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

PURPOSE: 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. 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. RESULTS: 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. CONCLUSION: 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.

Radiomics using non-contrast CT to predict hemorrhagic transformation risk in stroke patients undergoing revascularization.

OBJECTIVES: This study explores whether textural features from initial non-contrast CT scans of infarcted brain tissue are linked to hemorrhagic transformation susceptibility. MATERIALS AND METHODS: Stroke patients undergoing thrombolysis or thrombectomy from Jan 2012 to Jan 2022 were analyzed retrospectively. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging. A total of 94 radiomic features were extracted from the infarcted tissue on initial NCCT scans. Patients were divided into training and test sets (7:3 ratio). Two models were developed with fivefold cross-validation: one incorporating first-order and textural radiomic features, and another using only textural radiomic features. A clinical model was also constructed using logistic regression with clinical variables, and test set validation was performed. RESULTS: Among 362 patients, 218 had hemorrhagic transformations. The LightGBM model with all radiomics features had the best performance, with an area under the receiver operating characteristic curve (AUROC) of 0.986 (95% confidence interval [CI], 0.971-1.000) on the test dataset. The ExtraTrees model performed best when textural features were employed, with an AUROC of 0.845 (95% CI, 0.774-0.916). Minimum, maximum, and ten percentile values were significant predictors of hemorrhagic transformation. The clinical model showed an AUROC of 0.544 (95% CI, 0.431-0.658). The performance of the radiomics models was significantly better than that of the clinical model on the test dataset (p < 0.001). CONCLUSIONS: The radiomics model can predict hemorrhagic transformation using NCCT in stroke patients. Low Hounsfield unit was a strong predictor of hemorrhagic transformation, while textural features alone can predict hemorrhagic transformation. CLINICAL RELEVANCE STATEMENT: Using radiomic features extracted from initial non-contrast computed tomography, early prediction of hemorrhagic transformation has the potential to improve patient care and outcomes by aiding in personalized treatment decision-making and early identification of at-risk patients. KEY POINTS: • Predicting hemorrhagic transformation following thrombolysis in stroke is challenging since multiple factors are associated. • Radiomics features of infarcted tissue on initial non-contrast CT are associated with hemorrhagic transformation. • Textural features on non-contrast CT are associated with the frailty of the infarcted tissue.

Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches.

BACKGROUND: A typical stroke MRI protocol includes perfusion-weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue. PURPOSE: To acquire both PWI and MRA simultaneously using deep learning approaches. STUDY TYPE: Retrospective. SUBJECTS: A total of 60 patients (30-73 years old, 31 females) with ischemic symptoms due to occlusion or ≥50% stenosis (measured relative to proximal artery diameter) of the internal carotid artery, middle cerebral artery, or anterior cerebral artery. The 51/1/8 patient data were used as training/validation/test. FIELD STRENGTH/SEQUENCE: A 3 T, time-resolved angiography with stochastic trajectory (contrast-enhanced MRA) and echo planar imaging (dynamic susceptibility contrast MRI, DSC-MRI). ASSESSMENT: We investigated eight different U-Net architectures with different encoder/decoder sizes and with/without an adversarial network to generate perfusion maps from contrast-enhanced MRA. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), mean transit time (MTT), and time-to-max (Tmax ) were mapped from DSC-MRI and used as ground truth to train the networks and to generate the perfusion maps from the contrast-enhanced MRA input. STATISTICAL TESTS: Normalized root mean square error, structural similarity (SSIM), peak signal-to-noise ratio (pSNR), DICE, and FID scores were calculated between the perfusion maps from DSC-MRI and contrast-enhanced MRA. One-tailed t-test was performed to check the significance of the improvements between networks. P values < 0.05 were considered significant. RESULTS: The four perfusion maps were successfully extracted using the deep learning networks. U-net with multiple decoders and enhanced encoders showed the best performance (pSNR 24.7 ± 3.2 and SSIM 0.89 ± 0.08 for rCBV). DICE score in hypo-perfused area showed strong agreement between the generated perfusion maps and the ground truth (highest DICE: 0.95 ± 0.04). DATA CONCLUSION: With the proposed approach, dynamic angiography MRI may provide vessel architecture and perfusion-relevant parameters simultaneously from a single scan. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 5.

Detection of Cerebral Microbleeds in MR Images Using a Single-Stage Triplanar Ensemble Detection Network (TPE-Det).

BACKGROUND: Cerebral microbleeds (CMBs) are microscopic brain hemorrhages with implications for various diseases. Automated detection of CMBs is a challenging task due to their wide distribution throughout the brain, small size, and visual similarity to their mimics. For this reason, most of the previously proposed methods have been accomplished through two distinct stages, which may lead to difficulties in integrating them into clinical workflows. PURPOSE: To develop a clinically feasible end-to-end CMBs detection network with a single-stage structure utilizing 3D information. This study proposes triplanar ensemble detection network (TPE-Det), ensembling 2D convolutional neural networks (CNNs) based detection networks on axial, sagittal, and coronal planes. STUDY TYPE: Retrospective. SUBJECTS: Two datasets (DS1 and DS2) were used: 1) 116 patients with 367 CMBs and 12 patients without CMBs for training, validation, and testing (70.39 ± 9.30 years, 68 women, 60 men, DS1); 2) 58 subjects with 148 microbleeds and 21 subjects without CMBs only for testing (76.13 ± 7.89 years, 47 women, 32 men, DS2). FIELD STRENGTH/SEQUENCE: A 3 T field strength and 3D GRE sequence scan for SWI reconstructions. ASSESSMENT: The sensitivity, FPavg (false-positive per subject), and precision measures were computed and analyzed with statistical analysis. STATISTICAL TESTS: A paired t-test was performed to investigate the improvement of detection performance by the suggested ensembling technique in this study. A P value < 0.05 was considered significant. RESULTS: The proposed TPE-Det detected CMBs on the DS1 testing set with a sensitivity of 96.05% and an FPavg of 0.88, presenting statistically significant improvement. Even when the testing on DS2 was performed without retraining, the proposed model provided a sensitivity of 85.03% and an FPavg of 0.55. The precision was significantly higher than the other models. DATA CONCLUSION: The ensembling of multidimensional networks significantly improves precision, suggesting that this new approach could increase the benefits of detecting lesions in the clinic. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Outcome of epidural blood patch for imaging-negative spontaneous intracranial hypotension.

BACKGROUND: Spontaneous intracranial hypotension is diagnosed by an abnormal finding in brain MRI, spinal imaging, or lumbar puncture. However, the sensitivity of each test is low. We investigated whether patients with suspected spontaneous intracranial hypotension and negative imaging findings would respond to epidural blood patch. METHODS: We prospectively recruited patients with new-onset orthostatic headache admitted at the Samsung Medical Center from January 2017 to July 2021. In patients without abnormal imaging findings and no history of prior epidural blood patch, treatment outcome-defined as both 50% response in maximal headache intensity and improvement of orthostatic component-was collected at discharge and three months after epidural blood patch. RESULTS: We included 21 treatment-naïve patients with orthostatic headache and negative brain and spinal imaging results who received epidural blood patch. After epidural blood patch (mean 1.3 times, range 1-3), 14 (66.7%) and 19 (90.5%) patients achieved both 50% response and improvement of orthostatic component at discharge and three months post-treatment, respectively. Additionally, complete remission was reported in 11 (52.4%) patients at three-month follow-up, while most of the remaining patients had only mild headaches. Among nine (42.9%) patients who underwent lumbar puncture, none had an abnormally low opening pressure (median 13.8 cm H2O, range 9.2-21.5). CONCLUSION: Given the high responder rates of epidural blood patch in our study, empirical epidural blood patch should be considered to treat new-onset orthostatic headache, even when brain and spinal imaging are negative. The necessity of lumbar puncture is questionable considering the high response rate of epidural blood patch and low rate of "low pressure."

Nigrosome 1 visibility and its association with nigrostriatal dopaminergic loss in Parkinson's disease.

BACKGROUND: Nigrosome 1 (NG1), a small cluster of dopaminergic cells in the substantia nigra and visible in the susceptibility map-weighted magnetic resonance image (SMwI), is severely affected in Parkinson's disease (PD). However, the degree of nigrostriatal degeneration according to the visibility of NG1 has not yet been well elucidated. METHODS: We consecutively recruited 138 PD and 78 non-neurodegenerative disease (non-ND) patients, who underwent both 18 F-FP-CIT positron emission tomography (PET) and SMwI. Three neurologists and one radiologist evaluated the visibility of NG1 in SMwI. The participants were thereby grouped into visible, intermediate, and non-visible groups. Nigrostriatal dopaminergic input was calculated using the specific binding ratio (SBR) of the 18 F-FP-CIT PET. We determined the threshold of regional SBR for discriminating NG1 visibility and the probability for NG1 visibility according to regional SBR. RESULTS: Visual rating of NG1 showed excellent interobserver agreements as well as high sensitivity and specificity to differentiate the PD group from the non-ND group. NG1 was visible in seven patients (5.1%) in the PD group, who had relatively short disease duration or less severe loss of striatal dopamine. The threshold of putaminal SBR reduction on the more affected side for the disappearance of NG1 was 45.5%, and the probability for NG1 visibility dropped to 50% after the reduction of putaminal SBR to 41% from the normal mean. CONCLUSIONS: Almost half loss of nigrostriatal dopaminergic input is required to dissipate the hyperintensity of NG1 on SMwI, suggesting its utility in diagnosing PD only after the onset of the motor symptoms.

Diagnostic performance of a high-spatial-resolution voxelwise analysis of neuromelanin-sensitive imaging in early-stage idiopathic Parkinson's disease.

BACKGROUND: Quantitative assessments of neuromelanin (NM) of the substantia nigra pars compacta (SNpc) in neuromelanin-sensitive MRI (NM-MRI) to determine its abnormality have been conducted by measuring either the volume or contrast ratio (CR) of the SNpc. A recent study determined the regions in the SNpc that are significantly different between early-stage idiopathic Parkinson's disease (IPD) patients and healthy controls (HCs) using a high spatial-resolution NM-MRI template, which enables a template-based voxelwise analysis to overcome the susceptibility of CR measurement to inter-rater discrepancy. We aimed to assess the diagnostic performance, which has not been reported, of the CRs between early-stage IPD patients and HCs using a NM-MRI template. METHODS: We retrospectively enrolled early-stage IPD patients (n = 50) and HCs (n = 50) who underwent 0.8-mm isovoxel NM-MRI and dopamine-transporter PET as the standard of reference. A template-based voxelwise analysis revealed two regions in nigrosomes 1 and 2 (N1 and N2, respectively), with significant differences in each substantia nigra (SNpc) between IPD and HCs. The mean CR values of N1, N2, volume-weighted mean of N1 and N2 (N1 + N2), and whole SNpc on each side were compared between IPD and HC using the independent t-test or the Mann-Whitney U test. The diagnostic performance was compared in each region using receiver operating characteristic curves. RESULTS: The mean CR values in the right N1 (0.149459 vs. 0.194505), left N1 (0.133328 vs. 0.169160), right N2 (0.230245 vs. 0.278181), left N2 (0.235784 vs. 0.314169), right N1 + N2 (0.155322 vs. 0.278143), left N1 + N2 (0.140991 vs. 0.276755), right whole SNpc (0.131397 vs. 0.141422), and left whole SNpc (0.127099 vs. 0.137873) significantly differed between IPD patients and HCs (all p < 0.001). The areas under the curve of the left N1 + N2, right N1 + N2, left N1, right N1, left N2, right N2, left whole SNpc, and right whole SNpc were 0.994 (sensitivity, 98.0%; specificity, 94.0%), 0.985, 0.804, 0.802, 0.777, 0.766, 0.632, and 0.606, respectively. CONCLUSION: Our NM-MRI template-based CR measurements revealed significant differences between early-stage IPD patients and HCs. The CR values of the left N1 + N2 demonstrated the highest diagnostic performance.

Sandwich spatial saturation for neuromelanin-sensitive MRI: Development and multi-center trial.

Neuromelanin (NM)-sensitive MRI using a magnetization transfer (MT)-prepared T1-weighted sequence has been suggested as a tool to visualize NM contents in the brain. In this study, a new NM-sensitive imaging method, sandwichNM, is proposed by utilizing the incidental MT effects of spatial saturation RF pulses in order to generate consistent high-quality NM images using product sequences. The spatial saturation pulses are located both superior and inferior to the imaging volume, increasing MT weighting while avoiding asymmetric MT effects. When the parameters of the spatial saturation were optimized, sandwichNM reported a higher NM contrast ratio than those of conventional NM-sensitive imaging methods with matched parameters for comparability with sandwichNM (SandwichNM: 23.6 ± 5.4%; MT-prepared TSE: 20.6 ± 7.4%; MT-prepared GRE: 17.4 ± 6.0%). In a multi-vendor experiment, the sandwichNM images displayed higher means and lower standard deviations of the NM contrast ratio across subjects in all three vendors (SandwichNM vs. MT-prepared GRE; Vendor A: 28.4 ± 1.5% vs. 24.4 ± 2.8%; Vendor B: 27.2 ± 1.0% vs. 13.3 ± 1.3%; Vendor C: 27.3 ± 0.7% vs. 20.1 ± 0.9%). For each subject, the standard deviations of the NM contrast ratio across the vendors were substantially lower in SandwichNM (SandwichNM vs. MT-prepared GRE; subject 1: 1.5% vs. 8.1%, subject 2: 1.1 % vs. 5.1%, subject 3: 0.9% vs. 4.0%, subject 4: 1.1% vs. 5.3%), demonstrating consistent contrasts across the vendors. The proposed method utilizes product sequences, requiring no alteration of a sequence and, therefore, may have a wide practical utility in exploring the NM imaging.

A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism.

OBJECTIVES: This study aimed to compare susceptibility map-weighted imaging (SMwI) using various MRI machines (three vendors) with N-3-fluoropropyl-2-ß-carbomethoxy-3-ß-(4-iodophe nyl)nortropane (18F-FP-CIT) PET in the diagnosis of neurodegenerative parkinsonism in a multi-centre setting. METHODS: We prospectively recruited 257 subjects, including 157 patients with neurodegenerative parkinsonism, 54 patients with non-neurodegenerative parkinsonism, and 46 healthy subjects from 10 hospitals between November 2019 and October 2020. All participants underwent both SMwI and 18F-FP-CIT PET. SMwI was interpreted by two independent reviewers for the presence or absence of abnormalities in nigrosome 1, and discrepancies were resolved by consensus. 18F-FP-CIT PET was used as the reference standard. Inter-observer agreement was tested using Cohen's kappa coefficient. McNemar's test was used to test the agreement between the interpretations of SMwI and 18F-FP-CIT PET per participant and substantia nigra (SN). RESULTS: The inter-observer agreement was 0.924 and 0.942 per SN and participant, respectively. The diagnostic sensitivity of SMwI was 97.9% and 99.4% per SN and participant, respectively; its specificity was 95.9% and 95.2%, respectively, and its accuracy was 97.1% and 97.7%, respectively. There was no significant difference between the results of SMwI and 18F-FP-CIT PET (p > 0.05, for both SN and participant). CONCLUSIONS: This study demonstrated that the high diagnostic performance of SMwI was maintained in a multi-centre setting with various MRI scanners, suggesting the generalisability of SMwI for determining nigrostriatal degeneration in patients with parkinsonism. KEY POINTS: • Susceptibility map-weighted imaging helps clinicians to predict nigrostriatal degeneration. • The protocol for susceptibility map-weighted imaging can be standardised across MRI vendors. • Susceptibility map-weighted imaging showed diagnostic performance comparable to that of dopamine transporter PET in a multi-centre setting with various MRI scanners.

Early-stage Parkinson's disease: Abnormal nigrosome 1 and 2 revealed by a voxelwise analysis of neuromelanin-sensitive MRI.

Previous pathologic studies evaluated the substantia nigra pars compacta (SNpc) of a limited number of idiopathic Parkinson's disease (IPD) patients with relatively longer disease durations. Therefore, it remains unknown which region of the SNpc is most significantly affected in early-stage IPD. We hypothesized that a voxelwise analysis of thin-section neuromelanin-sensitive MRI (NM-MRI) may help determine the significantly affected regions of the SNpc in early-stage IPD and localize these areas in each nigrosome on high-spatial-resolution susceptibility map-weighted imaging (SMwI). Ninety-six healthy subjects and 50 early-stage IPD patients underwent both a 0.8 × 0.8 × 0.8 mm3 NM-MRI and a 0.5 × 0.5 × 1.0 mm3 multi-echo gradient-recalled echo imaging for SMwI. Both NM-MRI and SMwI templates were created by using image data from the 96 healthy subjects. Permutation-based nonparametric tests were conducted to investigate spatial differences between the two groups in NM-MRI, and the results were displayed on both NM-MRI and SMwI templates. The posterolateral and anteromedial regions of the SNpc in NM-MRI were significantly different between the two groups, corresponding to the nigrosome 1 and nigrosome 2 regions, respectively, on the SMwI template. There were the areas of significant spatial difference in the hypointense SN on SMwI between early-stage IPD patients and healthy subjects. These areas on SMwI were slightly greater than those on NM-MRI, including the areas showing group difference on NM-MRI. Our voxelwise analysis of NM-MRI suggests that two regions (nigrosome 1 and nigrosome 2) of the SNpc are separately affected in early-stage IPD.

Exploring linearity of deep neural network trained QSM: QSMnet.

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test "linearity" of QSMnet for susceptibility. The results show that QSMnet underestimates susceptibility in hemorrhagic lesions, revealing degraded linearity of the network for the untrained susceptibility range. To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility. The newly trained network, which is referred to as QSMnet+, is assessed in computer-simulated lesions with an extended susceptibility range (-1.4 â€‹ppm to +1.4 â€‹ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet+ over QSMnet (root mean square error of QSMnet+: 0.04 â€‹ppm vs. QSMnet: 0.36 â€‹ppm). When applied to patient data, QSMnet+ maps show less noticeable artifacts to those of conventional QSM maps. Moreover, the susceptibility values of QSMnet+ in hemorrhagic lesions are better matched to those of the conventional QSM method than those of QSMnet when analyzed using linear regression (QSMnet+: slope â€‹= â€‹1.05, intercept â€‹= â€‹-0.03, R2 â€‹= â€‹0.93; QSMnet: slope â€‹= â€‹0.68, intercept â€‹= â€‹0.06, R2 â€‹= â€‹0.86), consolidating improved linearity in QSMnet+. This study demonstrates the importance of the trained data range in deep neural network-powered parametric mapping and suggests the data augmentation approach for generalization of network. The new network can be applicable for a wide range of susceptibility quantification.

Multiphase MR Angiography Collateral Map: Functional Outcome after Acute Anterior Circulation Ischemic Stroke.

Background Collateral circulation determines tissue fate and affects treatment result in acute ischemic stroke. A precise method for collateral estimation in an optimal imaging protocol is necessary to make an appropriate treatment decision for acute ischemic stroke. Purpose To verify the value of multiphase collateral imaging data sets (MR angiography collateral map) derived from dynamic contrast material-enhanced MR angiography for predicting functional outcomes after acute ischemic stroke. Materials and Methods This secondary analysis of an ongoing prospective observational study included data from participants with acute ischemic stroke due to occlusion or stenosis of the unilateral internal carotid artery and/or M1 segment of the middle cerebral artery who were evaluated within 8 hours of symptom onset. Data were obtained from March 2016 through August 2018. The collateral grading based on the MR angiography collateral map was estimated by using six-scale MR acute ischemic stroke collateral (MAC) scores. To identify independent predictors of favorable functional outcomes, age, sex, risk factors, baseline National Institutes of Health Stroke Scale (NIHSS) score, baseline diffusion-weighted imaging (DWI) lesion volume, site of steno-occlusion, collateral grade, mode of treatment, and early reperfusion were evaluated with multiple logistic regression analyses. Results One hundred fifty-four participants (mean age ± standard deviation, 69 years ± 13; 99 men) were evaluated. Younger age (odds ratio [OR], 0.45; 95% confidence interval [CI]: 0.29, 0.70; P < .001), lower baseline NIHSS score (OR, 0.85; 95% CI: 0.78, 0.94; P < .001), MAC score of 3 (OR, 27; 95% CI: 4.0, 179; P < .001), MAC score of 4 (OR, 17; 95% CI: 2.1, 134; P = .007), MAC score of 5 (OR, 27; 95% CI: 2.5, 306; P = .007), and successful early reperfusion (OR, 7.5; 95% CI: 2.6, 22; P < .001) were independently associated with favorable functional outcomes in multivariable analysis. There was a linear negative association between collateral perfusion grades and functional outcomes (P < .001). Conclusion An MR angiography collateral map was clinically reliable for collateral estimation in patients with acute ischemic stroke. This map provided patient-specific pacing information for ischemic progression. © RSNA, 2020.

Quantitative susceptibility mapping using deep neural network: QSMnet.

Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.

Differential involvement of nigral subregions in idiopathic parkinson's disease.

In this study, the prevalence of abnormality in putative nigrosome 1 and nigrosome 4 (N1 and N4, respectively) was investigated in early versus late-stage idiopathic Parkinson's disease (IPD) patients. A total of 128 IPD patients (early stage[n = 89]; late stage[n = 39]) and 15 healthy subjects were scanned for high-resolution (0.5 × 0.5 × 1.0 mm3 ) multiecho gradient-recalled echo MRI and dopamine transporter PET imaging. The MRI data were processed for susceptibility map-weighted imaging (SMWI) to improve a contrast-to-noise ratio, and the images were resliced at 0.5 mm to define N1 and N4. When each side of N1 and N4 was assessed separately for the loss of hyperintensity by two independent reviewers, the consensus review results showed that in early-stage IPD (178 substantia nigras [SNs]), the loss of hyperintensity was observed more often in only the N1 region (65.2%) when compared to in both N1 and N4 regions (34.8%). In late-stage IPD (78 SNs), on the other hand, the loss in only the N1 region (25.6%) was less prevalent than in both N1 and N4 (74.4%) (P < 0.0001). Additionally, intact SNs (both in N1 and N4) were observed 17 SNs (9.6%) of the early-stage IPD patients, whereas it was not found in any SNs of the late-stage IPD patients (P = 0.005). Moreover, involvement of both N1 and N4 on both sides was found in 19.1% of the early-stage IPD patients, whereas its incidence was higher (61.5%) in the late-stage IPD patients (P < 0.0001), suggesting that the loss of hyperintensity in IPD progresses from N1 to N4 as the disease advances. Hum Brain Mapp 39:542-553, 2018. © 2017 Wiley Periodicals, Inc.

Rapid whole-brain gray matter imaging using single-slab three-dimensional dual-echo fast spin echo: A feasibility study.

PURPOSE: To achieve rapid, high resolution whole-brain gray matter (GM) imaging by developing a novel, single-slab three-dimensional dual-echo fast-spin-echo pulse sequence and GM-selective reconstruction. METHODS: Unlike conventional GM imaging that uses time-consuming double-inversion-recovery preparation, the proposed pulse sequence was designed to have two split portions along the echo train, in which the first half was dedicated to yield short inversion recovery (IR)-induced white matter suppression and variable-flip-angle-induced two-step GM signal evolution while the second half cerebrospinal fluid-only signals. Multi-step variable-flip-angle schedules and sampling reordering were optimized to yield high GM signals while balancing cerebrospinal fluid signals between ECHOes. GM-selective images were then reconstructed directly from the weighted subtraction between ECHOes by solving a sparse signal recovery problem. In vivo studies were performed to validate the effectiveness of the proposed method over conventional double-inversion-recovery. RESULTS: The proposed method, while achieving one millimeter isotropic, whole-brain GM imaging within 5.5 min, showed superior performance than conventional double-inversion-recovery in producing GM-only images without apparent artifacts and noise. CONCLUSION: We successfully demonstrated the feasibility of the proposed method in achieving whole-brain GM imaging in a clinically acceptable imaging time. The proposed method is expected to be a promising alternative to conventional double-inversion-recovery in clinical applications. Magn Reson Med 78:1691-1699, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Mechanisms of T2 * anisotropy and gradient echo myelin water imaging.

In MRI, structurally aligned molecular or micro-organization (e.g. axonal fibers) can be a source of substantial signal variations that depend on the structural orientation and the applied magnetic field. This signal anisotropy gives us a unique opportunity to explore information that exists at a resolution several orders of magnitude smaller than that of typical MRI. In this review, one of the signal anisotropies, T2 * anisotropy in white matter, and a related imaging method, gradient echo myelin water imaging (GRE-MWI), are explored. The T2 * anisotropy has been attributed to isotropic and anisotropic magnetic susceptibility of myelin and compartmentalized microstructure of white matter fibers (i.e. axonal, myelin, and extracellular space). The susceptibility and microstructure create magnetic frequency shifts that change with the relative orientation of the fiber and the main magnetic field, generating the T2 * anisotropy. The resulting multi-component magnitude decay and nonlinear phase evolution have been utilized for GRE-MWI, assisting in resolving the signal fraction of the multiple compartments in white matter. The GRE-MWI method has been further improved by signal compensation techniques including physiological noise compensation schemes. The T2 * anisotropy and GRE-MWI provide microstructural information on a voxel (e.g. fiber orientation and tissue composition), and may serve as sensitive biomarkers for microstructural changes in the brain. Copyright © 2016 John Wiley & Sons, Ltd.

Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI).

PURPOSE: To enhance the visibility of nigrosome 1 in substantia nigra, which has recently been suggested as an imaging biomarker for Parkinson's disease (PD) at 3T magnetic resonance imaging (MRI). MATERIALS AND METHODS: The substantia nigra structure was visualized at 3T MRI using multiecho susceptibility map-weighted imaging (SMWI) in 15 healthy volunteers and 6 patients with Parkinson's disease (PD). The visibility of nigrosome 1 was further enhanced by acquiring data in an oblique-coronal imaging plane at a high spatial resolution (0.5 × 0.5 × 1.0 mm3 ). To compare the visibility, the contrast-to-noise ratios (CNR) of the nigrosome 1 structure relative to the neighboring substantia nigra structure were evaluated in the SMWI and other conventional susceptibility contrast images (magnitude, frequency, quantitative susceptibility map [QSM] and susceptibility-weighted image). RESULTS: In healthy volunteers, the CNRs of the nigrosome 1 structure were 1.04 ± 0.38, 0.84 ± 0.32, 1.04 ± 0.40, 0.86 ± 0.41, and 1.45 ± 0.48 for magnitude, frequency, quantitative susceptibility map, susceptibility-weighted image, and SMWI, respectively. Compared to conventional susceptibility contrast images, the SMWI method significantly improved the CNR of nigrosome 1 (P = 0.014 for magnitude, P = 0.030 for QSM, and P < 0.001 for frequency and SWI, respectively). The magnetic susceptibility difference between nigrosome 1 and neighboring substantia nigra structures was 0.037 ± 0.016 ppm (measured in QSM, P < 0.001) in healthy volunteers. In the PD patients, the visibility of the nigrosome 1 structures was reduced. CONCLUSION: The SMWI method enhances the visibility of nigrosome 1 structures at 3T MRI when compared to conventional susceptibility contrast images. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:528-536.

The Impact of Discrepancy between Measured versus Stated Weight on Hemorrhagic Transformation and Clinical Outcomes after Intravenous Alteplase Thrombolysis.

BACKGROUND: An accurate measurement of patient weight is important in determining the dosage for intravenous alteplase thrombolysis. In most emergency rooms, however, weight is not measured. We investigated the difference between stated and measured weight and its effect on hemorrhagic transformation and clinical outcomes. METHODS: We enrolled 128 consecutive patients who had hyperacute stroke and were treated by alteplase. Alteplase dose was calculated using the weight provided by patient or guardian/caregiver, and the actual weight was measured after administration. Patients were classified into 2 groups: overused group (stated weight >measured weight) and underused group (measured weight ≥stated weight). The prevalence of hemorrhagic transformation on follow-up, determined by gradient-recalled echo MRI or non-enhanced CT, was compared between the 2 groups. The predictors for hemorrhage with progression, defined as an increase in the National Institutes of Health Stroke Scale (NIHSS) by a value of 4 or more accompanied by hemorrhage, were determined using multivariable logistic regression analysis and included the overused or underused alteplase and baseline clinical and laboratory findings. RESULTS: Sixty-six (51.6%) of 128 patients were in the underused group and 62 patients (48.4%) in the overused group. The median difference between the stated and measured weights was 1.5 (interquartile range 0.56-3.81) kg, with the largest difference being 25.6 kg. Although there were no significant difference in baseline clinical and laboratory findings between the 2 groups, the overused group showed a significantly higher prevalence of hemorrhagic transformation (p = 0.012) and hemorrhage with progression (p = 0.025). The multivariable logistic regression analysis demonstrated that overused alteplase (OR 7.26; 95% CI 1.24-42.45; p = 0.028), baseline glucose (>144 mg/dL; OR 5.03; 95% CI 1.00-25.26; p = 0.050), and initial NIHSS (OR 1.13 per 1-point NIHSS increase; 95% CI 1.00-1.27; p = 0.047) in model 1 that use alteplase overdose as a categorical variable and overused alteplase (OR 1.67 1-mg increase; 95% CI 1.05-2.66; p = 0.027) in model 2 that use an overused alteplase dose as numerical variable were significant predictors for hemorrhage with progression. CONCLUSION: More alteplase usage than actual weight led to higher hemorrhagic transformation. As one of the predictors for clinical deterioration, it is important to administrate alteplase based on an accurately measured weight.

Pediatric intracerebral histiocytic sarcoma with rhabdoid features: Case report and literature review.

A 16-year-old boy presented with marked weight loss, weakness of the left extremities and dizziness of 2 months duration and vomiting for 2 days. Brain MRI showed an approximately 6.5 × 5.3 cm-sized huge heterogeneous enhancing mass located in the corpus callosum, extending into the lateral ventricle. Open biopsy showed that the lesion consisted of lymphoplasmacytes and plump histiocytes with rhabdoid morphology, which were stained with S-100 protein, CD68 (KP1) and negative for CD1a. Histiocytic tumor was initially diagnosed. Chemotherapy using methotrexate, 6-mercaptopurine, vinblastine, interferon-alpha and dexamethasone was performed. After 5 months, partial removal was done. Microscopically, plump and bizarre tumor cells as well as rhabdoid features were found. Occasional spindle cells and necrosis were also found. These cells were positive for CD163, CD68, lysozyme, CD4, INI-1 and BRG1. BRAF V600E mutation was detected. The lesion was finally diagnosed as histiocytic sarcoma. Radiotherapy (6000 cGy in 30 fractions) was done. Both cerebral and extracerebral histiocytic sarcomas have long been diagnosed by unclarified criteria; its rarity as well as previously unclarified criteria can easily lead to a misinterpretation. Histiocytic sarcoma of the CNS is exceptionally rare in children, associated with an exceptionally poor prognosis. To date, only seven cases of pediatric cerebral histiocytic sarcomas have been reported. The present case is the first pediatric case showing BRAF V600E-mutated intracerebral histiocytic sarcoma.

Single Subcortical Infarct: Pathomechanism Assessed by Thin-Section Computed Tomography Perfusion.

INTRODUCTION: The pathomechanism of a single subcortical infarct (SSI) may be better determined by assessing the perfusion status between parent artery and ischemic lesion. We aimed to compare the classifications into branch atheromatous disease (BAD) versus non-BAD based on diffusion-weighted imaging (DWI) or computed tomography perfusion (CTP), and to test whether a CTP-based classification improves the predicting power for progression in SSI (PSSI) compared to that by DWI. METHODS: We enrolled 109 consecutive patients with SSI examined by whole-supratentorial brain CTP and follow-up DWI. Time-to-drain (TTD) maps were calculated from 1-mm dynamic CTP data. BAD was assumed when either the ischemic lesion extended to the basal surface of the parent artery on axial DWI or the hypoperfused area (TTD ≥ 5 seconds) was <5 mm apart from the cerebrospinal fluid perforators interface on both coronal and sagittal CTPs. We tested the relationship between DWI and CTP for determining BAD, and compared demographics, imaging, and the frequency of PSSI between the BAD and non-BAD based on CTP. Multivariable regression analysis was performed to determine predicting factors for PSSI. RESULTS: On DWI, 66 of 109 patients (60.6%) were classified as BAD; on CTP, 32 patients were classified as BAD (29.4%), showing significant difference (P = .047). PSSI was significantly different between BAD versus non-BAD by CTP (40.6% versus 11.7%, P = .002), but not different by DWI (21.2% versus 18.6%, P = .930). BAD-type perfusion was the only independent predictor for PSSI (OR, 5.209; 95% CI, 1.745-15.555; P = .003). CONCLUSION: The classifications of SSI with and without BAD by CTP and DWI are significantly different. CTP may help to predict PSSI.