Accelerated deep learning-based function assessment in cardiovascular magnetic resonance.

IF 9.7 1区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiologia Medica Pub Date : 2025-05-17 DOI:10.1007/s11547-025-02019-6

Domenico De Santis, Federica Fanelli, Luca Pugliese, Giovanna Grazia Bona, Tiziano Polidori, Curzio Santangeli, Michela Polici, Antonella Del Gaudio, Giuseppe Tremamunno, Marta Zerunian, Andrea Laghi, Damiano Caruso

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引用次数: 0

Abstract

Purpose: To evaluate diagnostic accuracy and image quality of deep learning (DL) cine sequences for LV and RV parameters compared to conventional balanced steady-state free precession (bSSFP) cine sequences in cardiovascular magnetic resonance (CMR).

Material and methods: From January to April 2024, patients with clinically indicated CMR were prospectively included. LV and RV were segmented from short-axis bSSFP and DL cine sequences. LV and RV end-diastolic volume (EDV), end-systolic volume (EDV), stroke volume (SV), ejection fraction, and LV end-diastolic mass were calculated. The acquisition time of both sequences was registered. Results were compared with paired-samples t test or Wilcoxon signed-rank test. Agreement between DL cine and bSSFP was assessed using Bland-Altman plots. Image quality was graded by two readers based on blood-to-myocardium contrast, endocardial edge definition, and motion artifacts, using a 5-point Likert scale (1 = insufficient quality; 5 = excellent quality).

Results: Sixty-two patients were included (mean age: 47 ± 17 years, 41 men). No significant differences between DL cine and bSSFP were found for all LV and RV parameters (P ≥ .176). DL cine was significantly faster (1.35 ± .55 m vs 2.83 ± .79 m; P < .001). The agreement between DL cine and bSSFP was strong, with bias ranging from 45 to 1.75% for LV and from - 0.38 to 2.43% for RV. Among LV parameters, the highest agreement was obtained for ESV and SV, which fell within the acceptable limit of agreement (LOA) in 84% of cases. EDV obtained the highest agreement among RV parameters, falling within the acceptable LOA in 90% of cases. Overall image quality was comparable (median: 5, IQR: 4-5; P = .330), while endocardial edge definition of DL cine (median: 4, IQR: 4-5) was lower than bSSFP (median: 5, IQR: 4-5; P = .002).

Conclusion: DL cine allows fast and accurate quantification of LV and RV parameters and comparable image quality with conventional bSSFP.

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加速基于深度学习的心血管磁共振功能评估。

目的：评价深度学习（DL）影像序列在心血管磁共振（CMR）中对LV和RV参数的诊断准确性和图像质量，并与传统平衡稳态自由进动（bSSFP）影像序列进行比较。材料与方法：前瞻性纳入2024年1 - 4月临床适应症CMR患者。从短轴bSSFP和DL序列中分割LV和RV。计算左、右室舒张末期容积（EDV）、收缩末期容积（EDV）、卒中容积（SV）、射血分数和左室舒张末期质量。对两个序列的采集时间进行了登记。结果采用配对样本t检验或Wilcoxon符号秩检验进行比较。使用Bland-Altman图评估DL - cine和bSSFP之间的一致性。图像质量由两名读者根据血液-心肌对比、心内膜边缘清晰度和运动伪影，使用5点李克特量表(1 =质量不足；5 =优质)。结果：纳入62例患者（平均年龄：47±17岁，男性41例）。DL cine和bSSFP在所有LV和RV参数上均无显著差异（P≥0.176）。DL - cine明显更快(1.35±。55米vs 2.83±。79米;结论：DL影像可以快速准确地定量LV和RV参数，图像质量与常规bSSFP相当。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiologia Medica 医学-核医学

CiteScore

14.10

自引率

7.90%

发文量

133

审稿时长

4-8 weeks

期刊介绍： Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.