Editorial for “Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI Using Reduced‐Field‐of‐View Echo‐Planar Imaging and Enhanced Reverse Polarity Gradient Method”

Abstract

Editorial for “Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI Using Reduced-Field-of-View EchoPlanar Imaging and Enhanced Reverse Polarity Gradient Method” In recent years, diffusion magnetic resonance imaging (MRI) has been increasingly recognized as a key tool in breast imaging. The evidence base of its value in lesion detection, characterization, and treatment response prediction is expanding rapidly as new evidence continues to emerge from basic and clinical research, including several prospective multicenter trials. However, its integration into clinical practice is still hampered by inconsistent image quality. The echo-planar imaging (EPI) sequence, which is the most commonly used technique for clinical diffusion MRI, is known to be susceptible to artifacts. This problem is further complicated and amplified in the breast, where the large field-of-view (FOV), off-center geometry, and highly variable anatomy pose unique challenges to MRI. Advanced imaging techniques have been developed to remediate this issue, but their performance in the breast remains largely unknown and awaits validation. In this issue of JMRI, Rodríguez-Soto et al responded to this unmet need with a retrospective assessment of two advanced EPI techniques, reduced field-of-view (rFOV) and reverse polarity gradient (RPG), in a cohort of 170 women receiving diagnostic and surveillance MRI of the breast. Both rFOV and RPG are distortion reduction techniques that mitigate the severe geometric distortion along the phase-encoding (PE) direction of EPI images. The rFOV technique suppresses distortion at the acquisition stage of image formation by reducing the length of the readout echo train, whereas RPG seeks to estimate the distortion field and correct for it in the postprocessing stage. While both techniques have established clinical applications in other organs, reports of their application in breast imaging are rare, inconclusive, and limited to small-scale feasibility demonstrations and niche applications. In this study, Rodríguez-Soto et al conducted a systematic comparison of image distortion in full-FOV and rFOV EPI images with and without RPG correction. High-fidelity anatomical images acquired with a Rapid Acquisition with Relaxation Enhancement (RARE) sequence were used as the reference. Distortion was quantified by the displacement field, which was measured at landmarks in a National Institute of Standards and Technology/University of California San Francisco breast phantom and estimated with the RPG algorithm on in vivo data. Their results illustrated a constructive interaction between the two distortion correction techniques. While both rFOV and RPG substantially reduced the discrepancy between EPI and reference data, maximum distortion reduction was achieved with the combined use of both techniques. The results of this study provided the much-needed evidence of effectiveness for rFOV and RPG in clinical reality. Since both of these techniques are commercially available from major MRI vendors, these results are likely to have an immediate impact on the clinical practice of breast imaging. As the research community continues to make innovations in the field of biomedical imaging, timely evaluation of their performance on clinical data is also crucial for successful translation. In this regard, the work by RodríguezSoto et al constitutes a building block of a path toward reliable, robust, and practical clinical breast diffusion MRI and a welcome addition to the ever-growing body of literature in this field. Moreover, data generated in this study may serve as the foundation and materials for future investigations of other questions of potential clinical impact. Quantitative analysis of these data, for instance, may shed light on the impact of artifact correction (which is in essence a nonlinear transformation of image data) on the accuracy and precision of quantitative imaging markers (eg, ADC). This information may guide our future search of more robust imaging biomarker candidates and facilitate their validation in and adaptation to clinical practice.