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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 [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]