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Accelerating imaging: deep learning for enhanced 123I-ioflupane SPECT efficiency.

Yoshinobu Ishiwata1,2, Keiichi Horie3, Kazuhiro Aritome4

  • 1Department of Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-Ward, Yokohama, 2360004, Japan. ishi_y@yokohama-cu.ac.jp.

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Summary
This summary is machine-generated.

Deep learning reconstruction enables diagnostic-quality 5-minute 123I-ioflupane SPECT scans, reducing acquisition time by 80%. This deep learning (DL) approach maintains quantitative accuracy and interpretability, improving patient comfort and throughput.

Keywords:
Deep learningDopamine transporter imagingParkinsonismScan time reductionSingle-photon emission computed tomography (SPECT)

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Area of Science:

  • Nuclear Medicine
  • Artificial Intelligence in Medical Imaging
  • Radiopharmaceutical Imaging

Background:

  • Conventional 123I-ioflupane dopamine-transporter SPECT scans require 25-40 minutes, leading to patient discomfort and limited throughput.
  • Deep learning (DL) reconstruction offers a potential solution to reduce scan times while maintaining image quality.

Purpose of the Study:

  • To assess the feasibility of using DL reconstruction to generate diagnostic-quality 123I-ioflupane SPECT images from significantly reduced 5-minute acquisition times.
  • To compare the image quality and diagnostic performance of DL-reconstructed 5-minute scans with conventional 25-minute scans.

Main Methods:

  • Retrospective analysis of 207 123I-ioflupane SPECT studies.
  • Training and validation of six convolutional neural network architectures (U-Net variants, V-Net, Attention U-Net, TransUNet) to translate 5-minute scans into virtual 25-minute scans.
  • Quantitative image quality assessment using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
  • Blinded reader study with three nuclear medicine physicians to evaluate diagnostic performance and inter-observer agreement.

Main Results:

  • All DL reconstructions significantly improved image quality (PSNR, SSIM) compared to raw 5-minute scans (p < 0.01).
  • A compact four-layer U-Net achieved the highest image quality, statistically indistinguishable from 25-minute scans (p > 0.05).
  • Reader concordance improved from fair (κ = 0.29-0.41) to substantial (κ = 0.62-0.70) with DL reconstruction, with high intra- and inter-observer reliability (ICC).

Conclusions:

  • A four-layer U-Net deep learning model can restore diagnostic fidelity to 5-minute 123I-ioflupane SPECT scans.
  • This DL-accelerated protocol enables an 80% reduction in scan time without compromising quantitative metrics or diagnostic interpretability.
  • DL-accelerated SPECT protocols have the potential to enhance patient comfort, reduce motion artifacts, and increase imaging throughput, warranting further prospective validation.