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Objectives <p>To determine whether AI-reconstructed prostate MRI at reduced acquisition times maintains prostate cancer (PCa) detection performance comparable to conventional scans.</p> Materials and methods <p>This multicenter, retrospective, consecutive-cohort study included 120 multi-coil T2-weighted prostate MRI scans from the University Medical Center Groningen (UMCG) and 312 publicly available scans from New York University (NYU). An AI model trained on the NYU data was tested on retrospectively undersampled UMCG scans at acceleration factors R = 3 and R = 6 (i.e., data reduction in k-space). Eight experienced radiologists participated in a multi-reader multi-case PCa detection study. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC). Histopathology and PI-RADS ≤ 2 findings served as reference standards. Multiple image quality metrics were subjectively evaluated using a 4-point Likert scale.</p> Results <p>No statistically significant reduction in PCa detection was observed at an MRI acceleration up to R = 6 (<i>p</i> = 0.08). AUROC values were 0.86 (95% CI: 0.74–0.90) for R = 1, 0.82 (0.72–0.88) for R = 3, and 0.80 (0.70–0.86) for R = 6. Compared to R = 1, R = 3 scans were rated by radiologists to have significantly improved sharpness (+0.2, <i>p</i> &lt; 0.05) and lower noise (+0.1, <i>p</i> &lt; 0.05). Overall visual quality at R = 6 remained comparable to R = 1 (2.81 at R6 vs. 2.74 at R1).</p> Conclusion <p>AI-driven reconstruction enabled a sixfold acceleration of T2-weighted prostate MRI (0:33–1:27 min) without a statistically significant reduction in PCa detection, while preserving perceived image quality. However, the decreasing diagnostic performance at higher accelerations warrants further prospective evaluation.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>This study investigated whether deep learning reconstruction enables three- to sixfold acceleration without reducing radiologists’ detection of clinically significant prostate cancer</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>In a multi-reader multi-case study with eight radiologists, three- and sixfold acceleration showed no significant change in area under the receiver operating characteristic curve</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Deep learning reconstruction shortened T2-weighted acquisition times at sixfold acceleration while preserving perceived image quality and diagnostic performance across acceleration factors</i>.</p> Graphical Abstract <p></p>

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Diagnostic assessment of artificial intelligence reconstruction on accelerated prostate MRI: a retrospective, paired, multi-reader multi-case study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月2日

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  • Quintin van Lohuizen,
  • Stefan Johannes Fransen,
  • George Yiasemis,
  • Jasper Jonathan Twilt,
  • Christian Roest,
  • Yuki Arita,
  • Jacob Borstlap,
  • Jurgen J. Fütterer,
  • Maarten de Rooij,
  • Dennis B. Rouw,
  • Ivo G. Schoots,
  • Baris Turkbey,
  • Samuel Joseph Withey,
  • Frank F. J. Simonis,
  • Henkjan Huisman,
  • Thomas Christian Kwee,
  • Jonas Teuwen,
  • Derya Yakar

摘要

Objectives

To determine whether AI-reconstructed prostate MRI at reduced acquisition times maintains prostate cancer (PCa) detection performance comparable to conventional scans.

Materials and methods

This multicenter, retrospective, consecutive-cohort study included 120 multi-coil T2-weighted prostate MRI scans from the University Medical Center Groningen (UMCG) and 312 publicly available scans from New York University (NYU). An AI model trained on the NYU data was tested on retrospectively undersampled UMCG scans at acceleration factors R = 3 and R = 6 (i.e., data reduction in k-space). Eight experienced radiologists participated in a multi-reader multi-case PCa detection study. Diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUROC). Histopathology and PI-RADS ≤ 2 findings served as reference standards. Multiple image quality metrics were subjectively evaluated using a 4-point Likert scale.

Results

No statistically significant reduction in PCa detection was observed at an MRI acceleration up to R = 6 (p = 0.08). AUROC values were 0.86 (95% CI: 0.74–0.90) for R = 1, 0.82 (0.72–0.88) for R = 3, and 0.80 (0.70–0.86) for R = 6. Compared to R = 1, R = 3 scans were rated by radiologists to have significantly improved sharpness (+0.2, p < 0.05) and lower noise (+0.1, p < 0.05). Overall visual quality at R = 6 remained comparable to R = 1 (2.81 at R6 vs. 2.74 at R1).

Conclusion

AI-driven reconstruction enabled a sixfold acceleration of T2-weighted prostate MRI (0:33–1:27 min) without a statistically significant reduction in PCa detection, while preserving perceived image quality. However, the decreasing diagnostic performance at higher accelerations warrants further prospective evaluation.

Key Points

Question This study investigated whether deep learning reconstruction enables three- to sixfold acceleration without reducing radiologists’ detection of clinically significant prostate cancer.

Findings In a multi-reader multi-case study with eight radiologists, three- and sixfold acceleration showed no significant change in area under the receiver operating characteristic curve.

Clinical relevance Deep learning reconstruction shortened T2-weighted acquisition times at sixfold acceleration while preserving perceived image quality and diagnostic performance across acceleration factors.

Graphical Abstract