Figure 1. OEF maps from PET and QQ in axial, sagittal, and coronal views in a subject. Both PET and QQ show uniform OEF maps and good agreement between scans and methods.

Figure 3. OEF comparison in cortical gray matters (A-E), white matter (F), and deep gray matters (G-J) among PET and QQ average. No significant difference was found between PET and QQ (all p-values <0.01, TOST). The unit in y-axis is %. Red line, blue box, black whisker, and red cross, black circle indicates median value, interquartile range, the range extending to 1.5 of the interquartile range, outlier beyond the whisker range, and individual subject value.

Figure 1: multi-TR scheme: a) Schematic drawings of the multi-TR scheme without BGS . Data acquisition at each time point consists of presaturation (Sat.), and control or label modules (LD) followed by data acquisition (Acq.). ASL dynamics data are acquired by changing the LD and PLD in each session. b) The BGS timing optimization for the short and long time point acquisition. Four BGS pulses are inserted in LD and PLD. Label or control module is changed with interleaved manner (L/C to C/L) with BGS pulse insertion.

Figure 5: Representative Volunteer case of fetal type: a) 4D MRA (separate acquisition) and b) 4D perfusion results with background suppression, and c) ATT and CBF map calculated from the individual timepoints in 4D perfusion. The left and right ATT difference in PCA territory is indicated by 4D-MRA (white arrows). In the ASL dynamic, the flow signal is increased earlier in the left PCA territory compare to the right PCA territory (yellow allows). The ATT map shows longer ATT in the right PCA territory (red arrow). These observations are consistent with the 4D-MRA findings.

Figure 4: Angiography of a patient with moyamoya disease. Non-invasive time resolved angiography by 4D-sPack of R-ICA (red) and L-ICA (cyan) are shown in axial view with maximum intensity projections (A; top row). The non-invasive angiogram at t=2000 ms is compared to conventional DSA in coronal and sagittal view for R-ICA and L-ICA, respectively (B; red & cyan boxes). Note 4D-sPack can even depict small distal collateral vessels originating from R-ICA in agreement with DSA (B; arrows) as well as delayed perfusion by L-ICA (A; circles) in agreement with prolonged time-to-peak (TTP; C).

Figure 2: Angiography and perfusion in a patient with left-ICA dissection, left-M1 occlusion and hypoplastic right-VA. Contrast-enhanced MR neck-angiography (A) in an axial cut shows the cerebral blood supply by R-ICA (red) and L-VA only (yellow; B). CBF is only slightly decreased in the left posterior hemisphere (circles, C), but with substantially prolonged perfusion delay (D) in this L-VA territory, as depicted by ss-ASL (E). The right-ICA widely supplies the contralateral hemisphere (E) with takeover of the left anterior and media territories as demonstrated by 4D-sPack (F).

Figure 2. OEF of the infarcted area defined on DWI and contralateral mirror area and their quotient (rOEF) in four ischemic stroke phases. (a) OEF significantly reduced compared with the contralateral mirror area in all four stroke phases (two-tailed paired-t test, p < 0.05 for all). Besides, OEF showed a trend of decrease from acute to chronic phase and was statistically significant (ANOVA, p = 0.022). (b) rOEF showed the same decreasing tendency with OEF (ANOVA, p = 0.024). The center line is the mean and the other two lines are standard deviation; *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 3. OEF and rOEF of initial diffusion lesion, mismatched area and final infarct among different phases in the 8 patients whose final infarct volume was smaller than initial diffusion lesion according to longitudinal MR scans. OEF (a) and rOEF (b) of the final infarct showed a trend of decrease with time, while those of the initial diffusion lesion and the mismatched area increased. Note that only increasing trend of OEF of the mismatched area reached statistical significance (repeated measures ANOVA, p = 0.022).

Fig.1 (A,D,G) DWI, (B,E,H) FLAIR, and (C,F,I) FLAIR+APTw images of an ischemic stroke patient (female; 56 years old; left limb inactivity for 15 days). DWI images demonstrated that the patient developed an acute infarction of right frontal parietal lobe cerebral infarction. The APTw intensities in regions of the ipsilateral of the ischemic lesion (black arrow) were higher than the contralateral side in both levels of posterior limb of internal capsule and cerebral peduncle. DWI and FLAIR images show no difference between two sides of the two levels.

Table1

Figure 1. Multimodal images from representative patients at 2.25 to 92.73 hours after ischemic stroke. The color bar for MRSI shows NAA or lactate level in institutional units. The NAA signal reduction was visible within the lesion area and decreased with time.

Figure 3. (a) Significant reduction of NAA was observed for acute stage patients from within to over 9h window (p < 0.05), but lactate showed no significant difference. (b) A significant negative correlation between NAA and time after onset was found, but not for lactate.

Fig 1 A 56-year-old female with a subacute infarction 78 hours after the onset. a: DWI shows the hyperintense infarct area near the right lateral ventricle. b: The infarct area was low perfusion on the CBF image with 1.5 PLD. c: The infarct area was slightly hypointense on the APTw image. d: DWI-CBF fusion map shows the IC and IP. The ROI of the IC (white line) and the IP (orange line) is indicated. e: DWI-APTw fusion map. The ROI was copied from the DWI-CBF fusion map.

Fig 3 Association between APTw_max-min signal in and ∆NIHSS score in IC and IP. (a) The APTw_max-min within IC was inversely correlated with the ∆NIHSS score (R2 = 0.184, P = 0.041). (b) The APTw_max-min within IP showed negative correlation with ∆NIHSS scores (R2 = 0.255, P = 0.014).

Figure 3. Scatter plots with significant correlations (|r|>0.65, p<0.05) between change scores of language performance and FCs of brain regions.

Figure 2. Hierarchical clustering based on the profiles of overlapping percentage between stroke lesion and the selected cortex. The right-hand side of each row shows patient label (by a given number) and the corresponding lesion size. The columns are the selected brain regions related to the language network. The color represents the values of z-scores (red for positive, and green for negative value). The patients are clustered into four aphasia subtypes based on lesion patterns, including Broca and mixed type, unspecified, putamen, and Wernicke aphasia.

Figure 2. Multimodal images from representative patients at acute and subacute stages after ischemic stroke. All the images were registered to the structural T1-weighted images. The color bar for ASL-PWI shows the cerebral blood flow in ml/100g/min. The color bar for MRSI shows NAA or lactate level in institutional units.

Figure 3. Group comparison of mean relative NAA and lactate concentrations for different regions of interest in acute and subacute stroke patients, respectively (Error bars, 95% confidence interval). *p < 0.05, **p < 0.01.

Figure 2. Oxygen extraction fraction (OEF, A), nonblood susceptibility (B) and cerebral blood flow (CBF, C) in the perfusion lesion (PL), penumbra and diffusion lesion (DL) of the mismatched group. Significant differences of OEF and nonblood susceptibility were found between DL and PL and between DL and penumbra. There were no significant differences of CBF among PL, DL and penumbra.

Figure 1. Representative images of three patients in the corresponding groups classified by DEFUSE 3 criteria. (A) A 64-year-old female with the volume of diffusion lesion (shown in green on ADC maps) matched with that of perfusion lesion (shown in red on CBF maps). (B) A 50-year-old male whose perfusion lesion was rather larger than the diffusion lesion. (C) A 33-year-old female with re-perfused left hemisphere.

Figure 1. Simultaneous 3D brain neurometabolites and OEF mapping for an acute ischemic stroke patient. (A, B) 3D NAA and Lac map in triplanar views overlaid on T1-weighted images. The representative spectra were from the DWI lesion (red), infarct growth (blue) and contralateral normal (light green) regions, respectively. (C) 3D venous OEF map in triplanar views overlaid on QSM images. (D) 3D quantitative map in triplanar views. (E) Timeline of the experimental study.

Figure 4. Significant correlations between values and lactate or lactate to NAA ratio concurrently detected in infarct growth area (A) but not in infarct core (B) of acute ischemic stroke patients.

Figure 3: Representative axial and sagittal slices of 16-slices minimum intensity projections of tSWI data and segmentations. The manually segmented ground truth is shown in green, overlaid with (from left to right) the segmentation from the proposed algorithms for a multi-echo acquisition when R₂^* is available, without R₂^*, and for the Frangi method, respectively. These segmentations are shown in red so that true positives appear orange, false positives red, and false negatives green.

Figure 1: Algorithm flowchart summarizing the main steps 1-6 of the algorithm (the use of R₂^* data is omitted).

Sample BOLD signal (A, B) and representative CVR_end and CVR_max maps (C) from a patient with right hemisphere disease. % BOLD-signal changes of a right hemispheric voxel with 30-second temporal delay with respect to the straight sinus time series shown from raw (gray) and processed signals (orange) demonstrating non-monotonic post-ACZ effects. Panel B demonstrates a case with CVR_max greater than CVR_end. Panel C demonstrates challenges with CVR_end evaluation confounding essentially preserved, symmetric CVR_max augmentation in a patient remaining stable during serial follow-up.

Sample BOLD signal (A, B) and representative CVR_end and CVR_max maps (C) from a patient with right hemisphere disease. % BOLD-signal changes of a right hemispheric voxel with 30-second temporal delay with respect to the straight sinus time series shown from raw (gray) and processed signals (orange) demonstrating non-monotonic post-ACZ effects. Panel B demonstrates a case with CVR_max greater than CVR_end. Panel C demonstrates challenges with CVR_end evaluation confounding essentially preserved, symmetric CVR_max augmentation in a patient remaining stable during serial follow-up.

Figure.4 MRI images of a 63-years-old man. A: the MR angiography shows the occur of occlusion in left middle cerebral artery. B: DWI shows the hyperintense region in left basal ganglia. C～F: The modified ASPECT score of FVH shows that the FVH score was 6. G～I: APCVs score is 5.

Tables Clinical information and MR measurements for patients

Figure 2. The group average rOEF maps without (A) and with (B) PVC, and the group average fitting error maps without (C) and with (D) PVC.

Figure 3. Correlation between WM rOEF and rV_WMH with and without PVC (A, B), and correlation between watershed rOEF and rV_WMH with and without PVC (C, D).

Figure 1. Overview of a representative disconnectivity map. Disconnectivity map (green) is calculated using the lesion mask of the infarction (red), based on the tractograms of healthy subjects. The degree of overlap of the disconnectivity map and masks of deep nuclei was used to estimate the remote effect of the infarction.

Figure 3. Voxel-based morphometry analysis for the R2* value in the substantia nigra and the thalamus. The red area means a significant increase of the R2* value at 1-year follow-up compared with that at baseline. The blue area means a significant decrease at 1-year follow-up.

Fig 1. Individual CVR maps show significant CVR changes under external CO2 challenges (n=11) (1st row). Individual CVR maps to endogenous maps showed significant inter-subject variability (2nd row). Such inter-subject variability was not reduced by correction of respiratory effects using RETROICOR (3rd row). CVR maps to endogenous O2 show less inter-subject variability than CVR maps to endogenous CO2 (4th row).

Figure 2 The mean FC matrices for the aICS_L (upper-left matrix) and aICS_R (upper-right matrix) groups during the resting state. The significant differences between two groups are displayed in the lower matrix. A positive t value (yellow) represents a higher FC in the aICS_R group compared to that in the aICS_L group. Mot.: Motor; Lim.: Limbic; Vis.: Visual; Sen.: Sensory; Sub.: Subcortical; Tem.: Temporal; Cer.: Cerebellum.

Figure 3 Scatter plots for the significant correlations between functional connectivity and neuropsychological assessments during the resting state.

Voxelwise maps of the Bland-Altman analysis showing mean difference between breath-hold (BH) and cued deep breathing (CDB) cerebrovascular reactivity (CVR) across all subjects. Differences in non-optimized CVR (No-Opt) are shown on the left, lag-optimized on the right, with thresholded maps for each in the bottom row (p<0.05). Negative values represent voxels with greater BH CVR than CDB CVR, and vice versa. Significant differences appear primarily at brain edges and in white matter.

Voxelwise correlations between breath-hold (BH) and cued deep breathing (CDB) cerebrovascular reactivity (CVR) before lag optimization (No-Opt), lag-optimized CVR (Lag-Opt), and lag. A Fisher's Z transform was performed on individual subject correlations to calculate a group average. All single subject Fisher’s Z values are significantly different from 0 at an alpha-level of 0.05 (Z>0.0232).

Figure 1 A) PD-weighted scan for WMH segmentation; B) T2-weighted scan for white matter hyperintensity segmentation; C) white matter hyperintensity mask; D) myelin water fraction map; E) normal appearing white matter myelin water fraction mask; F) normal appearing white matter myelin water fraction map

Table 2 Multiple Linear Regression Results

Independent Variables in Step 1 = age and education; Independent Variables in Step 2 = age, education, and white matter hyperintensity volume; Independent Variables in Step 3 = age, education, white matter hyperintensity volume, and normal appearing white matter (NAWM) myelin water fraction (MWF)

*Significant at p ≤ 0.05

Figure 1. Group comparison of cross-correlation of ΔBOLD with P_ETCO₂ between HD subjects and healthy controls after adjusting for age, corrected at pfdr<0.05. Cold colors represent weaker cross-correlation in HD subjects when compared with controls.

Figure 2. Map of delayed BOLD responses to increased P_ETCO₂ in a representative HD subject. The magnitude of time delay in BOLD responses relative to P_ETCO₂ increases from cold colors to warm colors.

Figure 1. Registration pipeline of VALiDATe29 squirrel monkey atlas labels (template space) to structural images before MION (subject space). T₂* weighted image before MION administration were first skull stripped and down sampled to template size. Then the template image was transformed into subject space by performing affine linear automatic image registration (AIR) followed by non-linear LDDMM registration. Inverse transformation matrices were subsequently applied to the template labels. Labels are transformed to subject space and checked visually using MRICroN.

Figure 2. (a) Characteristic rCBV map of one subject. The 13^th slice is shown. (b) structural T₂* image before MION overlayed with transformed labels in subject space for the same slice. Structural image is in gray scale and labels are indicated in different color. (c) label names, the label numbers are consistent with numbers in b.

Figure 4. Inter-session repeatability of OEF, CBF and CMRO₂ in WB, GM, WM and WS regions. The red horizontal line, dotted black horizontal lines, and solid black horizontal lines represent the mean, ± SD, and ± 1.96xSD of inter-session measurement differences, respectively.

Figure 2. Gray matter (A), white matter (B) and watershed region mask (C), OEF map (D), CBF maps (E) and corresponding CMRO₂ maps (F) from a representative subject.

Figure 3: Whole brain segmentation at baseline clockwise from top left, GM, WM, lesion, and CSF. Lesion segmentation processed in SPM software; others processed by FSL-FAST. Two-dimensional transverse view of a three-dimensional binary mask. Lesion segmentation algorithm tends to categorise large CSF volumes as lesion, visible at first and second ventricles.

Figure 1i: inSPECT GUI displaying list of selectable metabolites (B), the configuration panel (A) Cramer-Rao lower bounds SD% threshold. Output panel (C) contains the button to execute processing of data, the lower edge (D) is the status bar, indicating a single CSV file has been opened.

Figure 1ii: inSPECT GUI with voxel selection tab active (B) with voxels checked for filtering. (A) is selected file information and checkboxes for inputting the concentration filter method. Panel (C) is the Save & Execute button, panel (D) is current status, a Baseline Segmentation file has been loaded.

Figure 2. Comparison of CVR magnitudes (A) and delays (B) obtained with SIDL and GLM. The outliers are labelled as outlier 1 and 2.

Figure 3. EtCO2, mean BOLD in deep grey matter and kernel of a subject with comparable estimates to GLM and of the two outliers.

Connectivity analysis: ROI to ROI, Low circulation time > Healthy control

Connectivity analysis: ICA, Low circulation time > Healthy control

Figure 1: Longitudinal changes in the National Institutes of Health Stroke Scale (NIHSS) scores in ischemic stroke patients subjected to yoga intervention.

Figure 2: Representative frequency distribution of the BOLD activity from a left hemisphere ischemic stroke patient. The purple peak corresponds the zero time point (pre-intervention), the red to 3 months post-intervention and green to 6 months post-intervention.

Functional connectogram (A-C) and group averaged functional connectivity (FC) matrix (D-F) of healthy controls (HC) and two clusters of subjects with stroke. Asterisk (*) indicates significant differences between the patient clusters (Two-sample t-test, p<0.05, FDR correction). Comment sign (#) indicates significant difference between either cluster and HCs (Two-sample t-test, p<0.05, FDR correction).

Figure 1. Representative signal decay curve (left) and Monte Carlo simulation system (right).

Figure 3. Typical VSI map for a SCD patient (left) and mean VSI for all the subjects (right).

Fig. 1. (A) Typical T2-weighted (T2W) and diffusion-weighted images (DWI) for wild-type (WT) and AQP4 knockout (AQP4-KO) mice. The left side of the brain had a middle cerebral artery occlusion. (B) and (C) Normalized b-value dependent signal decay for the ipsilateral (B) and contralateral (C) sides at different diffusion times (Δ = 40, 70 and 100 ms). Solid lines indicate WT mice (closed circles with SD bars) and broken lines indicate AQP4-KO mice (white circles with SD bars).

Fig. 3. (A) Typical apparent diffusion coefficient (ADC) maps of wild-type (WT, top) and AQP4 knockout (AQP4-KO, bottom) mice focused on the ipsilateral region at Δ =70 ms. The ADC in the ischemic region has a range of 0 – 5×10^-4 mm²/s. (B) The bar graphs show the mean ADC of WT (white) and AQP4-KO (black) mice for each b-value range and Δ. The error bars correspond to the SD over animals. The results of Two-way ANOVA are shown in the table at the base of the figure.

Figure 1: Shown on the left are quantitative DSC CBF (A,B,C) and quantitative IVIM perfusion (D,E,F) images at normocapnia, hypercapnia, and post-occlusion. On the right is a standard ADC map (G) compared to IVIM diffusion coefficient image (H) post-occlusion.

Figure 2: Correlations of quantitative IVIM perfusion and DSC qCBF to neutron-capture microspheres at normocapnia, hypercapnia, and post-occlusion hemispherically. The correlation suggests comparable agreement between IVIM and neutron-capture microspheres as standard quantitative DSC CBF.

Figure 3: Example image showing large white matter hyperintensities (blue) overlaid on the subject’s FLAIR image. The total white matter hyperintensities volume for this subject was 13406 voxels.

Figure 4: Example image showing the left carotid artery from the same subject as Figure 3. The colour map shows the arterial radius (average across the vessel = 2.9470). The tortuosity for this vessel was 0.8773.

Figure 4. Subcortical associations with Node Degree. A. Boxplots of HIV group differences for each subcortical structure. B. Scatterplots showing Node Degree as a function of Motor Z-score for each region corresponding from A Spearman’s rho is displayed for each cohort. Note that only regions with significant cohort differences are shown. {* p < 0.05; ** p < 0.01}

Figure 3. Global graph theory metric group differences between cohorts. A. by HIV-status using all subjects. B. by HIV-status stratified by CSVD-status. CSVD+ is defined by a subject receiving any Fazekas score greater than zero.

Figure 5. (A) Within-Subject Coefficients of Variance for both single (left) and multi-PLD (right) PCASL pre and post-ACZ were much closer to 0% than 100%, which are indicative of repeatability. (B) Intraclass Correlation Coefficients for both single (left) and multi-PLD (right) PCASL pre and post-ACZ were much closer to 1 than 0, which are indicative of repeatability.

Figure 4. (A) Mean labeling efficiency for single-PLD ASL methods increased for significantly (p=0.03) after acetazolamide injection but did not change significantly between the two sessions. (B) Mean labeling efficiency for multi-PLD ASL method increased significantly (p<0.01) after acetazolamide injection but did not change significantly between the two sessions.

Figure 1: Probability density functions (PDFs) of Orientation Dispersion Index (ODI) values from the NODDI model ⁶ for all stroke patients in the PLIC region of the brain. PDFs estimated for both, ipsilesional (I) and contralesional (C) sides of the brain. The corresponding mean ODI values for each PDF are also marked on the x-axis. The baseline Fugl-Meyer Upper Extremity score (FMUE-TP1) is also indicated on top of each subplot.

Table 1: Linear regression statistics for various models for PLIC region. Followup FMUE-TP2: response variable. Predictor variables are chosen from baseline as follows: A) Upper-left block (blue highlight): distribution-valued ODI and RDI observations (ΔDist). B) Upper-mid block: mean-based ODI and RDI observations (ΔMean). C) Upper-last column: Only FMUE-TP1. D) Lower block (green highlight): combination of FMUE-TP1 with either ODI or RDI diffusion parameters.

Figure 1. Parametrical maps of the representative case. A 19 year-old female who presented with transcient ischemic attacks of the right side received revascularization surgery (indirect bypass surgery) to the left hemisphere. Preoperative isotropic volume fraction (V_iso) of NODDI and freewater fraction (FWF) of regularized bi-tensor model suggests increased water component in the left hemisphere (arrows) compared to the contralateral hemisphere. The increased water seems to decrease after the surgery (arrows).

Parametrical values of normal controls (Normal), preoperative (Pre) and postoperative (Post) patients in six regions. Compared to normal controls, preoperative regions showed higher V_iso and FWF in most regions, and significantly decreased after the surgery. In the white matter (WM) of the ACA areas, OD is higher, and FAt and FA are lower compared than normal controls. FA in the WM of MCA showed significant increase after the surgery. GM, gray matter. *P<.05 (unpaired T test) and †P<.05 (paired T test) after Bonferroni correction for six parameters.

Figure 1 A. 48-year-old male with an occlusion in the right MCA. B. 49-year-old male with severe stenosis of the left ICA C7 and L-MCA M1. (a) High APTw value area was visible (black arrow). (b) Perfusion was lower in the ipsilateral side than the contralateral side (white arrow).

Figure 2 The ROC curve showed the sensitivity and specificity values of APT and 3D-ASL in the diagnosis of symptomatic intracranial artery stenosis. The diagnosis of combined ASL and APT(AUC=0.997, P＜0.001) shows the most robust diagnostic performance in SIAS.

Figure.1 Visual assessment of patency of STA-MCA bypass using 4D Flow MRI. Time-of flight magnetic resonance angiography (MRA) (A, B) and 4D Flow MRI (C, D) were performed preoperatively (A and C) and 7 days after bypass surgery (B and D). The yellow arrows indicated the locations of STA used to STA-MCA bypass.

Table.2 Paired samples t- test of BFV Before and after bypass surgery.

Figure 3. Relationship between the FVH score and MRI parameters in patients with cerebral infarction. The FVH score was positively correlated with rrCBF2.5, and negatively correlated with ADC value (red asterisk). No significant correlation was observed between FVH score and APTw values.

Figure 2. Measurement of APTw values at the cerebral infarction area of an 65 patient. A: brain MR angiography showed occlusion in left middle cerebral artery. B: DWI showed a hyperintensity region in left basal ganglia. C: The DWI-APTw fusion image of showed that the signal of infarct area was lower than that of its contralateral region. D-F: a 3D ROI was delineated layer by layer for measurement for APTw values.

Figure 1. Representative MRI of thrombotic stroke patients who have undergone thrombectomy: despite the lack of Gd-DTPA extravasation, rWEI map identifies the areas with compromised BBB function.

Figure 2. Ratios of values from ROI’s between ipsi- and contra-hemisphere. Both WEI and rWEI are stable in non-infarct areas across hemispheres while both are elevated in infarct regions. The difference between WEI and rWEI ratios in infarct regions may be due to a nonlinear relationship between CBV and rCBV.

Whole-brain cerebral blood flow in rats at +4 and +8 weeks post treatment, for the four groups studied.

Urinary albumin levels, measured by ELISA, as a surrogate of kidney injury. Higher values suggest more kidney injury.

Figure 1: Weighted degree in the parietal region of the brain over 30 days post ischemic event . No change is seen in the contralateral side 1 day post stroke; however, effects of edema can be seen on the ipsilateral side. Weighted degree levels can be seen returning to control levels at day 9 until spiking at day 30. Contralateral (right) changes at days 3 & 5 can be attributed to swelling on the ipsilateral (left) side that compresses the contralateral hemisphere.

Figure 3: Changes in betweenness centrality in the piriform region indicate how many times a node acts as a bridge. Immediately following stroke, the ipsilateral side shows an increase in the number of nodes acting as a bridge while the contralateral side displays a slight delay. The contralateral also recovers sooner than the ipsilateral, indicating a prolonged/delayed restructuring process on that side.

Figure 2: (A) Anterior CC contained tracks from the most anterior and inferior regions of prefrontal cortex. (B) Mid-Anterior CC contained tracks from caudal anterior cingulate and superior & middle frontal cortical regions. (C) Central and Mid-Posterior CC contained tracks from cortical regions proximal to the central sulcus (precentral, paracentral, postcentral) and lateral sulcus (insula, transverse temporal, superior temporal, supramarginal). (D) Posterior CC contained tracks from posterior parietal, inferior temporal and occipital lobes.

Figure 3: (A) Phantom mid-sagittal CC showing the ideal intersection of the transcallosal tracks (green) between precentral gyri. (B) The intersection of tracks (red) between precentral gyri for Patient A shows a region where no tracks were detected (yellow arrow) but were found in (A). (C) The intersection of tracks (blue) between precentral gyri for Patient B had a similar extent along the CC as the phantom.

Perfusion map of K_trans [min-1] (top row) and v_p [arb.] (second row), T₁ weighted images (third row), and diffusion weighted images (bottom row) of a single animal from before stroke, 24 hours post stroke, and 6 days post stroke.

The progressive changes of the brain and body temperatures in adult monkeys administrated with saline or the novel neurotensin analog (ABS-201)

The Temperature (°C) Changes in the Body and Brain of Each Monkey after administrated with saline or the novel neurotensin analog (ABS-201)

Figure 1. A 61-year-old patient with bilateral MMA. (A) Occlusion of bilateral ICA, ACA, and MCA was observed on 3D time of flight MRA images. (B) He had a punctate acute cerebral infarction in the left frontal lobe. The perfusion of the left frontal lobe decreased, and the arterial transit artifact was grade 2 on CBF images when PLD were 1.5 s and 2.5 s (C and D). (E and F) Three-dimensional images of bilateral cerebral hemispheres. The gray value of the left cerebral hemisphere was less than that of the contralateral on the gray distribution histogram of bilateral cerebral hemispheres.

Table 2. Correlation between the degree of intracranial artery, cerebral perfusion, and occurrence of cerebrovascular events in patients with MMA.

DKI-IVIM analysis on one individual slice of diffusion-weighted images (b = 1000 s/mm ).

(a) Signal intensity plotted as function of b-value for the two ROIs located in the ischemic lesion and normal contralateral region. The slope related to fD* between b = 0s/mm and b = 300 s/mm is decreased for the ischemic region.

(b) Free-hand region of interest (ROI) delineation in the ischemic lesion and the corresponding contralateral ROI on normal tissues.

Intravoxel incoherent motion (IVIM) parameters (D, f and fD*) and conventional apparent diffusion coefficient (ADC) and the cerebral blood flow (CBF) mesured through ASL in the lesion and the controlateral normal region averaged over all the stroke patients. Comparison with motion correction data processing for the DKI-IVIM analysis.

(a) Increased Ds values at left median cingulate and paracingulate gyrus (DCG.L), posterior cingulate gyrus (PCG.L) and left precuneus gyrus (PCUN.L) (cluster size = 156), left middle frontal gyrus (MFG.L), orbital middle frontal gyrus (ORBmid.L) and superior frontal gyrus (SFG.L) (cluster size = 165) (p ＜0.05, FWE correction). (b) Decreased Ds values at right middle temporal gyrus (MTG.R), inferior temporal gyrus (ITG.R)(cluster size = 116)(p ＜0.05, FWE correction). (c) Decreased f values at MTG.R,ITG.R(cluster size = 85) (p ＜0.05, FWE correction).

Canonical time courses resulting from clustering algorithm (left). The corresponding maps are shown on the right. These were calculated by linear regression of the canonical maps from the raw data.

Fractional maps obtained from two subjects. Subject 2 showed significantly enlarged ventricles and grey matter atrophy.