A small (< 1 mm in diameter) or absent ipsilateral posterior communicating artery is a risk factor for ischemic cerebral infarction in patients with internal-carotid-artery occlusion.

Multi-parametric MRI (mp-MRI) is considered the best non-invasive imaging modality for diagnosing prostate cancer (PCa). However, mp-MRI for PCa diagnosis is currently limited by the qualitative or semi-quantitative interpretation criteria, leading to inter-reader variability and a suboptimal ability to assess lesion aggressiveness. Convolutional neural networks (CNNs) are a powerful method to automatically learn the discriminative features for various tasks, including cancer detection. We propose a novel multi-class CNN, FocalNet, to jointly detect PCa lesions and predict their aggressiveness using Gleason score (GS). FocalNet characterizes lesion aggressiveness and fully utilizes distinctive knowledge from mp-MRI. We collected a prostate mp-MRI dataset from 417 patients who underwent 3T mp-MRI exams prior to robotic-assisted laparoscopic prostatectomy (RALP). FocalNet is trained and evaluated in this large study cohort with 5-fold cross-validation. In the free-response receiver operating characteristics (FROC) analysis for lesion detection, FocalNet achieved 89.7% and 87.9% sensitivity for index lesions and clinically significant lesions at 1 false positive per patient, respectively. For GS classification, evaluated by the receiver operating characteristics (ROC) analysis, FocalNet received the area under the curve (AUC) of 0.81 and 0.79 for the classifications of clinically significant PCa (GS≥3+4) and PCa with GS≥4+3, respectively. With the comparison to the prospective performance of radiologists using the current diagnostic guideline, FocalNet demonstrated comparable detection sensitivity for index lesions and clinically significant lesions, only 3.4% and 1.5% lower than highly experienced radiologists without statistical significance.

A phase-contrast cine magnetic resonance (MR) imaging technique was used to study normal dynamics of cerebrospinal fluid (CSF) in 10 healthy volunteers and four patients with normal MR images. This pulse sequence yielded 16 quantitative flow-encoded images per cardiac cycle (peripheral gating). Flow encoding depicted craniocaudal flow as high signal intensity and caudo-cranial flow as low signal intensity. Sagittal and axial images of the head, cervical spine, and lumbar spine were obtained, and strategic sites were analyzed for quantitative CSF flow. The onset of CSF systole in the subarachnoid space was synchronous with the onset of systole in the carotid artery. CSF systole and diastole at the foramen of Monro and aqueduct were essentially simultaneous. The systolic and diastolic components were different in the subarachnoid space, where systole occupied approximately 40% and diastole 60% of the cardiac cycle, compared with the ventricular system, where they were equal. This difference results in systole in the intracranial and spinal subarachnoid spaces preceding that in the ventricular system; the same is true for diastole. The fourth ventricle and cisterna magna serve as mixing chambers. The high-velocity flow in the cervical spine and essentially no flow in the distal lumbar sac indicate that a portion of the capacitance necessary in this essentially closed system resides in the distal spinal canal.