BACKGROUND AND PURPOSE:We investigated the relationship between tumor blood-flow measurement based on perfusion imaging by arterial spin-labeling (ASL-PI) and histopathologic findings in brain tumors.

In a neurologically healthy cohort, the associations of PVS differ according to their topography. PVS distribution may be useful for the early detection and classification of small vessel disease.

Background Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer’s disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e., more than two types of) diseases, or multi-sequence magnetic resonance imaging (MRI) scans. Results We propose unsupervised medical anomaly detection generative adversarial network (MADGAN), a novel two-step method using GAN-based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi-sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty + 100 $$\ell _1$$ ℓ 1 loss—trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones—reconstructs unseen healthy/abnormal scans; (Diagnosis) Average $$\ell _2$$ ℓ 2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1-weighted (T1) and 135 healthy contrast-enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self-attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI), with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921. Conclusions Similar to physicians’ way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper-intense enhancing lesions, such as (especially late-stage) AD and brain metastases on multi-sequence MRI scans.

BACKGROUND AND PURPOSE:Cerebral hemodynamics abnormality in Alzheimer disease (AD) is not fully understood. Our aim was to determine whether regional hypoperfusion due to AD is associated with abnormalities in regional arterial blood volume (rABV) and regional arterial transit time (rATT) as measured by quantitative arterial spin-labeling (ASL) with multiple-delay time sampling.

Manganese (Mn) accumulation in the brain is detected as symmetrical high signal intensity in the globus pallidi on T1-weighted MR images without an abnormal signal on T2-weighted images. In this review, we present several cases of Mn accumulation in the brain due to acquired or congenital diseases of the abdomen including hepatic cirrhosis with a portosystemic shunt, congenital biliary atresia, primary biliary cirrhosis, congenital intrahepatic portosystemic shunt without liver dysfunction, Rendu-Osler-Weber syndrome with a diffuse intrahepatic portosystemic shunt, and patent ductus venosus. Other causes of Mn accumulation in the brain are Mn overload from total parenteral nutrition and welding-related Mn intoxication.