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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Hongyan Huang1, Junyang Mo1, Zhiguang Ding1

  • 1From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).

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|January 14, 2025
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Summary
This summary is machine-generated.

Deep learning can create simulated contrast-enhanced prostate MRI from noncontrast scans, offering a viable alternative to reduce contrast agent risks. This AI-generated imaging shows high similarity to real scans and aids in accurately assessing prostate cancer risk using PI-RADS scores.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Diagnostic Imaging
  • Oncology and Cancer Diagnostics

Background:

  • Multiparametric MRI (mpMRI) is standard for suspected prostate cancer, but contrast agents pose risks.
  • Concerns exist regarding contrast agent accumulation and potential toxicity in patients.
  • Need for alternative imaging methods to mitigate contrast-related risks.

Purpose of the Study:

  • Evaluate feasibility of generating simulated contrast-enhanced MRI from noncontrast sequences using deep learning.
  • Assess the utility of simulated contrast-enhanced MRI for prostate cancer assessment via PI-RADS v2.1.
  • Explore potential to reduce contrast agent use in prostate MRI.

Main Methods:

  • Retrospective study of 567 male patients with suspected prostate cancer undergoing mpMRI.
  • Deep learning (pix2pix algorithm) trained to synthesize contrast-enhanced MRI from T1w, T2w, DWI, and ADC maps.
  • Radiologists independently scored images using PI-RADS v2.1; agreement assessed with Cohen κ.

Main Results:

  • Simulated and acquired contrast-enhanced images showed high similarity (MS-SSIM: 0.69–0.82).
  • Excellent inter-reader agreement for PI-RADS scores (Cohen κ = 0.96) between simulated and acquired CE-MRI.
  • Addition of simulated CE-MRI upgraded 10.5% of biparametric MRI cases to PI-RADS 4.

Conclusions:

  • Feasible to generate simulated contrast-enhanced prostate MRI using deep learning.
  • Simulated CE-MRI demonstrates high similarity and excellent agreement with acquired CE-MRI for PI-RADS scoring.
  • Deep learning-based simulated CE-MRI is a promising tool for assessing clinically significant prostate cancer.