記述言語：英語   掲載種別：研究論文（学術雑誌）  

<jats:title>Abstract</jats:title>
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<jats:title>Background</jats:title>
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Conventional
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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.
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<jats:title>Methods</jats:title>
<jats:p>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).</jats:p>
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<jats:title>Results</jats:title>
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All DL reconstructions significantly outperformed raw 5-min images in PSNR and SSIM (p &lt; 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 (
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 &gt; 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.
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<jats:title>Conclusions</jats:title>
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A compact four-layer U-Net restores diagnostic fidelity to 5-min
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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.
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DOI： 10.1007/s11604-025-01933-z

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.rmed.2025.108488

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

PURPOSE: We aimed to identify computed tomography (CT) radiomics features that are associated with cellular infiltration and construct CT radiomics models predictive of cellular infiltration in patients with fibrotic ILD. MATERIALS AND METHODS: CT images of patients with ILD who underwent surgical lung biopsy (SLB) were analyzed. Radiomics features were extracted using artificial intelligence-based software and PyRadiomics. We constructed a model predicting cell counts in histological specimens, and another model predicting two classifications of higher or lower cellularity. We tested these models using external validation. RESULTS: Overall, 100 patients (mean age: 62 ± 8.9 [standard deviation] years; 61 men) were included. The CT radiomics model used to predict cell count in 140 histological specimens predicted the actual cell count in 59 external validation specimens (root-mean-square error: 0.797). The two-classification model's accuracy was 70% and the F1 score was 0.73 in the external validation dataset including 30 patients. CONCLUSION: The CT radiomics-based model developed in this study provided useful information regarding the cellular infiltration in the ILD with good correlation with SLB specimens.

DOI： 10.1007/s11604-024-01607-2

PubMed

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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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J-GLOBAL

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