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.

RSL3 Inhibits Porcine Epidemic Diarrhea Virus Replication by Activating Ferroptosis.

Porcine epidemic diarrhea virus (PEDV) is a highly contagious coronavirus that induces diarrhea and death in neonatal piglets, resulting in substantial economic losses to the global swine industry. The mechanisms of PEDV infection and the roles of host factors are still under exploration. In this study, we used the ferroptosis pathway downstream target activator (1S,3R)-RSL3 compound as a starting point, combined with the interactions of N-acetylcysteine and deferoxamine, to elucidate the effects of a series of compounds on PEDV proliferation. We also established glutathione peroxidase 4 (GPX4) gene overexpression to further elucidate the relationship between the ferroptosis pathway and PEDV. (1S,3R)-RSL3 inhibited PEDV replication in Vero cells, while N-acetylcysteine and deferoxamine promoted its proliferation. In addition, (1S,3R)-RSL3 mainly affected the replication stage of PEDV. Overexpression of GPX4 promoted PEDV proliferation, indicating that the ferroptosis pathway could influence PEDV replication in Vero cells. This study focused on the mechanism of (1S,3R)-RSL3 inhibition on PEDV, laying the foundation for exploring the pathogenic mechanisms of PEDV and drug development.

Predicting Left Ventricular Myocardial Fibrosis in Patients with Hypertrophic Cardiomyopathy by Speckle Tracking Automated Functional Imaging.

OBJECTIVE: The study was performed to explore the predictive value of multiple strain parameters for myocardial fibrosis in patients with hypertrophic cardiomyopathy (HCM) by using speckle tracking automated functional imaging (AFI). METHODS: A total of 61 patients diagnosed with HCM were finally enrolled in this study. All patients completed transthoracic echocardiography and cardiac magnetic resonance late gadolinium enhancement (LGE) within 1 month. Twenty age- and sex-matched healthy participants were included as the control group. Multiple parameters, including segmental longitudinal strain (LS), global longitudinal strain (GLS), post-systolic index and peak strain dispersion, were automatically analyzed by AFI. RESULTS: A total of 1458 myocardial segments were analyzed according to the left ventricular 18-segment model. Among the 1098 segments from HCM patients, segments with LGE had a lower absolute value of segmental LS than those without LGE (p < 0.05). The cutoff values of segmental LS for predicting positive LGE in the basal, intermediate and apical regions were -12.5%, -11.5% and -14.5%, respectively. GLS could predict significant myocardial fibrosis (≥2 positive LGE segments) at a cutoff value of -16.5% with a sensitivity of 80.9% and specificity of 76.5%. As an independent predictor of significant myocardial fibrosis, GLS was substantially associated with the severity of myocardial fibrosis and 5 years sudden cardiac death risk score in HCM patients. CONCLUSION: Speckle tracking AFI could efficiently identify left ventricular myocardial fibrosis in patients with HCM by multiple parameters. GLS predicted significant myocardial fibrosis at a cutoff value of -16.5%, which may indicate the adverse clinical outcomes in HCM patients.

Quantitative assessment of right ventricular size and function with multiple parameters from artificial intelligence-based three-dimensional echocardiography: A comparative study with cardiac magnetic resonance.

AIMS: This study aimed to explore the validation and the diagnostic value of multiple right ventricle (RV) volumes and functional parameters derived from a novel artificial intelligence (AI)-based three-dimensional echocardiography (3DE) algorithm compared to cardiac magnetic resonance (CMR). METHODS AND RESULTS: A total of 51 patients with a broad spectrum of clinical diagnoses were finally included in this study. AI-based RV 3DE was performed in a single-beat HeartModel mode within 24 hours after CMR. In the entire population, RV volumes and right ventricular ejection fraction (RVEF) measured by AI-based 3DE showed statistically significant correlations with the corresponding CMR analysis (p < 0.05 for all). However, the Bland-Altman plots indicated that these parameters were slightly underestimated by AI-based 3DE. Based on CMR derived RVEF < 45% as RV dysfunction, end-systolic volume (ESV), end-systolic volume index (ESVi), stroke volume (SV), and RVEF showed great diagnostic performance in identifying RV dysfunction, as well as some non-volumetric parameters, including tricuspid annular systolic excursion (TAPSE), fractional area change (FAC), and free-wall longitudinal strains (LS) (p < 0.05 for all). The cutoff value was 43% for RVEF with a sensitivity of 94% and specificity of 67%. CONCLUSION: AI-based 3DE could provide rapid and accurate quantitation of the RV volumes and function with multiple parameters. Both volumetric and non-volumetric measurements derived from AI-based 3DE contributed to the identification of the RV dysfunction.

SquiggleNet: real-time, direct classification of nanopore signals.

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

MR Virtual Endoscopy of the Fetal Limb Anomalies Using Three-Dimensional Fast Imaging Employing Steady-State Acquisition Sequence.

OBJECTIVE: To retrospectively investigate the feasibility of magnetic resonance virtual endoscopy (MRVE) to visualize the normal limbs and limb deformities Methods: MR sequences included two-dimensional (2D) single fast spin-echo sequence and 2D and 3D steady-state procession fast imaging sequences. MRVE reconstruction was retrospectively performed by 2 radiologists in 32 fetuses in 30 pregnant women. The correlation between the radiologists for the virtual endoscopy threshold of MRVE was determined. Image quality and limb segment visibility were independently rated. Area under the receiver operating characteristics curve (AUC) of 2D MRI and MRVE was calculated. RESULTS: The mean virtual endoscopy threshold required for the visualization of the limb was 991.93 ± 12.13 and 991.83 ± 12.26 for 2 radiologists, respectively. The correlation between the radiologists for virtual endoscopy threshold was excellent (r = 0.933). The weighted kappa statistic was 0.96 for the evaluation of image quality of limb segments, indicating excellent interobserver agreement. Compared to that of 2D MRI alone, a higher AUC of 2D MRI with MRVE was achieved in detection of both upper and lower limb deformities (0.91 vs. 0.69 and 0.83 vs. 0.71, respectively). CONCLUSION: MRVE may display normal and abnormal fetal limb orientation and structures from multiple perspectives and provide incremental information for obstetrics.

Functional diagnosis of placenta accreta by intravoxel incoherent motion model diffusion-weighted imaging.

OBJECTIVES: To investigate the diagnostic value of intravoxel incoherent motion (IVIM) DWI for placenta accreta by comparing diffusion and perfusion characteristics of placentas with accreta lesions (APs) with those of normal placentas (NPs). METHODS: Twenty-five pregnant women with AP and 24 with NP underwent 3-T magnetic resonance examinations with IVIM-DWI. The perfusion percentage (f), pseudo-diffusion coefficient (D*), and diffusion coefficient (D) values were calculated from different ROIs: the entire-plane of the AP (AP-ROI) and NP (NP-ROI) and the implanted (IR-ROI) and non-implanted region (NIR-ROI) of the AP. The AP-ROIs and NP-ROIs were compared using covariance analysis; the IR-ROIs and NIR-ROIs were compared using the Wilcoxon signed-rank test. ROC curves were produced to evaluate the parameters for predicting placenta accreta. RESULTS: The f and D* values for the AP-ROIs ([45.0 ± 7.63]%, [11.64 ± 2.15]mm2/s) were significantly higher than those for the NP-ROIs ([31.85 ± 5.96]%, [9.04 ± 3.13]mm2/s) (both p < 0.05); the IR-ROIs (54.8%, 14.03 mm2/s) were also significantly higher than the NIR-ROIs (37.4%, 11.4 mm2/s) (both p < 0.05). No significant differences were found between the D values of the AP-ROIs and NP-ROIs (p > 0.05) or of the IR-ROIs and NIR-ROIs (p > 0.05). The areas under the curve for f and D* of the ROC curves were 0.93 and 0.79, respectively. CONCLUSIONS: These results suggest that the IVIM parameters f and D* can be used to quantitatively evaluate the higher perfusion of AP when compared with NP. Furthermore, IVIM may be a useful functional diagnostic technique to predict placenta accreta. KEY POINTS: • Intravoxel incoherent motion (IVIM) may be a useful diagnostic technique to quantitatively estimate the perfusion of the placenta. • The perfusion percentage (f) and pseudo-diffusion coefficient (D*) values differed significantly between placentas with accreta lesions and normal placentas. • ROC curves showed that perfusion percentage (f) and pseudo-diffusion coefficient (D*) values could accurately predict placenta accreta.