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Deep learning-based high-resolution united compressed sensing for gadoxetic acid-enhanced liver magnetic resonance imaging in the detection of colorectal liver metastases

   
@article{QIMS154799,
	author = {Dongqiu Shan and Yuedi Ma and Junhui Yuan and Dechang Yuan and Guangguang An and Chunmiao Xu and Renzhi Zhang and Yue Wu and Xuejun Chen},
	title = {Deep learning-based high-resolution united compressed sensing for gadoxetic acid-enhanced liver magnetic resonance imaging in the detection of colorectal liver metastases},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {16},
	number = {7},
	year = {2026},
	keywords = {},
	abstract = {Background: The hepatobiliary phase (HBP) of gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) is important for detecting colorectal liver metastasis (CRLM), but image quality may be limited. This study evaluated whether deep learning-based reconstruction united compressed sensing (DR-uCS) and deep learning-based reconstruction high-resolution united compressed sensing (DR-HR-uCS) improve image quality and lesion detection in CRLM.Methods: This retrospective study included 86 patients with 116 CRLM lesions (71 lesions ≥1 cm and 45 lesions },
	issn = {2223-4306},	url = {https://qims.amegroups.org/article/view/154799}
}