]FDG) uptake. Due to the rapid and variable nature of injury in this animal model, acquisition of both modes of data must be performed simultaneously in order to meaningfully correlate PET and MRI data. In addition, inter-animal variability in the hypoxic-ischemic injury due to vascular differences limits the ability to analyze multi-modal data and observe changes to a group-wise approach if data is not acquired simultaneously in individual subjects. The method presented here allows one to acquire both diffusion-weighted MRI and [18F]FDG uptake data in the same animal before, during, and after the hypoxic challenge in order to interrogate immediate physiological changes.

The method presented here uses simultaneous positron emission tomography and magnetic resonance imaging. In the cerebral hypoxia-ischemia model, dynamic changes in diffusion and glucose metabolism occur during and after injury. The evolving and irreproducible damage in this model necessitates simultaneous acquisition if meaningful multi-modal imaging data are to be acquired.

Dynamic changes in tissue water diffusion and glucose metabolism occur during and after hypoxia in cerebral hypoxia-ischemia reflecting a bioenergetics ...

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from &plusmn; 25 min to &plusmn; 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (&plusmn; 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (&plusmn; 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke.

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat ...

Quantification of Cerebral Perfusion using Laser Speckle Imaging and Infarct Volume using MRI in a Pre-clinical Model of Posterior Circulation Stroke

Background: Basilar artery occlusion (BAO) is a subset of posterior circulation stroke that carries a mortality as high as 90%.&#160; The current clinical standard to diagnose ischemic stroke include computerized tomography (CT), CT angiography and perfusion and magnetic resonance imaging (MRI). Large animal pre-clinical models to accurately reflect the clinical disease as well as methods to assess stroke burden and evaluate treatments are lacking.
Methods: We describe a canine model of large vessel occlusion (LVO) stroke in the posterior circulation, and developed a laser speckle imaging (LSI) protocol to monitor perfusion changes in real time.&#160; We then utilized high b-value DWI (b=1800s/mm2) MRI to increase detection sensitivity. We also evaluated the ability of magnetic resonance angiography (MRA) to assess arterial occlusion and correlate with DSA. Finally, we verified infarct size from apparent diffusion coefficient (ADC) mapping with histology.&#160;
Results:&#160; Administration of thromboembolism occluded the basilar artery as tracked by DSA (n=7).&#160; &#160;LSI correlated with DSA, demonstrating a reduction in perfusion after stroke onset that persisted throughout the experiment, allowing us to monitor perfusion in real time.&#160; DWI with an optimized b-value for dogs illustrated the stroke volume and allowed us to derive ADC and magnetic resonance angiography (MRA) images. The MRA performed at the end of the experiment correlated with DSA performed after occlusion. Finally, stroke burden on MRI correlated with histology.
Conclusions: Our studies demonstrate real time perfusion imaging using LSI of a canine thromboembolic LVO model of posterior circulation stroke, which utilizes multimodal imaging important in the diagnosis and treatment of ischemic stroke.

A canine model of LVO stroke was utilized to develop laser speckle imaging to monitor cerebral perfusion in real-time.&#160; Diffusion-weighted MRI was optimized to image infarct volume utilizing a high b-value, enabling ADC and MRA, correlated with DSA at the time of stroke.&#160; Finally, ADC reconstructions correlated with histological findings.

Background: Basilar artery occlusion (BAO) is a subset of posterior circulation stroke that carries a mortality as high as 90%.&#160; The current clinical ...

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Magnetic resonance imaging (MRI) is an important tool for the clinical assessment of cardiovascular morphology and heart function. It is also the recognized standard-of-care for blood flow quantification based on phase contrast MRI. While such measurement of blood flow has been possible in adults for decades, methods to extend this capability to fetal blood flow have only recently been developed.
Fetal blood flow quantification in major vessels is important for monitoring fetal pathologies such as congenital heart disease (CHD) and fetal growth restriction (FGR). CHD causes alterations in the cardiac structure and vasculature that change the course of blood in the fetus. In FGR, the path of blood flow is altered through the dilation of shunts such that the oxygenated blood supply to the brain is increased. Blood flow quantification enables assessment of the severity of the fetal pathology, which in turn allows for suitable&#160;in utero&#160;patient management and planning for postnatal care.
The primary challenges of applying phase contrast MRI to the human fetus include small blood vessel size, high fetal heart rate, potential MRI data corruption due to maternal respiration, unpredictable fetal movements, and lack of conventional cardiac gating methods to synchronize data acquisition. Here, we describe recent technical developments from our lab that have enabled the quantification of fetal blood flow using phase contrast MRI, including advances in accelerated imaging, motion compensation, and cardiac gating.

Here we present a protocol for measuring fetal blood flow rapidly with MRI and retrospectively performing motion correction and cardiac gating.

Magnetic resonance imaging (MRI) is an important tool for the clinical assessment of cardiovascular morphology and heart function. It is also the ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.

Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling

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