From Compressed-Sensing to Deep Learning MR: Comparative Biventricular Cardiac Function Analysis in a Patient Cohort.

BACKGROUND: Conventional segmented, retrospectively gated cine (Conv-cine) is challenged in patients with breath-hold difficulties. Compressed sensing (CS) has shown values in cine imaging but generally requires long reconstruction time. Recent artificial intelligence (AI) has demonstrated potential in fast cine imaging. PURPOSE: To compare CS-cine and AI-cine with Conv-cine in quantitative biventricular functions, image quality, and reconstruction time. STUDY TYPE: Prospective human studies. SUBJECTS: 70 patients (age, 39 ± 15 years, 54.3% male). FIELD STRENGTH/SEQUENCE: 3T; balanced steady state free precession gradient echo sequences. ASSESSMENT: Biventricular functional parameters of CS-, AI-, and Conv-cine were measured by two radiologists independently and compared. The scan and reconstruction time were recorded. Subjective scores of image quality were compared by three radiologists. STATISTICAL TESTS: Paired t-test and two related-samples Wilcoxon sign test were used to compare biventricular functional parameters between CS-, AI-, and Conv-cine. Intraclass correlation coefficient (ICC), Bland-Altman analysis, and Kendall's W method were applied to evaluate agreement of biventricular functional parameters and image quality of these three sequences. A P-value <0.05 was considered statistically significant, and standardized mean difference (SMD) < 0. 100 was considered no significant difference. RESULTS: Compared to Conv-cine, no statistically significant differences were identified in CS- and AI-cine function results (all P > 0.05), except for very small differences in left ventricle end-diastole volumes of 2.5 mL (SMD = 0.082) and 4.1 mL (SMD = 0.096), respectively. Bland-Altman scatter plots revealed that biventricular function results were mostly distributed within the 95% confidence interval. All parameters had acceptable to excellent interobserver agreements (ICC: 0.748-0.989). Compared with Conv-cine (84 ± 13 sec), both CS (14 ± 2 sec) and AI (15 ± 2 sec) techniques reduced scan time. Compared with CS-cine (304 ± 17 sec), AI-cine (24 ± 4 sec) reduced reconstruction time. CS-cine demonstrated significantly lower quality scores than Conv-cine, while AI-cine demonstrated similar scores (P = 0.634). CONCLUSION: CS- and AI-cine can achieve whole-heart cardiac cine imaging in a single breath-hold. Both CS- and AI-cine have the potential to supplement the gold standard Conv-cine in studying biventricular functions and benefit patients having difficulties with breath-holds. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Dark blood T2-weighted imaging of the human heart with AI-assisted compressed sensing: a patient cohort study.

Background: Dark blood T2-weighted (DB-T2W) imaging is widely used to evaluate myocardial edema in myocarditis and inflammatory cardiomyopathy. However, this technique is sensitive to arrhythmia, tachycardia, and cardiac and respiratory motion due to the long scan time with multiple breath-holds. The application of artificial intelligence (AI)-assisted compressed sensing (ACS) has facilitated significant progress in accelerating medical imaging. However, the effect of DB-T2W imaging on ACS has not been elucidated. This study aimed to examine the effects of ACS on the image quality of single-shot and multi-shot DB-T2W imaging of edema. Methods: Thirty-three patients were included in this study and received DB-T2W imaging with ACS, including single-shot acquisition (SS-ACS) and multi-shot acquisition (MS-ACS). The resulting images were compared with those of the conventional multi-shot DB-T2W imaging with parallel imaging (MS-PI). Quantitative assessments of the signal-to-noise ratio (SNR), tissue contrast ratio (CR), and contrast-to-noise ratio (CNR) were performed. Three radiologists independently evaluated the overall image quality, blood nulling, free wall of the left ventricle, free wall of the right ventricle, and interventricular septum using a 5-point Likert scale. Results: The total scan time of the DB-T2W imaging with ACS was significantly reduced compared to the conventional parallel imaging [number of heartbeats (SS-ACS:MS-ACS:MS-PI) =19:63:99; P<0.001]. The SNRmyocardium and CNRblood-myocardium of MS-ACS and SS-ACS were higher than those of MS-PI (all P values <0.01). Furthermore, the CRblood-myocardium of SS-ACS was also higher than that of MS-PI (P<0.01). There were significant differences in overall image quality, blood nulling, left ventricle free wall visibility, and septum visibility between the MS-PI, MS-ACS, and SS-ACS protocols (all P values <0.05). Moreover, blood in the heart was better nulled using SS-ACS (P<0.01). Conclusions: The ACS method shortens the scan time of DB-T2W imaging and achieves comparable or even better image quality compared to the PI method. Moreover, DB-T2W imaging using the ACS method can reduce the number of breath-holds to 1 with single-shot acquisition.