Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-09-10 DOI:10.1186/s41747-024-00506-5

Takahiro Ueda, Kaori Yamamoto, Natsuka Yazawa, Ikki Tozawa, Masato Ikedo, Masao Yui, Hiroyuki Nagata, Masahiko Nomura, Yoshiyuki Ozawa, Yoshiharu Ohno

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Abstract

Background

We aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI).

Methods

Fifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey’s test, and qualitative indexes using the Wilcoxon signed-rank test.

Results

SNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.010). CNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.003). OIQ of T1WI and T2WI obtained using CS with DLR were higher than that using CS without DLR or conventional PI (p < 0.001). DCL of T2WI obtained using CS with DLR was higher than that using conventional PI or CS without DLR (p < 0.001).

Conclusion

CS with DLR provided better image quality and shorter examination time than those obtainable with PI for female pelvic 1.5-T MRI.

Relevance statement

CS with DLR can be considered effective for attaining better image quality and shorter examination time for female pelvic MRI at 1.5 T compared with those obtainable with PI.

Key Points

Patients underwent MRI with T1- and T2-weighted sequences using CS and PI.
All CS data was reconstructed with and without DLR.
CS with DLR allowed for examination times significantly shorter than those of PI and provided significantly higher signal- and CNRs, as well as OIQ.

Graphical Abstract

Abstract Image

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压缩传感和深度学习重建在 1.5 T 下用于成年女性盆腔磁共振成像的功效

背景我们旨在确定压缩传感（CS）和深度学习重建（DLR）与传统平行成像（PI）在提高图像质量的同时缩短女性盆腔 1.5 T 磁共振成像（MRI）检查时间方面的能力。方法52 名患有各种盆腔疾病的女性患者连续接受了使用 CS 和 PI 进行 T1 和 T2 加权序列的 MRI 检查。所有 CS 数据都在有 DLR 和无 DLR 的情况下进行了重建。通过感兴趣区测量确定了 T1 加权图像（T1WI）上肌肉的信噪比（SNR）和脂肪组织与髂肌之间的对比度-噪声比（CNR），以及 T2 加权图像（T2WI）上子宫肌层与直肌之间的对比度-噪声比（CNR）。整体图像质量（OIQ）和诊断置信度（DCL）按 5 分制进行评估。结果使用带 DLR 的 CS 所获得的 T1WI 和 T2WI 的信噪比高于使用不带 DLR 的 CS 或传统 PI 所获得的信噪比（p < 0.010）。使用带 DLR 的 CS 获得的 T1WI 和 T2WI 的 CNRs 高于使用不带 DLR 的 CS 或传统 PI 的 CNRs（p < 0.003）。使用带 DLR 的 CS 获得的 T1WI 和 T2WI 的 OIQ 高于使用不带 DLR 的 CS 或传统 PI（p < 0.001）。使用带 DLR 的 CS 获得的 T2WI 的 DCL 高于使用传统 PI 或不带 DLR 的 CS（p < 0.001）。带 DLR 的 CS 可使检查时间明显短于 PI，并提供明显更高的信号和 CNR 以及 OIQ。

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