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Feasibility of Artificial Intelligence Constrained Compressed SENSE Accelerated 3D Isotropic T1 VISTA Sequence For Vessel Wall MR Imaging: Exploring the Potential of Higher Acceleration Factors Compared to Traditional Compressed SENSE.
Ma, Yue; Wang, Mengmeng; Qiao, Yuting; Wen, Yafei; Zhu, Yi; Jiang, Ke; Lian, Jianxiu; Tong, Dan.
Affiliation
  • Ma Y; Department of Radiology, The First Hospital of Jilin University, Changchun 130000, PR China.
  • Wang M; Department of Radiology, The First Hospital of Jilin University, Changchun 130000, PR China.
  • Qiao Y; Department of Radiology, The First Hospital of Jilin University, Changchun 130000, PR China.
  • Wen Y; Department of Radiology, The First Hospital of Jilin University, Changchun 130000, PR China.
  • Zhu Y; Philips Healthcare, Beijing, China.
  • Jiang K; Philips Healthcare, Beijing, China.
  • Lian J; Philips Healthcare, Beijing, China.
  • Tong D; Department of Radiology, The First Hospital of Jilin University, Changchun 130000, PR China. Electronic address: tongdan@jlu.edu.cn.
Acad Radiol ; 2024 Apr 24.
Article in En | MEDLINE | ID: mdl-38664146
ABSTRACT
RATIONALE AND

OBJECTIVES:

Investigate the feasibility of using deep learning-based accelerated 3D T1-weighted volumetric isotropic turbo spin-echo acquisition (VISTA) for vessel wall magnetic resonance imaging (VW-MRI), compared to traditional Compressed SENSE and optimize acceleration factor (AF) to obtain high-quality clinical images.

METHODS:

40 patients with atherosclerotic plaques in the intracranial or carotid artery were prospectively enrolled in our study from October 1, 2022 to October 31, 2023 underwent high-resolution vessel wall imaging on a 3.0 T MR system using variable Compressed SENSE (CS) AFs and reconstructed by an optimized artificial intelligence constrained Compressed SENSE (CS-AI). Images were reconstructed through both traditional CS and optimized CS-AI. Two radiologists qualitatively assessed the image quality scores of CS and CS-AI across different segments and quantitatively evaluated SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) metrics. Paired t-tests, ANOVA, and Friedman tests analyzed image quality metrics. Written informed consent was obtained from all patients in this study.

RESULTS:

CS-AI groups demonstrated good image quality scores compared to reference scans until AF up to 12 (P < 0.05). The CS-AI 10 protocol provided the best images in the lumen of both normal and lesion sites (P < 0.05). The plaque SNR was significantly higher in CS-AI groups compared to CS groups until the AF increased to 12 (P < 0.05). CS-AI protocols had higher CNR compared to CS with whichever AF on both pre-and post-contrast T1WI (P < 0.05), The CNR was highest in the CS-AI 10 protocol on pre-contrast T1WI and in CS-AI 12 on post-contrast T1WI (P < 0.05).

CONCLUSION:

The study demonstrated the feasibility of using CS-AI technology to diagnose arteriosclerotic vascular disease with 3D T1 VISTA sequences. The image quality and diagnostic efficiency of CS-AI images were comparable or better than traditional CS images. Higher AFs are feasible and have potential for use in VW-MRI. The determination of standardized AFs for clinical scanning protocol is expected to help for empirical evaluation of new imaging technology.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Acad Radiol Journal subject: RADIOLOGIA Year: 2024 Document type: Article Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Acad Radiol Journal subject: RADIOLOGIA Year: 2024 Document type: Article Country of publication: Estados Unidos