3D dynamic diuretic renal scintigraphy using a hybrid whole body CZT SPECT/CT camera protocol in the evaluation of acute ureteric obstruction caused by ureteric stone.

Purpose: To investigate the performance of dynamic 3D diuretic renal scintigraphy using a hybrid whole body CZT SPECT/CT for the evaluation of acute ureteric obstruction in patients with urinary stone disease.

Methods: 20 patients who presented to the Emergency Department with acute renal colic due to urinary stone disease confirmed by means of CT were prospectively included. Three observers evaluated and graded hydronephrosis, hydroureter, perirenal stranding, and thickening of the renal fascia from the CT as well as the renal scintigraphy curves from the dynamic SPECT study.

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission.

Purpose: Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA).

Methods: FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images.