ABSTRACT
Objectives: The aim of this retrospective study was to explore the diagnostic potential of various cardiac parameters in differentiating between heart failure with preserved ejection fraction (HFpEF) and heart failure with mid-ranged and reduced ejection fraction (HFm + rEF), and to discern their relationship with normal cardiac function. Methods: This research encompassed a comparative analysis of heart failure subtypes based on multiple indicators. Participants were categorized into HFm + rEF, HFpEF, and control groups. For each participant, we investigated indicators of left ventricular function (LVEDVi, LVESVi, and LVEF) and myocardial strain parameters (GLS, GCS, GRS). Additionally, quantitative tissue evaluation parameters including native T1, enhanced T1, and extracellular volume (ECV) were examined.For comprehensive diagnostic performance analysis, receiver operating characteristic (ROC) curve evaluations for each parameters were conducted. Results: HFm + rEF patients exhibited elevated LVEDVi and LVESVi and decreased LVEF compared to both HFpEF and control groups. Myocardial strain revealed significant reductions in GLS, GCS, and GRS for HFm + rEF patients compared to the other groups. HFpEF patients showed strain reductions relative to the control group. In cardiac magnetic resonance imaging (CMR) evaluations, HFm + rEF patients demonstrated heightened native T1 times and ECV fractions. Native T1 was particularly effective in distinguishing HFpEF from healthy subjects. Conclusion: Native T1, ECV, and myocardial strain parameters have substantial diagnostic value in identifying HFpEF. Among them, native T1 displayed superior diagnostic efficiency relative to ECV, offering critical insights into early-stage HFpEF. These findings can play a pivotal role in refining clinical management and treatment strategies for heart failure patients.
ABSTRACT
OBJECTIVES: To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V). METHODS: This was a prospective, single-institutional study using the excretory phase CTU images for analysis. Patients were assigned to the LD-DLIR group (100kV and automatic mA modulation for noise index (NI) of 23) and C-ASIR-V group (100kV and NI of 10) according to the scan protocols in the excretory phase. Two radiologists independently assessed the overall image quality, artifacts, noise and sharpness of urinary tracts. Additionally, the mean CT attenuation, signal-to-noise ratio (SNR) and contrast-to-noise (CNR) in the urinary tracts were evaluated. RESULTS: 26 patients each were included in the LD-DLIR group (10 males and 16 females; mean age: 57.23 years, range: 33-76 years) and C-ASIR-V group (14 males and 12 females; mean age: 60 years, range: 33-77 years). LD-DLIR group used a significantly lower effective radiation dose compared with the C-ASIR-V group (2.01 ± 0.44 mSv vs 6.9 ± 1.46 mSv, p < 0.001). LD-DLIR group showed good overall image quality with average score >4 and was similar to that of the C-ASIR-V group. Both groups had adequate and similar attenuation value, SNR and CNR in most segments of urinary tracts. CONCLUSION: It is feasibility to provide comparable image quality while reducing 71% radiation dose in low-dose CTU with a deep learning image reconstruction algorithm compared to the conventional CTU with ASIR-V. ADVANCES IN KNOWLEDGE: (1) CT urography with deep learning reconstruction algorithm can reduce the radiation dose by 71% while still maintaining image quality.
