Dynamic Susceptibility Contrast MRI Research Articles

Role of Contrast Perfusion Weighted Magnetic Resonance Imaging in Grading of Brain Tumors with Histopathological Correlation

Background: Traditional MRI sequences provide precise anatomical information about brain tumors. But these sequences can’t quantitatively evaluate vascular physiology, or capture tumor biology at the molecular level; which is important for tumor grading, therapeutic assessment, and prognostication. Furthermore, non-enhancing parts of the tumor affected brain, which generally indicates peri-tumoral brain edema with infiltrative tumor cells, is not visible on conventional MRI sequences. Perfusion weighted Magnetic Resonance Imaging (PW-MRI) techniques such as Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) have shown promise as imaging biomarkers for glioma therapy, since these new sequences can also provide information about vascular hemodynamics. Our study was first prospective study from Himalayan belt of India, wherein we tried to assess the role of PW-MRI in the grading of brain tumors, with histopathological correlation. Aim and Objective: To compare tumor perfusion and histopathological tumor grading, in evaluation of brain tumor especially gliomas. Material and Method: 40 patients who were referred to Department of Radiodiagnosis for evaluation of brain tumor, and who gave informed consent, were included in this prospective study, done over a period of 18 months. All patients underwent MRI, followed by surgical resection. Observation and Result: In our study, diagnostic accuracy was 90% for mean rCBV (lesion) (cut off: 7 by ROC), mean rCBF (lesion) (cut off: 13.9 by ROC), and rCBF ratio (cut off: 2.9 by ROC); for differentiating benign versus malignant brain tumor. rCBF ratio was found to be best diagnostic parameter with sensitivity of 96%, specificity of 80%, positive predictive value of 89%, negative predictive value of 92%, diagnostic accuracy of 90%, area under ROC curve=0.909, and p value<0.001. Conclusion: We come to conclusion that high grade brain tumor display higher CBF, than do low grade brain tumor. Three parameters showed good diagnostic accuracy, which includes rCBF ratio, mean rCBF (lesion), and mean rCBV (lesion). These parameters can be used to predict the grade of the brain tumor, with good diagnostic accuracy.

Perfusion MRI-based differentiation between early tumor progression and pseudoprogression in glioblastoma and its use in clinical practice

Abstract Background Early treatment effects in patients with glioblastoma are frequently discussed during multidisciplinary team meetings (MDTM), after which a decision regarding (dis)continuation of tumor-targeted treatment is made. This study examined whether a separate and systematic evaluation of perfusion MRI (pMRI) could impact such treatment decisions in the early stage. Methods This retrospective observational study evaluated the diagnostic accuracy for detecting early tumor progression of 4 different approaches including conventional MRI, pMRI with Arterial Spin Labeling (ASL), and/or Dynamic Susceptibility Contrast (DSC) MRI, and compared those to the MDTM evaluation in clinical practice. Results Sixty-five glioblastoma patients with clinical and radiological data until 9 months after irradiation were included. For all approaches, the sensitivity for detecting early true disease progression was poor to moderate (32%–62%). Area under the curve values were comparable (range 0.63–0.74), but highest for the MDTM evaluation (0.74). In the cases of inconclusive MDTM (26%), systematic pMRI evaluation showed a higher sensitivity compared to conventional MRI (respectively, 36% vs 0%), while the specificity was 100% for all MRI approaches. Multivariable regression analysis showed that a lower KPS score (OR = 0.84 [95% CI: 0.77–0.91]) and pMRI indicative of tumor progression (OR = 0.09 [95% CI: 0.02–0.52]) were independently associated with concluding tumor progression at the MDTM. Conclusion MDTM assessment in daily clinical practice has a higher diagnostic accuracy in distinguishing early tumor progression from pseudoprogression compared to a separate, systematic evaluation of pMRI. Systematic evaluation of pMRI might be helpful if the clinical MDTM assessment is uncertain.

Multi-Site Benchmark Study for Standardized Relative Cerebral Blood Volume in Untreated Brain Metastases Using the DSC-MRI Consensus Acquisition Protocol.

A national consensus recommendation for the collection of DSC (dynamic susceptibility contrast) MRI perfusion data, used to create maps of relative cerebral blood volume (rCBV), has been recently established for primary and metastatic brain tumors. The goal was to reduce inter-site variability and improve ease of comparison across time and sites, fostering widespread use of this informative measure. To translate this goal into practice the prospective collection of consensus DSC-MRI data and characterization of derived rCBV maps in brain metastases is needed. The purpose of this multi-site study was to determine rCBV in untreated brain metastases in comparison to glioblastoma and normal appearing brain using the national consensus protocol. Subjects from three sites with untreated enhancing brain metastases underwent DSC-MRI according to a recommended option that uses a mid-range flip angle, GRE-EPI acquisition and the administration of both a pre-load and 2nd DSC-MRI dose of 0.1 mmol/kg GBCA. Quantitative maps of standardized rCBV (sRCBV) were generated and enhancing lesion ROIs determined from post-contrast T1-weighted images alone or calibrated difference maps, termed delta T1 (dT1) maps. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and glioblastoma (GBM) from a previous study. Comparisons were performed using either the Wilcoxon signed-rank test for paired comparisons or the Mann-Whitney nonparametric test for unpaired comparisons. 49 patients with a primary histology of lung (n=25), breast (n=6), squamous cell carcinoma (SCC) (n=1), melanoma (n=5), gastrointestinal (GI) (n=3) and genitourinary (GU) (n=9) were included in comparison to GBM (n=31). The mean sRCBV of all metastases (1.83+/-1.05) were significantly lower (p=0.0009) than mean sRCBV for GBM (2.67±1.34) with both statistically greater (p<0.0001) than NAWM (0.68 +/- 0.18). Histologically distinct metastases are each statistically greater than NAWM (p<0.0001) with lung (p=0.0002) and GU (p=.02) sRCBV being significantly different than GBM sRCBV. 49 patients with a primary histology of lung (n=25), breast (n=6), squamous cell carcinoma (SCC) (n=1), melanoma (n=5), gastrointestinal (GI) (n=3) and genitourinary (GU) (n=9) were included in comparison to GBM (n=31). The mean sRCBV of all metastases (1.83+/-1.05) were significantly lower (p=0.0009) than mean sRCBV for GBM (2.67+1.34) with both statistically greater (p<0.0001) than NAWM (0.68 +/- 0.18). Histologically distinct metastases are each statistically greater than NAWM (p<0.0001) with lung (p=0.0002) and GU (p=.02) sRCBV being significantly different than GBM sRCBV. dT1=delta T1; GBCA=gadolinium-based contrast agent; NAWM=normal appearing white matter; normalized relative cerebral blood volume=nRCBV; relative cerebral blood volume=rCBV; standardized relative cerebral blood volume=sRCBV.

Radiomics analysis of cerebral blood flow suggests a possible link between perfusion homogeneity and poor glioblastoma multiforme prognosis

Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies. Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes. Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively). Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.

Quantification of Collateral Supply with Local-AIF Dynamic Susceptibility Contrast MRI Predicts Infarct Growth.

In ischemic stroke, leptomeningeal collaterals can provide delayed and dispersed compensatory blood flow to tissue-at-risk despite an occlusion and can impact treatment response and infarct growth. The purpose of this work is to test the hypothesis that inclusion of this delayed and dispersed flow with an appropriately calculated Local Arterial Input Function (Local-AIF) is needed to quantify the degree of collateral blood supply in tissue distal to an occlusion. Seven experiments were conducted in a pre-clinical middle cerebral artery occlusion model. Dynamic susceptibility contrast MRI was imaged and post-processed to yield quantitative cerebral blood flow (qCBF) maps with both a traditionally chosen single arterial input function applied globally to the whole brain (i.e. "Global-AIF") and a delay and dispersion corrected AIF (i.e. "Local-AIF") that is sensitive to retrograde flow. Leptomeningeal collateral arterial recruitment was quantified with a pial collateral score from x-ray angiograms, and infarct growth calculated from serially acquired diffusion weighted MRI scans. The degree of collateralization at x-ray correlated more strongly with qCBF using the Local-AIF in the ischemic penumbra (R2=0.81) than traditionally chosen Global-AIF (R2=0.05). qCBF using a Local-AIF was negatively correlated (less infarct progression as perfusion increased) with infarct growth (R2 = 0.79) more strongly than a Global-AIF (R2=0.02). In acute stroke, qCBF calculated with a Local-AIF is more accurate for assessing tissue status and collateral supply than traditionally chosen Global-AIFs. These findings support use of a Local-AIF that corrects for delayed and dispersed retrograde flow in determining quantitative tissue perfusion with collateral supply in occlusive disease. MRI = magnetic resonance imaging; DSC = dynamic susceptibility contrast; PCS = pial collateral score; MCAO = middle cerebral artery occlusion; MCA = middle cerebral artery; AIF = arterial input function; rCBF = relative cerebral blood flow; qCBF = quantitative cerebral blood flow.

Perfusion parameter map generation from TOF-MRA in stroke using generative adversarial networks

PurposeTo generate perfusion parameter maps from Time-of-flight magnetic resonance angiography (TOF-MRA) images using artificial intelligence to provide an alternative to traditional perfusion imaging techniques. Materials and methodsThis retrospective study included a total of 272 patients with cerebrovascular diseases; 200 with acute stroke (from 2010 to 2018), and 72 with steno-occlusive disease (from 2011 to 2014). For each patient the TOF MRA image and the corresponding Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) were retrieved from the datasets. The authors propose an adapted generative adversarial network (GAN) architecture, 3D pix2pix GAN, that generates common perfusion maps (CBF, CBV, MTT, TTP, Tmax) from TOF-MRA images. The performance was evaluated by the structural similarity index measure (SSIM). For a subset of 20 patients from the acute stroke dataset, the Dice coefficient was calculated to measure the overlap between the generated and real hypoperfused lesions with a time-to-maximum (Tmax) > 6 s. ResultsThe GAN model exhibited high visual overlap and performance for all perfusion maps in both datasets: acute stroke (mean SSIM 0.88–0.92, mean PSNR 28.48–30.89, mean MAE 0.02–0.04 and mean NRMSE 0.14–0.37) and steno-occlusive disease patients (mean SSIM 0.83–0.98, mean PSNR 23.62–38.21, mean MAE 0.01–0.05 and mean NRMSE 0.03–0.15). For the overlap analysis for lesions with Tmax>6 s, the median Dice coefficient was 0.49. ConclusionOur AI model can successfully generate perfusion parameter maps from TOF-MRA images, paving the way for a non-invasive alternative for assessing cerebral hemodynamics in cerebrovascular disease patients. This method could impact the stratification of patients with cerebrovascular diseases. Our results warrant more extensive refinement and validation of the method.

E.2 Proof of concept for liquid biopsy: positive correlation between extracellular vesicles shed by high grade gliomas and volume of hypervascular tumour tissue on MRI

Background: High grade gliomas (HGGs) shed extracellular vesicles (EVs) into the bloodstream. EV-derived RNA (EV-RNA) can be detected in plasma, making it a potential biomarker for HGG recurrence after treatment. We sought to establish a baseline relationship between EV-RNA in plasma and hypervascular HGG tissue on MRI. Methods: Eight patients with a new diagnosis of HGG had measurements of plasma EV-RNA and contemporaneous dynamic susceptibility contrast (DSC) MRI. Patient-specific median signal intensity of corpus callosum (mSI-CC) was determined from 10 measurements on the relative cerebral blood volume (rCBV) map. Tumour tissue with signal intensity &gt; mSI-CC and &gt; 2x, &gt; 3x, &gt; 4x and &gt; 5x mSI-CC was segmented on the rCBV map. EV-RNA plasma concentration was correlated with tissue volumes. Results: Pearson correlation showed a significant positive relationship between EV-RNA plasma concentration and tissue volume with signal intensity &gt; mSI-CC (r(6) = 0.899, p = 0.002). No significant relationship could be detected for progressively smaller tissue volumes with signal intensity &gt; 2x, &gt; 3x, &gt; 4x and &gt; 5x mSI-CC. Conclusions: EV-RNA plasma concentration correlates strongly with the total volume of hypervascular HGG tissue on DSC MRI at baseline and merits further evaluation as a biomarker of tumour behaviour in longitudinal imaging studies.

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 (CCl 4 )-induced CLD models. Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl 4 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 CCl 4 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.

Diffuse glioma molecular profiling with arterial spin labeling and dynamic susceptibility contrast perfusion MRI: A comparative study.

Evaluation of molecular markers (IDH, pTERT, 1p/19q codeletion, and MGMT) in adult diffuse gliomas is crucial for accurate diagnosis and optimal treatment planning. Dynamic Susceptibility Contrast (DSC) and Arterial Spin Labeling (ASL) perfusion MRI techniques have both shown good performance in classifying molecular markers, however, their performance has not been compared side-by-side. Pretreatment MRI data from 90 patients diagnosed with diffuse glioma (54 men/36 female, 53.1±15.5 years, grades 2-4) were retrospectively analyzed. DSC-derived normalized cerebral blood flow/volume (nCBF/nCBV) and ASL-derived nCBF in tumor and perifocal edema were analyzed in patients with available IDH-mutation (n = 67), pTERT-mutation (n = 39), 1p/19q codeletion (n = 33), and MGMT promoter methylation (n = 31) status. Cross-validated uni- and multivariate logistic regression models assessed perfusion parameters' performance in molecular marker detection. ASL and DSC perfusion parameters in tumor and edema distinguished IDH-wildtype (wt) and pTERT-wt tumors from mutated ones. Univariate classification performance was comparable for ASL-nCBF and DSC-nCBV in IDH (maximum AUROCC 0.82 and 0.83, respectively) and pTERT (maximum AUROCC 0.70 and 0.81, respectively) status differentiation. The multivariate approach improved IDH (DSC-nCBV AUROCC 0.89) and pTERT (ASL-nCBF AUROCC 0.8 and DSC-nCBV AUROCC 0.86) classification. However, ASL and DSC parameters could not differentiate 1p/19q codeletion or MGMT promoter methylation status. Positive correlations were found between ASL-nCBF and DSC-nCBV/-nCBF in tumor and edema. ASL is a viable gadolinium-free replacement for DSC for molecular characterization of adult diffuse gliomas.

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.