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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based

Seung Ha Cha1, Yeo Eun Han1, Na Yeon Han1

  • 1Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

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Summary
This summary is machine-generated.

Deep learning multiplexed sensitivity-encoding diffusion-weighted imaging (DL MUSE-DWI) significantly improves bladder MRI image quality. This advanced technique enhances lesion visualization and quantitative metrics, offering a promising tool for clinical diagnosis.

Keywords:
bladder MRIdeep learning reconstructiondiffusion-weighted imaging (DWI)image quality

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Area of Science:

  • Radiology and Imaging Science
  • Medical Artificial Intelligence
  • Oncology Imaging

Background:

  • Bladder MRI is crucial for visualizing tumors, but image quality can be a limitation.
  • Conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) is used, but further enhancements are desirable.
  • Deep learning (DL) algorithms show potential for improving MRI image reconstruction.

Purpose of the Study:

  • To compare the image quality of conventional MUSE-DWI with DL-reconstructed MUSE-DWI for bladder MRI.
  • To evaluate the impact of DL reconstruction on qualitative and quantitative imaging metrics.
  • To assess the diagnostic potential of DL MUSE-DWI in patients with bladder masses.

Main Methods:

  • Retrospective analysis of 57 patients with visible bladder masses undergoing bladder MRI.
  • Images were reconstructed using both conventional MUSE-DWI and a vendor-provided DL algorithm (AIR Recon DL).
  • Qualitative assessment (sharpness, conspicuity, artifacts) and quantitative analysis (SNR, CNR, SIR, ADC) were performed by two radiologists.

Main Results:

  • DL MUSE-DWI demonstrated significantly improved qualitative image quality, including superior sharpness and lesion conspicuity.
  • Quantitative analysis showed higher SNR, CNR, and SIR in DL MUSE-DWI compared to conventional MUSE-DWI.
  • Apparent diffusion coefficient (ADC) values were significantly higher with DL MUSE-DWI, with excellent interobserver agreement for SIR and ADC.

Conclusions:

  • DL MUSE-DWI significantly enhances image quality for bladder MRI, improving lesion visualization and quantitative accuracy.
  • The technique shows high potential for improving diagnostic confidence and clinical utility in bladder cancer imaging.
  • DL MUSE-DWI represents a promising advancement for routine clinical application in bladder MRI.