Fully automatic quantification of transient severe respiratory motion artifact of gadoxetate disodium–enhanced MRI during arterial phase

Abstract

Purpose It is important to fully automate the evaluation of gadoxetate disodium–enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium–enhanced MR imaging. Methods The proposed method was based on the construction of quality‐aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality‐aware features, the method calculated quantitative quality scores of gadoxetate disodium–enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5‐point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts). Results Pearson's linear correlation coefficient (PLCC) and Spearman's rank–ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies Conclusions This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.

Using diffusion model to generate high-resolution MRI

“…This imaging technique allows for safe, radiation-free assessment of lesion progression and treatment without invasion [2,3]. However, the lengthy scanning process can introduce spatial motion and crosstalk artifacts in two-dimensional (2D) scans [4][5][6]. To address this, we have integrated an advanced diffusion probabilistic deep learning (DL) model to stably synthesize high-resolution MRI from low-resolution (LR-MRI) scans.…”

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

“…However, high-resolution MRI scans require long image acquisition time to overcome the inherited low signal-to-noise ratio, especially when a large field of view (FOV) is used in body imaging (Plenge et al 2012 , Shi et al 2015 ). Extending scan time is not only prohibitive when dealing with patients who cannot tolerate the long scan but also introduces more motion artifacts compromising the image quality (Pang et al 2016 , Jang et al 2022 , Chen et al 2023 ). More importantly, due to constraints from the fundamental physics applied in MRI data acquisition scheme and sequences to hardware limitations and safety concerns associated with RF power deposition to patients, resolution of clinical MRI sequences can be limited.…”