Prostate MRI has become integral to prostate cancer diagnosis and management, extending beyond prebiopsy triage to biopsy guidance, local staging, active surveillance, focal therapy planning, posttreatment surveillance, and evaluation of biochemical recurrence. As MRI use expands, image quality has emerged as a critical determinant of diagnostic reliability. Suboptimal prostate MRI quality can reduce cancer detection, increase equivocal PI-RADS assessments, compromise staging and longitudinal assessment, and undermine referring clinician confidence in MRI-directed care. The Prostate Imaging Quality System, or PI-QUAL, provides a standardized framework for assessing the diagnostic adequacy of prostate MRI examinations. Initially developed as a research quality-control tool using data from the multicenter PRECISION trial, PI-QUAL has been refined by the European Society of Urogenital Radiology into a more practical version applicable to both multiparametric and biparametric MRI examinations. In this Special Series Review, we describe the rationale for PI-QUAL, summarize evidence linking image quality with prostate MRI performance and patient management, and propose a stepwise approach for clinical implementation, including multidisciplinary training, retrospective audits, protocol optimization, structured reporting, quality dashboards, benchmark development, and artificial intelligence tools. Incorporating PI-QUAL into routine practice can make prostate MRI quality easier to measure, communicate, and improve.
MR vessel wall imaging is an advanced technique for evaluating intracranial arterial pathology by directly depicting abnormalities of the vessel wall, including abnormalities that may precede, accompany, or occur without luminal findings on routine angiographic imaging. Despite increasing clinical interest and a growing evidence base, intracranial MR vessel wall imaging remains inconsistently implemented and underutilized in routine practice. In this AJR Expert Panel Narrative Review, we evaluate the barriers that limit implementation and effective utilization of VWI in the clinical environment for evaluation of intracranial arterial pathology, so that these can be addressed with future efforts. The discussed barriers to broader adoption span four major domains: technical implementation, education and expertise, clinical workflow and communication, and evidence generation. Addressing these barriers will require coordinated efforts among radiologists, technologists, referring clinicians, MRI physicists, and vendors. The goal is not simply greater use of intracranial MR vessel wall imaging, but appropriate utilization. Broader adoption will require moving this from a technique primarily found at specialized centers to a reproducible, widely available clinical service through standardized acquisition, coordinated education, workflow integration, and multicenter evidence demonstrating management impact and patient benefit.
Anti-amyloid monoclonal antibody therapies (AATs) are increasingly used for the treatment of early symptomatic Alzheimer disease. Safe implementation of AATs relies on accurate assessment for exclusionary findings on baseline brain MRI and reliable longitudinal detection of amyloid-related imaging abnormalities (ARIA), including ARIA-H (hemosiderin or hemorrhage) and ARIA-E (edema or effusion). In routine clinical practice, MRI findings encountered during baseline screening and ARIA surveillance sometimes fall into borderline or ambiguous categories that do not clearly conform to trial-defined criteria. For example, subtle or artifactual FLAIR signal abnormality, equivocal microhemorrhage counts, and overlapping features of ARIA-E and ARIA-H can create uncertainty with direct implications for treatment management. Given limited practical guidance addressing these and other gray zones, this imaging-focused review synthesizes common interpretive challenges encountered in a high-volume AAT program and offers practical management-oriented recommendations. Key issues addressed include recognizing technical factors that affect interpretation of susceptibility-weighted and FLAIR images, managing variability in lesion detection across serial examinations, and accurately reporting the temporal evolution of ARIA-related findings. As AAT use expands, recognition of the presented pitfalls can improve diagnostic confidence and help avoid misclassification of findings that may influence therapeutic decisions.
