Combined whole-body dynamic and static PET/CT with low-dose [F]PSMA-1007 in prostate cancer patients.

Aim: In addition to significant improvements in sensitivity and image quality, the recent introduction of long axial field-of-view (LAFOV) PET/CT scanners has enabled dynamic whole-body imaging for the first time. We aim herein to determine an appropriate acquisition time range for static low-dose [F]PSMA-1007 PET imaging and to investigate the whole-body pharmacokinetics of [F]PSMA-1007 by dynamic PET with the LAFOV Biograph Vision Quadra PET/CT in a group of prostate cancer patients.

Methodology: In total, 38 prostate cancer patients were enrolled in the analysis for staging or re-staging purposes.

The signal intensity variation of multiple sclerosis (MS) lesions on magnetic resonance imaging (MRI) as a potential biomarker for patients' disability: A feasibility study.

Introduction: Although many lesion-based MRI biomarkers in multiple sclerosis (MS) patients were investigated, none of the previous studies dealt with the signal intensity variations (SIVs) of MS lesions. In this study, the SIVs of MS lesions on direct myelin imaging and standard clinical sequences as possible MRI biomarkers for disability in MS patients were assessed.

Methods: Twenty seven MS patients were included in this prospective study.

Correction to: Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry.

The original version of this article unfortunately contained a mistake. The given names and family names were interchanged.

Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry.

Background: Spontaneous episodic vertigo syndromes, namely vestibular migraine (VM) and Menière's disease (MD), are difficult to differentiate, even for an experienced clinician. In the presence of complex diagnostic information, automated systems can support human decision making. Recent developments in machine learning might facilitate bedside diagnosis of VM and MD.

Developing and Implementing Diagnostic Prediction Models for Vestibular Diseases in Primary Care.

Diagnosing patients with vertigo and dizziness is a challenge in primary care settings where laboratory examinations are often not available. This study uses data from patients with confirmed diagnoses of vestibular syndromes to develop and validate simple diagnostic prediction models for the primary care physician. We describe the implementation of these models into an application that may assist the practitioners with their clinical decisions.