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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 ​ppm to +1.4 ​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 ​ppm vs. QSMnet: 0.36 ​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 ​= ​1.05, intercept ​= ​−0.03, R2 ​= ​0.93; QSMnet: slope ​= ​0.68, intercept ​= ​0.06, R2 ​= ​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. [Display omitted]

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. [Display omitted] •New QSM reconstruction, QSMnet, is developed using a deep neural network.•QSMnet generates a highly accurate QSM map close to a gold standard (COSMOS) map.•Processing time of QSMnet is only a few seconds, achieving real-time processing.•In patients, QSMnet delivers similar lesion contrasts to conventional QSM.

Background The diagnosis of reversible cerebral vasoconstriction syndrome (RCVS) is challenging due to its varied clinical manifestations and imaging findings. While it typically presents with a sudden, severe thunderclap headache and multifocal constriction of the cerebral arteries, the wide spectrum of radiological presentations may complicate the diagnosis. Main Body This review presents a series of cases that show both typical and atypical presentations of RCVS. Typical cases show the characteristic “string of beads” pattern on angiography, which usually resolves within 3–6 months. However, diagnostic challenges arise when angiography appears normal in the early stages or when imaging artifacts obscure the findings. In addition, the variability in vasoconstriction patterns and the need for a differential diagnosis further complicate the accurate identification. These cases highlight the importance of considering RCVS in patients with recurrent thunderclap headaches, even when the initial imaging is inconclusive. Recognizing these challenges and the variability in presentation, along with the use of high-resolution vessel wall MRI and blood-brain barrier imaging, can improve diagnostic accuracy and improve patient outcomes. Conclusion The diagnosis of RCVS requires careful integration of clinical evaluation and advanced imaging techniques, with particular attention to radiological findings that can guide accurate diagnosis and management. Despite challenges, such as normal early stage angiography and imaging variability, maintaining a high suspicion of RCVS is essential, especially in patients with recurrent thunderclap headaches.

Distinguishing between Parkinson’s disease (PD) and essential tremor (ET) can be challenging sometimes. Although positron emission tomography can confirm PD diagnosis, its application is limited by high cost and exposure to radioactive isotopes. Patients with PD exhibit loss of the dorsal nigral hyperintensity on brain magnetic resonance imaging (MRI). Novel MRI-based approaches, including susceptibility map-weighted imaging (SMwI), allow visualization of the dorsal nigral hyperintensity at an increased resolution. Herein, we investigated the diagnostic accuracy of dorsal nigral hyperintensity evaluation on SMwI for distinguishing tremor-dominant PD from ET. Consecutive patients with tremor who underwent SMwI and were diagnosed with tremor-dominant PD or ET between July 2021 and July 2022 were enrolled. The dorsal nigral hyperintensity loss on SMwI was compared between the PD and ET groups. All 143 patients (100%) with tremor-dominant PD showed unilateral or bilateral dorsal nigral hyperintensity loss. Among 136 patients with ET, 131 (96.3%) exhibited an intact dorsal nigral hyperintensity, while 5 (3.7%) showed unilateral/bilateral dorsal nigral hyperintensity loss. SMwI discriminated between tremor-dominant PD and ET with a sensitivity and specificity of 100% and 96.3%, respectively. 18 F-FP-CIT PET revealed normal findings in 4/5 patients with ET who had false-positive results on SMwI. These results indicate that dorsal nigral hyperintensity loss on SMwI could differentiate between tremor-dominant PD and ET with high accuracy.

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.

We explored effects of (1) training with various sample sizes of multi-site vs. single-site training data, (2) cross-site domain adaptation, and (3) data sources and features on the performance of algorithms segmenting cerebral infarcts on Magnetic Resonance Imaging (MRI). We used 10,820 annotated diffusion-weighted images (DWIs) from 10 university hospitals. Algorithms based on 3D U-net were trained using progressively larger subsamples (ranging from 217 to 8661), while internal testing employed a distinct set of 2159 DWIs. External validation was conducted using three unrelated datasets (n = 2777, 50, and 250). For domain adaptation, we utilized 50 to 1000 subsamples from the 2777-image external target dataset. As the size of the multi-site training data increased from 217 to 1732, the Dice similarity coefficient (DSC) and average Hausdorff distance (AHD) improved from 0.58 to 0.65 and from 16.1 to 3.75 mm, respectively. Further increases in sample size to 4330 and 8661 led to marginal gains in DSC (to 0.68 and 0.70, respectively) and in AHD (to 2.92 and 1.73). Similar outcomes were observed in external testing. Notably, performance was relatively poor for segmenting brainstem or hyperacute (< 3 h) infarcts. Domain adaptation, even with a small subsample (n = 50) of external data, conditioned the algorithm trained with 217 images to perform comparably to an algorithm trained with 8661 images. In conclusion, the use of multi-site data (approximately 2000 DWIs) and domain adaptation significantly enhances the performance and generalizability of deep learning algorithms for infarct segmentation.

To evaluate the effect of deep learning (DL)-based artificial intelligence (AI) software on the diagnostic performance of radiologists with different experience levels in detecting nigrosome 1 (N1) abnormalities on susceptibility map-weighted imaging (SMwI). This retrospective diagnostic case-control study analyzed 139 SMwI scans of 59 patients with Parkinson's disease (PD) and 80 healthy participants. Participants were imaged using 3T MRI, and AI-generated assessments for N1 abnormalities were obtained using an AI model (version 1.0.1.0; Heuron Corporation, Seoul, Korea), which utilized YOLOX-based object detection and SparseInst segmentation models. Four radiologists (two experienced neuroradiologists and two less experienced residents) evaluated N1 abnormalities with and without AI in a crossover study design. Diagnostic performance metrics, inter-reader agreements, and reader responses to AI-generated assessments were evaluated. Use of AI significantly improved diagnostic performance compared with interpretation without it across three readers, with significant increases in specificity (0.86 vs. 0.94, = 0.004; 0.91 vs. 0.97, = 0.024; and 0.90 vs. 0.97, = 0.012). Inter-reader agreement also improved with AI, as Fleiss's kappa increased from 0.73 (95% confidence interval [CI]: 0.61-0.84) to 0.87 (95% CI: 0.76-0.99). The net reclassification index (NRI) demonstrated significant improvement in three of the four readers. When grouped by experience level, less experienced readers showed greater improvement (NRI = 12.8%, 95% CI: 0.067-0.190) than experienced readers (NRI = 0.8%, 95% CI: -0.037-0.051). In the less experienced group, reader-AI disagreement was significantly higher in the PD group than in the normal group (8.1% vs. 3.8%, = 0.029). DL-based AI enhances the diagnostic performance in detecting N1 abnormalities on SMwI, particularly benefiting less experienced radiologists. These findings underscore the potential for improving diagnostic workflows for PD.

•Novel neuromelanin imaging method, SandwichNM, is proposed by using a product sequence with spatial saturation pulses for MT weighting.•SandwichNM provides high-quality and high-contrast neuromelanin images compared to conventional neuromelanin imaging methods.•In the multivendor study, SandwichNM results display consistently high contrast across scanners from different vendors. 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.

To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without. This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes. In total, 194 patients were included, of whom 105 and 103 had positive results on F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes ( > 0.05). There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

This study aimed to develop a supervised deep learning (DL) model for grading collateral status from dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) images from patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) and compare its performance against experts’ manual grading. Among consecutive LVO-AIS at three medical center sites, DSC-MRP data were processed to generate collateral flow maps consisting of arterial, capillary, and venous phases. With the use of expert readings as a reference, a DL model was developed to analyze collateral status with output classified into good and poor grades. The resulting model was externally validated in a later-collected population from one medical center site. The model was trained on 255 patients and externally validated on 72 patients. In the all-site internal validation population, DL grading of good collateral probability yielded a c statistic of 0.91; in the external validation population, the c statistic was 0.85. In the external validation population, there was moderate agreement between the experts’ grades and DL grades (kappa = 0.53, 95% CI = 0.32–0.73, p  < 0.0001). Day 7 infarct growth volume was higher in DL-graded poor collateral group than good collateral group patients (median volume [26 mL vs. 6 mL], p  = 0.01) in patients with successful reperfusion (modified treatment in cerebral infarction (mTICI) = 2b–3). In all patients with a 90-day modified Rankin Scale (mRS) score, there was a shift to more favorable outcomes in the good collateral group, with a common odds ratio of 2.99 (95% CI = 1.89–4.76, p  < 0.0001). The DL-based collateral grading was in good agreement with expert manual grading in both development and validation populations. After exclusion of patients with large infarct volume, early reperfusion is more likely to benefit patients with the poor collateral flow, and the DL method has the potential to aid the assessment of collateral status.