ArticleQuantitative imaging in medicine and surgery2026
Deep learning-based high-resolution united compressed sensing for gadoxetic acid-enhanced liver magnetic resonance imaging in the detection of colorectal liver metastases.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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 <1 cm) who underwent 3.0-T gadoxetic acid-enhanced liver MRI. A standard-resolution HBP acquisition was reconstructed into conventional united compressed sensing (uCS) and DR-uCS from the same raw k-space data, while a separate high-resolution acquisition generated DR-HR-uCS images. Two radiologists independently assessed subjective image quality, artifact severity, liver edge/vessel clarity, and lesion conspicuity. Quantitative metrics [liver signal-to-noise ratio (SNR), lesion SNR, and contrast-to-noise ratio (CNR)] were measured by standardized region-of-interest analysis. Diagnostic performance for lesions ≥1 and <1 cm was evaluated using pathology or multidisciplinary consensus. Diagnostic time was recorded across three reader experience levels. Results: Both DR-uCS and DR-HR-uCS significantly improved overall image quality compared with uCS (median score: 5 Conclusions: DR-uCS improves HBP image quality, while DR-HR-uCS further enhances the detection efficiency and conspicuity of sub-centimeter CRLMs. Its advantage likely reflects the combined effects of high-resolution acquisition and deep learning-based reconstruction.
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