Dynamic Susceptibility Contrast MRI Data 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.

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.

A multiscale subvoxel perfusion model to estimate diffusive capillary wall conductivity in multiple sclerosis lesions from perfusion MRI data

We propose a new mathematical model to learn capillary leakage coefficients from dynamic susceptibility contrast MRI data. To this end, we derive an embedded mixed-dimension flow and transport model for brain tissue perfusion on a subvoxel scale. This model is used to obtain the contrast agent concentration distribution in a single MRI voxel during a perfusion MRI sequence. We further present a magnetic resonance signal model for the considered sequence including a model for local susceptibility effects. This allows modeling MR signal-time curves that can be compared with clinical MRI data. The proposed model can be used as a forward model in the inverse modeling problem of inferring model parameters such as the diffusive capillary wall conductivity. Acute multiple sclerosis lesions are associated with a breach in the integrity of the blood-brain barrier. Applying the model to perfusion MR data of a patient with acute multiple sclerosis lesions, we conclude that diffusive capillary wall conductivity is a good indicator for characterizing activity of lesions, even if other patient-specific model parameters are not well-known.

Blood Perfusion and Cellular Microstructural Changes Associated With Iron Deposition in Multiple Sclerosis Lesions

Background and Purpose: Susceptibility-weighted imaging (SWI) has emerged as a useful clinical tool in many neurological diseases including multiple sclerosis (MS). This study aims to investigate the relationship between SWI signal changes due to iron deposition in MS lesions and tissue blood perfusion and microstructural abnormalities to better understand their underlying histopathologies.Methods: Forty-six patients with relapsing remitting MS were recruited for this study. Conventional FLAIR, pre- and post-contrast T1-weighted imaging, SWI, diffusion tensor imaging (DTI), and dynamic susceptibility contrast (DSC) perfusion MRI were performed in these patients at 3T. The SWI was processed using both magnitude and phase information with one slice minimal intensity projection (mIP) and phase multiplication factor of 4. MS lesions were classified into 3 types based on their lesional signal appearance on SWI mIP relative to perilesional normal appearing white matter (peri-NAWM): Type-1: hypointense, Type-2: isointense, and Type-3: hyperintense lesions. The DTI and DSC MRI data were processed offline to generate DTI-derived mean diffusivity (MD) and fractional anisotropy (FA) maps, as well as DSC-derived cerebral blood flow (CBF) and cerebral blood volume (CBV) maps. Comparisons of diffusion and perfusion measurements between lesions and peri-NAWM, as well between different types of lesions, were performed.Results: A total of 137 lesions were identified on FLAIR in these patients that include 40 Type-1, 46 Type-2, and 51 Type-3 lesions according to their SWI intensity relative to peri-NAWM. All lesion types showed significant higher MD and lower FA compared to their peri-NAWM (P < 0.0001). Compared to Type-1 lesions (likely represent iron deposition), Type-2 lesions had significantly higher MD and lower FA (P < 0.001) as well as lower perfusion measurements (P < 0.05), while Type 3 lesions had significantly higher perfusion (P < 0.001) and lower FA (P < 0.05). Compared to Type-2, Type-3 lesions had higher perfusion (P < 0.0001) and marginally higher MD and lower FA (P < 0.05).Conclusion: The significant differences in diffusion and perfusion MRI metrics associated with MS lesions, that appear with different signal appearance on SWI, may help to identify the underlying destructive pathways of myelin and axons and their evolution related to inflammatory activities.

Evaluation of cerebrovascular reserve in patients with cerebrovascular diseases using resting-state MRI: A feasibility study

PurposeTo demonstrate the feasibility of mapping cerebrovascular reactivity (CVR) using resting-state functional MRI (fMRI) data without gas or other challenges in patients with cerebrovascular diseases and to show that brain regions affected by the diseases have diminished vascular reactivity. Materials and methodsTwo sub-studies were performed on patients with stroke and Moyamoya disease. In Study 1, 20 stroke patients (56.3 ± 9.7 years, 7 females) were enrolled and resting-state blood‑oxygenation-level-dependent (rs-BOLD) fMRI data were collected, from which CVR maps were computed. CVR values were compared across lesion, perilesional and control ROIs defined on anatomic images. Reproducibility of the CVR measurement was tested in 6 patients with follow-up scans. In Study 2, rs-BOLD fMRI and dynamic susceptibility contrast (DSC) MRI scans were collected in 5 patients with Moyamoya disease (32.4 ± 8.2 years, 4 females). Cerebral blood flow (CBF), cerebral blood volume (CBV), and time-to-peak (TTP) maps were obtained from the DSC MRI data. CVR values were compared between stenotic brain regions and control regions perfused by non-stenotic arteries. ResultsIn stroke patients, lesion CVR (0.250 ± 0.055 relative unit (r.u.)) was lower than control CVR (0.731 ± 0.088 r.u., p = 0.0002). CVR was also lower in the perilesional regions in a graded manner (perilesion 1 CVR = 0.422 ± 0.051 r.u., perilesion 2 CVR = 0.492 ± 0.046 r.u.), relative to that in the control regions (p = 0.005 and 0.036, respectively). In the repeatability analysis, a strong correlation was observed between lesion CVR (r2 = 0.91, p = 0.006) measured at two time points, as well as between control CVR (r2 = 0.79, p = 0.036) at two time points. In Moyamoya patients, CVR in the perfusion deficit regions delineated by DSC TTP maps (0.178 ± 0.189 r.u.) was lower than that in the control regions (0.868 ± 0.214 r.u., p = 0.013). Furthermore, the extent of reduction in CVR was significantly correlated with the extent of lengthening in TTP (r2 = 0.91, p = 0.033). ConclusionOur findings suggested that rs-BOLD data can be used to reproducibly evaluate CVR in patients with cerebrovascular diseases without the use of any vasoactive challenges.

Time-Efficient Perfusion Imaging Using DCE- and DSC-MRI

Abstract Dynamic contrast enhanced MRI (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) are perfusion imaging techniques used mainly for clinical and preclinical measurement of vessel permeability and capillary blood flow, respectively. It is advantageous to apply both methods to exploit their complementary information about the perfusion status of the tissue. We propose a novel acquisition method that combines advantages of the current simultaneous and sequential acquisition. The proposed method consists of a DCE-MRI acquisition interrupted by DSC-MRI acquisition. A new method for processing of the DCE-MRI data is proposed which takes the interleaved acquisition into account. Analysis of both the DCE- and DSC-MRI data is reformulated so that they are approximated by the same pharmacokinetic model (constrained distributed capillary adiabatic tissue homogeneity model). This provides a straightforward evaluation of the methodology as some of the estimated DCE- and DSC-MRI perfusion parameters should be identical. Evaluation on synthetic data showed an acceptable precision and no apparent bias introduced by the interleaved character of the DCE-MRI acquisition. Intravascular perfusion parameters obtained from clinical glioma data showed a fairly high correlation of blood flow estimates from DCE- and DSC-MRI, however, an unknown scaling factor was still present mainly because of the tissue-specific r 2 * $r_2^*$ relaxivity. The results show validity of the proposed acquisition method. They also indicate that simultaneous processing of both DCE- and DSC-MRI data with joint estimation of some perfusion parameters (included in both DCE- and DSC-MRI) might be possible to increase the reliability of the DCE- and DSC-MRI methods alone.

Stable spline deconvolution for dynamic susceptibility contrast MRI.

To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI. The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets. The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD. The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

A simplified spin and gradient echo approach for brain tumor perfusion imaging.

In this study, we propose a simplified acquisition and analysis approach for spin and gradient echo (SAGE)-based dynamic susceptibility-contrast MRI (DSC-MRI) data that is free of contrast agent T1 leakage effects. A five-echo SAGE sequence was used to acquire DSC-MRI data in rat C6 tumors (n = 7). Nonlinear fitting of all echoes was performed to obtain T1-insensitive ΔR2* and ΔR2 time series. The simplified approach, which includes two gradient echoes and one spin echo, was also used to analytically compute T1-insensitive ΔR2* using the two gradient echoes and ΔR2 using all three echoes. The blood flow, blood volume, and vessel size values derived from each method were compared. In all cases, the five-echo and simplified SAGE ΔR2* and ΔR2 were in excellent agreement and demonstrated significant T1 leakage correction compared with the uncorrected single-echo data. The derived hemodynamic parameters for blood volume, blood flow, and vessel size were not significantly different between the two methods. The proposed simplified SAGE technique enables the acquisition of gradient and spin echo DSC-MRI data corrected for T1 leakage effects yields parameters that are in agreement with the five-echo SAGE approach and does not require nonlinear fitting to extract ΔR2* and ΔR2 time series.

Dynamic susceptibility contrast MRI with a prebolus contrast agent administration design for improved absolute quantification of perfusion

Arterial partial-volume effects (PVEs) often hamper reproducible absolute quantification of cerebral blood flow (CBF) and cerebral blood volume (CBV) obtained by dynamic susceptibility contrast MRI (DSC-MRI). The aim of this study was to examine whether arterial PVEs in DSC-MRI data can be minimized by rescaling the arterial input function (AIF) using a sagittal-sinus venous output function obtained following a prebolus administration of a low dose of contrast agent. The study was carried out as a test-retest experiment in 20 healthy volunteers to examine the repeatability of the CBF and CBV estimates. All subjects were scanned twice with 7-20 days between investigations. DSC-MRI returned an overestimated average whole-brain CBF of 220 ± 44 mL/100 g/min (mean ± SD) before correction and 44 ± 15 mL/100 g/min when applying the prebolus design, averaged over all scans. Average whole-brain CBV was 20 ± 2.0 mL/100 g before correction and 4.0 ± 1.0 mL/100 g after prebolus correction. Quantitative estimates of CBF and CBV, obtained with the proposed prebolus DSC-MRI technique, approached those typically obtained by other perfusion modalities. The CBF and CBV estimates showed good repeatability.

Automated macrovessel artifact correction in dynamic susceptibility contrast magnetic resonance imaging using independent component analysis

Dynamic susceptibility contrast-MRI is the most commonly used functional MRI-based method for studying changes in cerebral perfusion. However, several studies indicated a systematic overestimation of perfusion parameters compared with other imaging modalities related to the high sensitivity of dynamic susceptibility contrast-MRI for blood flow in large vessels. In this study, we therefore suggest an improved, automated, robust, and efficient method allowing for generating hemodynamic parameter maps where signal influence from large vessels is minimized. Based on independent component analysis, this fully automated approach corrects dynamic susceptibility contrast-MRI data without any user interaction, thus making a clinical applicability possible. The accuracy of the proposed method was tested in 10 patients with cerebrovascular disease. Application of our correction algorithm resulted in a significant reduction of the effect of macrovessel signal on hemodynamic parameters like the cerebral blood flow and the cerebral blood volume compared with uncorrected data. As desired, our method specifically corrected for macrovessel artifacts in cortical grey matter tissue, leaving white matter tissue parameters largely unaffected. This may increase sensitivity and reliability of detecting perfusion abnormalities in patient groups, in particular with regard to stroke and other cerebrovascular disorders.

Minimising the effects of bolus dispersion in bolus‐tracking MRI

Bolus-tracking perfusion measurements in patients with vascular abnormalities are often unreliable, because delay and/or dispersion of the bolus within the vessels distorts the measured arterial input function (AIF). Erroneous measurements of perfusion can be identified by examining the measured response function, the shape of which is determined by both the tissue and arterial retention. In this work, an accurate response function is extracted by combining maximum-likelihood expectation-maximisation deconvolution, regularised using an oscillation index, with subsequent wavelet thresholding. Simulations show that this method recovers both the smooth-dispersed and the sharp-delayed response functions. This enables regions where the bolus is delayed and/or dispersed to be identified when the methodology is applied to data from patients with vascular abnormalities. Simulations also demonstrate robust and accurate perfusion estimates when there is no bolus delay and/or dispersion. The presence of delay and/or dispersion in the response function suggests that the perfusion measurements are erroneous, and that the global AIF is an inaccurate approximation to the true AIF in these regions. Perfusion measurements are corrected within the affected regions by defining a regional AIF from the independent component analysis of the dynamic susceptibility contrast MRI data. The regional AIF is shown to remove the delay and dispersion, improving the accuracy of the perfusion maps.

SU-GG-I-134: Applications of the Levenberg-Marquardt Nonlinear Least Square Fitting to Obtain the Optimum Arterial Input Function for DSC-MRI

Purpose: To develop an advanced non‐linear curve fitting (NLCF) algorithm for analyzing dynamic susceptibility contrast MRI data of the human brain.Method and Materials: Relative cerebral blood flow (rCBF) was assessed using dynamic susceptibility‐contrast MRI at 1.5 T. A simultaneous dual FLASH pulse sequence and Gd‐DTPA (0.2 mmol/kg) were used for 20 volunteers, with measurement of rCBF in white matter (WM) and in gray matter (GM). The acquired data was deconvolved with arterial input functions (AIF) using singular value decomposition (SVD). The truncated Taylor expansions of the cost function at the current iteration of the measured signal were employed to derive the Levenberg‐Marquardt equation in the optimization. Results: From each of the recalculated parameters, a perfusion‐weighted image was outlined by use of the modified non‐linear curve fitting algorithm. The results were an improved estimation of rCBF for the volunteers. The average GM/WM rCBF ratios were 1.90 ± 0.09 (n = 20), using deconvolution with AIF and SVD. Conclusion: The present study demonstrated an improvement of the kinetic parameters from the dynamic contrast‐enhanced (DCE) T2*‐weighted magnetic resonance imaging data with the use of contrast agents.

Altered regional blood volume in chronic cannabis smokers.

The quantitative measurement of cerebral perfusion is crucial for the study of both normal and impaired human brain function. Although cannabis is the most commonly abused illicit substance in the United States, its effects on cerebral blood volume (CBV) have not been fully examined. The objective of the present study was to examine differences in relative regional blood volume in focal regions of interest--including the frontal lobe, the temporal lobe, and the cerebellum--during a period of supervised abstinence from cannabis. Dynamic susceptibility contrast MRI data were collected on 12 current, long-term daily cannabis users between 6 and 36 hr after the subjects' last reported cannabis use. Resting-state CBV images were also acquired in 17 healthy comparison subjects. Data were acquired in the axial plane with a 1.5-Tesla GE Signa scanner following a bolus of gadolinium contrast agent. Cannabis users demonstrated significantly increased blood volumes in the right frontal area (p < .05), in the left temporal area (p < .005), and in the cerebellum (p < .005) relative to comparison subjects. Among the cannabis users, there were no significant correlations between regional blood volumes and either total lifetime episodes of smoking or urinary tetrahydrocannabinol concentrations. These findings have important implications for understanding the effects of chronic heavy cannabis use on brain function. It would be of interest to extend the investigation beyond 6-36 hr of abstinence from cannabis to determine whether increased CBV values persist for several weeks or eventually normalize.

Regional cerebral blood volume measured by dynamic susceptibility contrast MR imaging in alzheimer's disease: A principal components analysis

Dynamic susceptibility contrast (DSC) MRI is an alternative to positron emission tomography (PET) and single photon emission computed tomography (SPECT) for the evaluation of cerebral hemodynamics in patients with Alzheimer's disease. DSC MRI allows the construction of high resolution images of cerebral blood volume (CBV) without the use of radionuclides or ionizing radiation. In this study, DSC MRI data were collected from 16 patients with probable Alzheimer's disease and 16 age-matched control subjects. Characteristic patterns of regional CBV variation were found using principal component analysis. Three such patterns were identified: a global variation pattern, an anterior-to-posterior CBV gradient, and a temporoparietal pattern. Group differences in the principal component scores associated with the global and temporoparietal patterns (P = .08 and P = .007, respectively) suggest that these deficits reflect characteristic CBV abnormalities in Alzheimer's disease. Using only these two scores, the Alzheimer's disease group was classified with a sensitivity of 81% and a specificity of 88%. Additionally, disease severity, as measured by the Mini-Mental State Examination (MMSE), was correlated significantly with the third principal component score (Pearson's r = .50, P = .05).