基于深度学习的压缩感知在疑似冠心病患者非对比增强冠状动脉磁共振血管造影中的加速应用。

Deep Learning-Based Acceleration of Compressed Sensing for Noncontrast-Enhanced Coronary Magnetic Resonance Angiography in Patients With Suspected Coronary Artery Disease.

机构信息

Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.

Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.

出版信息

J Magn Reson Imaging. 2023 Nov;58(5):1521-1530. doi: 10.1002/jmri.28653. Epub 2023 Feb 27.

DOI:10.1002/jmri.28653

PMID:36847756

Abstract

BACKGROUND

The clinical application of coronary MR angiography (MRA) remains limited due to its long acquisition time and often unsatisfactory image quality. A compressed sensing artificial intelligence (CSAI) framework was recently introduced to overcome these limitations, but its feasibility in coronary MRA is unknown.

PURPOSE

To evaluate the diagnostic performance of noncontrast-enhanced coronary MRA with CSAI in patients with suspected coronary artery disease (CAD).

STUDY TYPE

Prospective observational study.

POPULATION

A total of 64 consecutive patients (mean age ± standard deviation [SD]: 59 ± 10 years, 48.4% females) with suspected CAD.

FIELD STRENGTH/SEQUENCE: A 3.0-T, balanced steady-state free precession sequence.

ASSESSMENT

Three observers evaluated the image quality for 15 coronary segments of the right and left coronary arteries using a 5-point scoring system (1 = not visible; 5 = excellent). Image scores ≥3 were considered diagnostic. Furthermore, the detection of CAD with ≥50% stenosis was evaluated in comparison to reference standard coronary computed tomography angiography (CTA). Mean acquisition times for CSAI-based coronary MRA were measured.

STATISTICAL TESTS

For each patient, vessel and segment, sensitivity, specificity, and diagnostic accuracy of CSAI-based coronary MRA for detecting CAD with ≥50% stenosis according to coronary CTA were calculated. Intraclass correlation coefficients (ICCs) were used to assess the interobserver agreement.

RESULTS

The mean MR acquisition time ± SD was 8.1 ± 2.4 minutes. Twenty-five (39.1%) patients had CAD with ≥50% stenosis on coronary CTA and 29 (45.3%) patients on MRA. A total of 885 segments on the CTA images and 818/885 (92.4%) coronary MRA segments were diagnostic (image score ≥3). The sensitivity, specificity, and diagnostic accuracy were as follows: per patient (92.0%, 84.6%, and 87.5%), per vessel (82.9%, 93.4%, and 91.1%), and per segment (77.6%, 98.2%, and 96.6%), respectively. The ICCs for image quality and stenosis assessment were 0.76-0.99 and 0.66-1.00, respectively.

DATA CONCLUSION

The image quality and diagnostic performance of coronary MRA with CSAI may show good results in comparison to coronary CTA in patients with suspected CAD.

EVIDENCE LEVEL

TECHNICAL EFFICACY

摘要

背景

由于采集时间长且图像质量往往不尽如人意，冠状动脉磁共振血管造影（MRA）的临床应用仍然受到限制。最近引入了一种压缩感知人工智能（CSAI）框架来克服这些限制，但它在冠状动脉 MRA 中的可行性尚不清楚。

目的

评估非对比增强冠状动脉 MRA 联合 CSAI 在疑似冠状动脉疾病（CAD）患者中的诊断性能。

研究类型

前瞻性观察研究。

人群

共纳入 64 例连续疑似 CAD 患者（平均年龄±标准差[SD]：59±10 岁，48.4%为女性）。

磁场强度/序列：3.0T 平衡稳态自由进动序列。

评估

三位观察者使用 5 分制（1=不可见；5=极好）对右冠状动脉和左冠状动脉的 15 个冠状动脉节段的图像质量进行评分。图像评分≥3 被认为具有诊断意义。此外，还将冠状动脉计算机断层血管造影（CTA）作为参考标准评估≥50%狭窄的 CAD 的检出情况。测量 CSAI 基于的冠状动脉 MRA 的平均采集时间。

统计学检验

对于每位患者、血管和节段，根据冠状动脉 CTA 计算 CSAI 基于的冠状动脉 MRA 检测≥50%狭窄的 CAD 的敏感性、特异性和诊断准确性。采用组内相关系数（ICC）评估观察者间的一致性。

结果

MR 采集时间的平均值±标准差为 8.1±2.4 分钟。25 例（39.1%）患者在冠状动脉 CTA 上存在≥50%狭窄，29 例（45.3%）患者在 MRA 上存在≥50%狭窄。CTA 图像上共有 885 个节段，818/885（92.4%）个冠状动脉 MRA 节段具有诊断意义（图像评分≥3）。患者的敏感性、特异性和诊断准确性分别为 92.0%、84.6%和 87.5%，血管为 82.9%、93.4%和 91.1%，节段为 77.6%、98.2%和 96.6%。图像质量和狭窄评估的 ICC 分别为 0.76-0.99 和 0.66-1.00。