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

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Abstract
Background: Conventional 123 I-ioflupane dopamine-transporter SPECT requires 25–40 min of acquisition, causing patient discomfort and limiting throughput. This study assessed whether deep-learning (DL) reconstruction can yield diagnostic-quality images from a 5-min scan. Methods: We retrospectively analysed 207 studies (1035 slices) obtained between April 2018 and June 2020. After cropping to 64 × 64 striatal regions, 600, 185 and 250 images from 120, 37 and 50 patients were used for training, validation and testing. Six convolutional architectures—U-Net (one–five depths), V-Net, U-Net + + , R2U-Net, Attention U-Net and TransUNet—were trained to translate 5-min into virtual 25-min images. Image quality was assessed with peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), analysed by Friedman and Dunn–Holm tests. A blinded reader study involved three nuclear medicine physicians grading 50 cases (100 striata) on a four-point scale; agreement with the 25-min consensus was measured by weighted κ and intra-/inter-observer intraclass correlation coefficients (ICC). Results: All DL reconstructions significantly outperformed raw 5-min images in PSNR and SSIM (p < 0.01). The four-layer U-Net achieved the highest quality (PSNR 32.7 ± 1.7 dB, SSIM 0.842 ± 0.069), ≈1.8 dB and 0.13 higher than baseline, and statistically indistinguishable from 25-min images ( p > 0.05). Reader concordance improved from fair with baseline (κ = 0.29–0.41) to substantial with the four-layer U-Net (κ = 0.62–0.70); intra-reader ICC was 0.84–0.93 and inter-reader ICC 0.73–0.75. Conclusions: A compact four-layer U-Net restores diagnostic fidelity to 5-min 123 I-ioflupane SPECT, enabling an 80% reduction in scan time without loss of quantitative metrics or interpretability. DL-accelerated protocols may enhance comfort, reduce motion artefacts and increase throughput, warranting prospective multicentre validation.
Funding Information
  • Japan Society of Physiological Anthropology (20K16705)

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