Chemical shift MRI sequences are routinely used in body imaging; this article reviews the MRI physics principles, followed by a detailed description of how to leverage both chemical shift and magnetic susceptibility information to make specific diagnoses in the chest and abdomen. For the radiologist performing body MRI, chemical shift imaging is a unique tool available to investigate the cellular composition of tissue. By leveraging the inherent difference in resonant frequency between water and fat protons that is due to their local chemical environment, chemical shift image acquisition can be timed to image water and fat protons when their collective signals are in phase or out of phase, to determine the relative amount of fat signal and water signal within an individual voxel. Loss of signal intensity between the in-phase and opposed-phase MR images indicates intravoxel lipid and can be used to make definitive benign diagnoses, characterize the tissue composition of solid organs, and serve as an adjunct imaging feature aiding in the diagnosis of a variety of benign and malignant processes in the chest and abdomen. Simultaneously, T2* decay can be used to advantage at chemical shift imaging to highlight a decrease in signal intensity or amplification of signal void (blooming art...

The genitourinary region is one of the most common sites of extrapulmonary tuberculosis (TB) involvement. The imaging features of genitourinary TB are protean and can mimic other entities, includin...

Advances in MRI technology have led to the development of low-field-strength (hereafter, "low-field") (0.55 T) MRI systems with lower weight, fewer shielding requirements, and lower cost than those of traditional (1.5-3 T) systems. The trade-offs of lower signal-to-noise ratio (SNR) at 0.55 T are partially offset by patient safety and potential comfort advantages (eg, lower specific absorption rate and a more cost-effective larger bore diameter) and physical advantages (eg, decreased T2* decay, shorter T1 relaxation times). Image reconstruction advances leveraging developing technologies (such as deep learning-based denoising) can be paired with traditional techniques (such as increasing the number of signal averages) to improve SNR. The overall image quality produced by low-field MRI systems, although perhaps somewhat inferior to 1.5-3 T MRI systems in terms of SNR, is nevertheless diagnostic for a broad variety of body imaging applications. Effective low-field body MRI requires (a) an understanding of the trade-offs resulting from lower field strengths, (b) an approach to modifying routine sequences to overcome SNR challenges, and (c) a workflow for carefully selecting appropriate patients. The authors describe the rationale, opportunities, and challenges of low-field body MRI; discuss important considerations for low-field imaging with common body MRI sequences; and delineate a variety of use cases for low-field body MRI. The authors also include lessons learned from their preliminary experience with a new low-field MRI system at a tertiary care center. Finally, they explore the future of low-field MRI, summarizing current limitations and potential future developments that may enhance the clinical adoption of this technology. ^©RSNA, 2023 Supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Venkatesh in this issue.

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. ^© RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.