大浦 大輔

RATIONALE AND OBJECTIVES: Accurate and efficient three-dimensional visualization of cerebral vasculature is essential for clinical evaluation; however, manual vessel extraction from time-of-flight (TOF) magnetic resonance angiography angiography (MRA) is time-consuming and operator-dependent. This study aimed to develop a deep learning-based cerebrovascular segmentation model and an automated vessel extraction method, and to evaluate their accuracy, volumetric reliability, and impact on volume rendering (VR) workflow efficiency. MATERIALS AND METHODS: A 3D U-Net-based vessel segmentation model was trained using TOF-MRA images. Automated vessel extraction was performed by dilating predicted vessel regions by one voxel. Forty-eight intracranial aneurysm cases were analyzed. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), normalized surface Dice (NSD); tolerance = 1 mm), and centerline distance (CLD). Inter-rater reliability was assessed using DSC between independently generated vessel masks in a subset of the dataset. Aneurysm volumes from original and vessel-extracted images were compared using equivalence testing with a 1% margin and two one-sided tests (TOST). VR image creation time was measured by 12 radiological technologists. RESULTS: The DSC between independently generated vessel masks was 0.916. The DSC, recall, and precision of dilated vessel masks were significantly higher than those of non-dilated masks (p < 0.0001). The NSD was 0.982 ± 0.015, and the CLD was 0.196 ± 0.182 mm. Aneurysm volumes showed strong correlation (r = 0.999) with a small mean absolute error (MAE) (0.0915 mm³), and equivalence by TOST (p < 0.001). VR image creation time was significantly reduced (p = 0.0130). CONCLUSION: The proposed method enables accurate, reproducible, and time-efficient generation of cerebral vascular VR images, suggesting its potential utility in clinical practice.

Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10-6 mm2/s to 480 × 10-6 mm2/s with a 20 × 10-6 mm2/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3-4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT.

[Background and Purpose] Clot location and range predict clinical outcomes for acute ischemic stroke (AIS). We developed a new technique for visualizing occlusion clots, namely, the DEpicting blood clot and MRA using Phase contrast angiography with Image Calculation for Thrombectomy (DEPICT) method. The purpose of this study was to assess the clinical usefulness of DEPICT. [Methods] We used DEPICT in 36 AIS patients to obtain MRA and black blood images with 1-min phase contrast angiography (PCA). We created the black blood images by subtracting the MRA from the T1WI using the source image of PCA. We evaluated the motion artifact, detectability of clot, and precision in location and range compared these to that of susceptibility vessel sign in T2*WI and measured contrast ration (CR) of clot between the cistern and brain tissue. Motion artifact was visually evaluated using a 3-point scale. Detectability and precision of the location and range of occlusion clots were assessed by comparison with findings from digital subtraction angiography (DSA). Gwet's AC1 and kappa statistics were used to assess inter-observer agreement. [Results] DEPICT showed significant robustness for motion artifact compared with T2*WI (p = 0.0026, Wilcoxon signed-rank test). DEPICT showed 100% detectability for the clot. Further, DEPICT showed higher Gwet's AC1 and kappa statistic values with DSA than T2*WI. CR demonstrated a positive value. [Conclusions] DEPICT technique based on 1-min PCA offers both MRA and black blood T1W images that can be used to accurately evaluate both location and range of the clot.

[Background] Magnetic resonance angiography (MRA) is one of the most important sequences to estimate a cerebrovascular disease. We often encounter poor image quality due to slow arterial flow related to aging and motion artifact caused by disturbance of consciousness. We focused on phase contrast angiography (PCA) to overcome these difficulties. PCA can reduce scan time drastically by combining transverse acquisition and partial slab setting covering entire brain arteries. However, transverse acquisition in PCA has a large difference in signal intensity between proximal and distal vessels. Therefore, we apply tilted optimized non-saturated excitation (TONE) to improve image quality. [Purpose] The purpose of this study to investigate the usefulness of TONE for PCA. [Method] We estimated the efficacy of TONE in transverse acquisition PCA using measurement of signal intensity in arteries. We compared image quality among 1 min PCA with/without TONE and time-of flight (TOF)-MRA, by visual. [Result] TONE improved the signal inhomogeneity in entire brain arteries. PCA with TONE (5°-9°) demonstrated the highest image quality. [Conclusion] Oblique transverse acquisition PCA with TONE provides superior image quality compared with TOF with similar scan time. TONE improved image quality by the homogenizing signal intensity of vessels from proximal to distal in oblique transvers acquisition PCA. Our MRA can be performed in about 1 min and provides sufficient quality to estimate brain vessels.