Dynamic Susceptibility Contrast MRI Research Articles

SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.

To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods. The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC-MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data. The proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods. These results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground-truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning.

Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models.

Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl4)-induced CLD models. Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns. The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated. Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

Microvascular Perfusion Imaging in Alzheimer's Disease.

Alzheimer's disease (AD) is the leading cause of dementia worldwide and significantly impacts the essential functions of daily life and social activities. Research on AD has found that its pathogenesis is related to the extracellular accumulation of amyloid-beta (Aβ) plaques and intracellular neurofibrillary tangles in the cortical and limbic areas of the human brain, as well as cerebrovascular factors. The detection of Aβ or tau can be performed using various probes and methodologies. However, these modalities are expensive to implement and often require invasive procedures, limiting accessibility on a large scale. While magnetic resonance imaging (MRI) and computed tomography (CT) are generally used for morphological and structural brain imaging, they show wide variability in their accuracy for the clinical diagnosis of AD. Several novel imaging modalities have emerged as alternatives that can accurately and vividly display the changes in blood flow and metabolism in each brain area and enable physicians and researchers to gain insights into the generation and progression of the cerebro-microvascular pathologies of AD. In this review, we summarize the current knowledge on microvascular perfusion imaging modalities and their application in AD, including MRI (dynamic susceptibility contrast-MRI, arterial spin labeling-MRI), CT (cerebral CT perfusion imaging), emission computed tomography (positron emission tomography (PET), single-photon emission computed tomography (SPECT)), transcranial doppler ultrasonography (TCD), and retinal microvascular imaging (optical coherence tomography imaging, computer-assisted methods for evaluating retinal vasculature).

Non-invasive perfusion MR imaging of the human brain via breath-holding

Dynamic susceptibility contrast (DSC) MRI plays a pivotal role in the accurate diagnosis and prognosis of several neurovascular diseases, but is limited by its reliance on gadolinium, an intravascularly injected chelated metal. Here, we determined the feasibility of measuring perfusion using a DSC analysis of breath-hold-induced gradient-echo-MRI signal changes. We acquired data at both 3 T and 7 T from ten healthy participants who engaged in eight consecutive breath-holds. By pairing a novel arterial input function strategy with a standard DSC MRI analysis, we measured the cerebral blood volume, flow, and transit delay, and found values to agree with those documented in the literature using gadolinium. We also observed voxel-wise agreement between breath-hold and arterial spin labeling measures of cerebral blood flow. Breath-holding resulted in significantly higher contrast-to-noise (6.2 at 3 T vs. 8.5 at 7 T) and gray matter-to-white matter contrast at higher field strength. Finally, using a simulation framework to assess the effect of dynamic vasodilation on perfusion estimation, we found global perfusion underestimation of 20–40%. For the first time, we have assessed the feasibility of and limitations associated with using breath-holds for perfusion estimation with DSC. We hope that the methods and results presented in this study will help pave the way toward contrast-free perfusion imaging, in both basic and clinical research.

A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection.

Perfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual-echo 3D spiral technique with a single-bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) data and overcome the limitations of current EPI-based techniques. A 3D spiral-based technique with dual-echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through-plane SENSE acceleration for improved speed and image quality, in-plane variable-density undersampling combined with a sliding-window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single-echo EPI and a dual-echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single-echo EPI collected during the second bolus injection. Volunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single-echo and dual-echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single-echo dual-dose EPI, with the additional DCE information. A 3D dual-echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single-bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC-EPI techniques for brain perfusion MRI.

Creation of a Simulated Sequence of Dynamic Susceptibility Contrast—Magnetic Resonance Imaging Brain Scans as a Tool to Verify the Quality of Methods for Diagnosing Diseases Affecting Brain Tissue Perfusion

DSC-MRI examination is one of the best methods of diagnosis for brain diseases. For this purpose, the so-called perfusion parameters are defined, of which the most used are CBF, CBV, and MTT. There are many approaches to determining these parameters, but regardless of the approach, there is a problem with the quality assessment of methods. To solve this problem, this article proposes virtual DSC-MRI brain examination, which consists of two steps. The first step is to create curves that are typical for DSC-MRI studies and characteristic of different brain regions, i.e., the gray and white matter, and blood vessels. Using perfusion descriptors, the curves are classified into three sets, which give us the model curves for each of the three regions. The curves corresponding to the perfusion of different regions of the brain in a suitable arrangement (consistent with human anatomy) form a model of the DSC-MRI examination. In the created model, one knows in advance the values of the complex perfusion parameters, as well as basic perfusion descriptors. The shown model study can be disturbed in a controlled manner—not only by adding noise, but also by determining the location of disturbances that are characteristic of specific brain diseases.

Contrast-free dynamic susceptibility contrast using sinusoidal and bolus oxygenation challenges.

Deoxygenation-based dynamic susceptibility contrast (dDSC) MRI uses respiratory challenges as a source of endogenous contrast as an alternative to gadolinium injection. These gas challenges induce T2*-weighted MRI signal losses, after which tracer kinetics modeling was applied to calculate cerebral perfusion. This work compares three gas challenges, desaturation (transient hypoxia), resaturation (transient normoxia), and SineO2 (sinusoidal modulation of end-tidal oxygen pressures) in a cohort of 10 healthy volunteers (age 37 ± 11 years; 60% female). Perfusion estimates consisted of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). Calculations were computed using a traditional tracer kinetics model in the time domain for desaturation and resaturation and in the frequency domain for SineO2. High correlations and limits of agreement were observed among the three deoxygenation-based paradigms for CBV, although MTT and CBF estimates varied with the hypoxic stimulus. Cross-modality correlation with gadolinium DSC was lower, particularly for MTT, but on a par with agreement between the other perfusion references. Overall, this work demonstrated the feasibility and reliability of oxygen respiratory challenges to measure brain perfusion. Additional work is needed to assess the utility of dDSC in the diagnostic evaluation of various pathologies such as ischemic strokes, brain tumors, and neurodegenerative diseases.

Probing the glioma microvasculature: a case series of the comparison between perfusion MRI and intraoperative high-frame-rate ultrafast Doppler ultrasound

BackgroundWe aimed to describe the microvascular features of three types of adult-type diffuse glioma by comparing dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) with intraoperative high-frame-rate ultrafast Doppler ultrasound.MethodsCase series of seven patients with primary brain tumours underwent both DSC perfusion MRI and intra-operative high-frame-rate ultrafast Doppler ultrasound. From the ultrasound images, three-dimensional vessel segmentation was obtained of the tumour vascular bed. Relative cerebral blood volume (rCBV) maps were generated with leakage correction and normalised to the contralateral normal-appearing white matter. From tumour histograms, median, mean, and maximum rCBV ratios were extracted.ResultsLow-grade gliomas (LGGs) showed lower perfusion than high-grade gliomas (HGGs), as expected. Within the LGG subgroup, oligodendroglioma showed higher perfusion than astrocytoma. In HGG, the median rCBV ratio for glioblastoma was 3.1 while astrocytoma grade 4 showed low perfusion with a median rCBV of 1.2. On the high-frame-rate ultrafast Doppler ultrasound images, all tumours showed a range of rich and organised vascular networks with visually apparent abnormal vessels, even in LGG.ConclusionsThis unique case series revealed in vivo insights about the microvascular architecture in both LGGs and HGGs. Ultrafast Doppler ultrasound revealed rich vascularisation, also in tumours with low perfusion at DSC MRI. These findings warrant further investigations using advanced MRI postprocessing, in particular for characterising adult-type diffuse glioma.Relevance statementOur findings challenge the current assumption behind the estimation of relative cerebral blood volume that the distribution of blood vessels in a voxel is random.Key points• Ultrafast Doppler ultrasound revealed rich vascularity irrespective of perfusion dynamic susceptibility contrast MRI state.• Rich and organised vascularisation was also observed even in low-grade glioma.• These findings challenge the assumptions for cerebral blood volume estimation with MRI.Graphical

Cerebral Microvascular Perfusion Assessed in Elderly Adults by Spin-Echo Dynamic Susceptibility Contrast MRI at 7 Tesla.

Perfusion measures of the total vasculature are commonly derived with gradient-echo (GE) dynamic susceptibility contrast (DSC) MR images, which are acquired during the early passes of a contrast agent. Alternatively, spin-echo (SE) DSC can be used to achieve specific sensitivity to the capillary signal. For an improved contrast-to-noise ratio, ultra-high-field MRI makes this technique more appealing to study cerebral microvascular physiology. Therefore, this study assessed the applicability of SE-DSC MRI at 7 T. Forty-one elderly adults underwent 7 T MRI using a multi-slice SE-EPI DSC sequence. The cerebral blood volume (CBV) and cerebral blood flow (CBF) were determined in the cortical grey matter (CGM) and white matter (WM) and compared to values from the literature. The relation of CBV and CBF with age and sex was investigated. Higher CBV and CBF values were found in CGM compared to WM, whereby the CGM-to-WM ratios depended on the amount of largest vessels excluded from the analysis. CBF was negatively associated with age in the CGM, while no significant association was found with CBV. Both CBV and CBF were higher in women compared to men in both CGM and WM. The current study verifies the possibility of quantifying cerebral microvascular perfusion with SE-DSC MRI at 7 T.

Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients.

Radiomics objectively quantifies image information through numerical metrics known as features. In this study, we investigated the stability of magnetic resonance imaging (MRI)-based radiomics features in rectal cancer using both anatomical MRI and quantitative MRI (qMRI), when different methods to define the tumor volume were used. Second, we evaluated the prognostic value of stable features associated to 5-year progression-free survival (PFS) and overall survival (OS). On a 1.5T MRI scanner, 81 patients underwent diagnostic MRI, an extended diffusion-weighted sequence with calculation of the apparent diffusion coefficient (ADC) and a multiecho dynamic contrast sequence generating both dynamic contrast-enhanced and dynamic susceptibility contrast (DSC) MR, allowing quantification of Ktrans, blood flow (BF) and area under the DSC curve (AUC). Radiomic features were extracted from T2w images and from ADC, Ktrans, BF and AUC maps. Tumor volumes were defined with three methods; machine learning, deep learning and manual delineations. The interclass correlation coefficient (ICC) assessed the stability of features. Internal validation was performed on 1000 bootstrap resamples in terms of discrimination, calibration and decisional benefit. For each combination of image and volume definition, 94 features were extracted. Features from qMRI contained higher prognostic potential than features from anatomical MRI. When stable features (> 90% ICC) were compared with clinical parameters, qMRI features demonstrated the best prognostic potential. A feature extracted from the DSC MRI parameter BF was associated with both PFS (p = 0.004) and OS (p = 0.004). In summary, stable qMRI-based radiomics features was identified, in particular, a feature based on BF from DSC MRI was associated with both PFS and OS.

Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas

PurposeIsocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mutations need to be identified preoperatively for better treatment planning. The relative cerebral blood volume (rCBV) information derived from dynamic susceptibility contrast MRI (DSC-MRI) has been demonstrated to correlate with tumor vascularity, functionality, and biology, and might provide some information about the genetic alterations in gliomas before surgery. Therefore, this study aims to predict IDH and TERTp mutational subgroups in gliomas using deep learning applied to rCBV images. MethodAfter the generation of rCBV images from DSC-MRI data, classical machine learning algorithms were applied to the features obtained from the segmented tumor volumes to classify IDH and TERTp mutation subgroups. Furthermore, pre-trained convolutional neural networks (CNNs) and CNNs enhanced with attention gates were trained using rCBV images or a combination of rCBV and anatomical images to classify the mutational subgroups. ResultsThe best accuracies obtained with classical machine learning algorithms were 83 %, 68 %, and 76 % for the identification of IDH mutational, TERTp mutational, and TERTp-only subgroups, respectively. On the other hand, the best-performing CNN model achieved 88 % accuracy (86 % sensitivity, 91 % specificity) for the IDH-mutational subgroups, 70 % accuracy (73 % sensitivity and 67 % specificity) for the TERTp-mutational subgroups, and 84 % accuracy (86 % sensitivity, 81 % specificity) for the TERTp-only subgroup using attention gates. ConclusionsDSC-MRI can be utilized to noninvasively classify IDH- and TERTp-based molecular subgroups of gliomas, facilitating preoperative identification of these genetic alterations.

Aberrant vascular architecture in the hippocampus correlates with tau burden in mild cognitive impairment and Alzheimer’s disease

Cerebrovascular dysfunction is a significant contributor to Alzheimer’s disease (AD) progression. AD mouse models show altered capillary morphology, density, and diminished blood flow in areas of tau and beta-amyloid accumulation. The purpose of this study was to examine alterations in vascular structure and their contributions to perfusion deficits in the hippocampus in AD and mild cognitive impairment (MCI). Seven individuals with AD and MCI (1 AD/6 MCI), nine cognitively intact older healthy adults, and seven younger healthy adults underwent pseudo-continuous arterial spin labeling (PCASL) and gradient-echo/spin-echo (GESE) dynamic susceptibility contrast (DSC) MRI. Cerebral blood flow (CBF), cerebral blood volume, relative vessel size index (rVSI), and mean vessel density were calculated from model fitting. Lower CBF from PCASL and SE DSC MRI was observed in the hippocampus of AD/MCI group. rVSI in the hippocampus of the AD/MCI group was larger than that of the two healthy groups (FDR-P = 0.02). No difference in vessel density was detected between the groups. We also explored relationship of tau burden from 18F-flortaucipir positron emission tomography and vascular measures from MRI. Tau burden was associated with larger vessel size and lower CBF in the hippocampus. We postulate that larger vessel size may be associated with vascular alterations in AD/MCI.

BIOM-51. IDENTIFICATION OF A LOW-FLIP ANGLE BASED CBV THRESHOLD FOR FRACTIONAL TUMOR BURDEN (FTB) MAPPING IN RECURRENT GLIOBLASTOMA

Abstract INTRODUCTION Differentiation of glioblastoma recurrence from post treatment radiation effects (PTRE) would improve patient management. Dynamic susceptibility contrast (DSC) MRI can be used to generate fractional tumor burden (FTB) maps, via application of regional cerebral blood volume (RCBV) thresholds, to spatially differentiate recurrence from PTRE. FTB maps derived from a DSC-MRI protocol that includes a contrast agent preload in addition to the bolus injection and a moderate-flip angle (MFA) been histologically validated. Recently, a single-dose, low-flip angle (LFA) protocol was shown to provide RCBV measurements consistent with the MFA protocol and has been recommended as a consensus protocol. This study aims to identify the RCBV thresholds for the single-dose, LFA protocol that generates FTB maps, concordant with those obtained from the reference double-dose, MFA protocol. METHODS Two sets of DSC-MRI data were collected sequentially from 52 patients using a single-dose, low-flip angle (30o) protocol and a double-dose, moderate flip angle (60o) protocol using TR = 1.5s and TE = 30ms. Standardized RCBV maps (sRCBV) were obtained and co-registered with the post-contrast T1-weighted images using the FDA-approved clinical software, IB Neuro™ and IB Delta Suite™, respectively. The ground truth MFA-based FTB maps were computed using multiple sRCBV thresholds (1.0 and 1.75). An ROC analysis was conducted to identify the optimal, voxel-wise LFA sRCBV thresholds to produce FTB maps that best match the reference maps. RESULTS/ CONCLUSIONS The ROC analysis identified a lower LFA threshold of 1.0 to accurately distinguish PTRE from tumor recurrence and an upper threshold of 1.55 to differentiate aggressive tumor regions. The single-dose, LFA protocol eliminates variations in the contrast agent incubation time between the preload and bolus injection. This study provides motivation for the use of the single-dose LFA protocol in the management of patients with glioblastoma and is a reliable method for distinguishing tumor from PTRE.

A community-endorsed open-source lexicon for contrast agent-based perfusion MRI: A consensus guidelines report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI).

This manuscript describes the ISMRM OSIPI (Open Science Initiative for Perfusion Imaging) lexicon for dynamic contrast-enhanced and dynamic susceptibility-contrast MRI. The lexicon was developed by Taskforce 4.2 of OSIPI to provide standardized definitions of commonly used quantities, models, and analysis processes with the aim of reducing reporting variability. The taskforce was established in February 2020 and consists of medical physicists, engineers, clinicians, data and computer scientists, and DICOM (Digital Imaging and Communications in Medicine) standard experts. Members of the taskforce collaborated via a slack channel and quarterly virtual meetings. Members participated by defining lexicon items and reporting formats that were reviewed by at least two other members of the taskforce. Version 1.0.0 of the lexicon was subject to open review from the wider perfusion imaging community between January and March 2022, and endorsed by the Perfusion Study Group of the ISMRM in the summer of 2022. The initial scope of the lexicon was set by the taskforce and defined such that it contained a basic set of quantities, processes, and models to enable users to report an end-to-end analysis pipeline including kinetic model fitting. We also provide guidance on how to easily incorporate lexicon items and definitions into free-text descriptions (e.g., in manuscripts and other documentation) and introduce an XML-based pipeline encoding format to encode analyses using lexicon definitions in standardized and extensible machine-readable code. The lexicon is designed to be open-source and extendable, enabling ongoing expansion of its content. We hope that widespread adoption of lexicon terminology and reporting formats described herein will increase reproducibility within the field.

Assessment of the Effects of Sodium Nitroprusside Administered Via Intracranial Subdural Catheters on the Cerebral Blood Flow and Lactate Using Dynamic Susceptibility Contrast Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy in a Pig Cardiac Arrest Model.

Background Cerebral blood flow (CBF) is impaired in the early phase after return of spontaneous circulation. Sodium nitroprusside (SNP) administration via intracranial subdural catheters improves cerebral cortical microcirculation. We determined whether the SNP treatment improves CBF in the subcortical tissue and evaluated the effects of this treatment on cerebral lactate. Methods and Results Sixty minutes after return of spontaneous circulation following 14 minutes of untreated cardiac arrest, 14 minipigs randomly received 4 mg SNP or saline via intracranial subdural catheters. CBF was measured in regions of interest within the cerebrum and thalamus using dynamic susceptibility contrast-magnetic resonance imaging. After return of spontaneous circulation, CBF was expressed as a percentage of the baseline value. In the saline group, the %CBF in the regions of interest within the cerebrum remained at approximately 50% until 3.5 hours after return of spontaneous circulation, whereas %CBF in the thalamic regions of interest recovered to approximately 73% at this time point. The percentages of the baseline values in the cortical gray matter and subcortical white matter were higher in the SNP group (group effect P=0.026 and 0.025, respectively) but not in the thalamus. The cerebral lactate/creatine ratio measured using magnetic resonance spectroscopy increased over time in the saline group but not in the SNP group (group-time interaction P=0.035). The thalamic lactate/creatine ratio was similar in the 2 groups. Conclusions SNP administered via intracranial subdural catheters improved CBF not only in the cortical gray matter but also in the subcortical white matter. The CBF improvement by SNP was accompanied by a decrease in cerebral lactate.

NEED OF THE HOUR TO USE MR PERFUSION TO DIFFERENTIATE BETWEEN RADIATION NECROSIS AND TUMOUR RECURRENCE IN KNOWN GLIOMA PATIENTS

Conventional MR imaging may be non-specific in diagnosing low-grade gliomas. Better modalities are used nowadays including MRS (spectroscopy), perfusion, and diffusion-weighted imaging (1).
 MR perfusion is better than CT perfusion in various aspects. MR perfusion does not expose the patient to ionizing radiation. CT perfusion, on the other hand, can be harmful if the patient receives repeated doses. However, CT perfusion may be more readily available in emergency settings and can be quicker to perform than MR perfusion, which requires a longer acquisition time. CT perfusion can also provide more precise measurements of blood flow in small vessels than MR perfusion. Ultimately, the choice between MR perfusion and CT perfusion depends on the specific clinical situation and the preferences of the medical team.
 Perfusion MRI studies have become an increasingly important tool in the management of high-grade gliomas as well. Perfusion MRI allows clinicians to assess the vascularity of these tumors, providing information on tumor grade, response to treatment, and prognosis. Perfusion imaging can be performed using various techniques, including dynamic susceptibility contrast (DSC) MRI and arterial spin labeling (ASL) MRI. DSC-MRI is the most commonly used technique, and it provides information on cerebral blood volume (CBV) and cerebral blood flow (CBF) in the tumor and surrounding tissue. ASL-MRI, on the other hand, measures blood flow by magnetically labeling arterial blood water protons and can provide information on tumor perfusion without the use of contrast agents. Overall, perfusion MRI studies have demonstrated significant potential for improving the diagnosis, management, and prognosis of high-grade gliomas. MR brain perfusion is a type of imaging technique that can be used to differentiate between radiation-related changes and tumor recurrence in patients who have undergone radiation therapy for brain tumors. (2).
  
 Tumor resection followed by chemoradiation remains the current criterion standard treatment for high-grade gliomas. Radiation therapy can cause changes in the brain that may appear similar to tumor recurrence on conventional MRI scans. However, MR brain perfusion can detect differences in blood flow patterns between tumor tissue and normal brain tissue, which can help to differentiate between radiation-related changes/necrosis and tumor recurrence (3).
 Every institute should start using MR perfusion studies to differentiate these. This will eventually help in patient correct diagnosis and reduction in morbidity and mortality.
 Key-words:
 Gliomas, MR Perfusion, CT Perfusion, Tumour Recurrence, Radiation Necrosis.
  
 Conflict of Interest: All authors declare no conflict of interest.
  
 Funding Role: None to declare.
  
 Author Contributions: All authors contributed equally to the drafting of study.
  
 Ethical Statement:  Written as per the ethical guidelines of hospital board.
  

Stronger influence of systemic than local hemodynamic-vascular factors on resting-state BOLD functional connectivity

Correlated fluctuations in the blood oxygenation level dependent (BOLD) signal of resting-state functional MRI (i.e., BOLD-functional connectivity, BOLD-FC) reflect a spectrum of neuronal and non-neuronal processes. In particular, there are multiple hemodynamic-vascular influences on BOLD-FC on both systemic (e.g., perfusion delay) and local levels (e.g., neurovascular coupling). While the influence of individual factors has been studied extensively, combined and comparative studies of systemic and local hemodynamic-vascular factors on BOLD-FC are scarce, notably in humans. We employed a multi-modal MRI approach to investigate and compare distinct hemodynamic-vascular processes and their impact on homotopic BOLD-FC in healthy controls and patients with unilateral asymptomatic internal carotid artery stenosis (ICAS). Asymptomatic ICAS is a cerebrovascular disorder, in which neuronal functioning is largely preserved but hemodynamic-vascular processes are impaired, mostly on the side of stenosis. Investigated indicators for local hemodynamic-vascular processes comprise capillary transit time heterogeneity (CTH) and cerebral blood volume (CBV) from dynamic susceptibility contrast (DSC) MRI, and cerebral blood flow (CBF) from pseudo-continuous arterial spin labeling (pCASL). Indicators for systemic processes are time-to-peak (TTP) from DSC MRI and BOLD lags from functional MRI. For each of these parameters, their influence on BOLD-FC was estimated by a comprehensive linear mixed model. Equally across groups, we found that individual mean BOLD-FC, local (CTH, CBV, and CBF) and systemic (TTP and BOLD lag) hemodynamic-vascular factors together explain 40.7% of BOLD-FC variance, with 20% of BOLD-FC variance explained by hemodynamic-vascular factors, with an about two-times larger contribution of systemic versus local factors. We conclude that regional differences in blood supply, i.e., systemic perfusion delays, exert a stronger influence on BOLD-FC than impairments in local neurovascular coupling.

A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 1.5 Tesla field strength.

1.5 Tesla (1.5T) remain a significant field strength for brain imaging worldwide. Recent computer simulations and clinical studies at 3T MRI have suggested that dynamic susceptibility contrast (DSC) MRI using a 30° flip angle ("low-FA") with model-based leakage correction and no gadolinium-based contrast agent (GBCA) preload provides equivalent relative cerebral blood volume (rCBV) measurements to the reference-standard acquisition using a single-dose GBCA preload with a 60° flip angle ("intermediate-FA") and model-based leakage correction. However, it remains unclear whether this holds true at 1.5T. The purpose of this study was to test this at 1.5T in human high-grade glioma (HGG) patients. This was a single-institution cross-sectional study of patients who had undergone 1.5T MRI for HGG. DSC-MRI consisted of gradient-echo echo-planar imaging (GRE-EPI) with a low-FA without preload (30°/P-); this then subsequently served as a preload for the standard intermediate-FA acquisition (60°/P+). Both normalized (nrCBV) and standardized relative cerebral blood volumes (srCBV) were calculated using model-based leakage correction (C+) with IBNeuro™ software. Whole-enhancing lesion mean and median nrCBV and srCBV from the low- and intermediate-FA methods were compared using the Pearson's, Spearman's and intraclass correlation coefficients (ICC). Twenty-three HGG patients composing a total of 31 scans were analyzed. The Pearson and Spearman correlations and ICCs between the 30°/P-/C+ and 60°/P+/C+ acquisitions demonstrated high correlations for both mean and median nrCBV and srCBV. Our study provides preliminary evidence that for HGG patients at 1.5T MRI, a low FA, no preload DSC-MRI acquisition can be an appealing alternative to the reference standard higher FA acquisition that utilizes a preload.

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI.

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measured -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.