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Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom

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Abstract

This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ADC = 1.1 × 10–3 mm2/s at 0 °C) was imaged at various b-values (0, 1000, 2000, and 4000 s/mm2) using a 3 T magnetic resonance imaging scanner with slice thicknesses of 1.5 and 3.0 mm. All DWIs were reconstructed with or without DLR. ADC maps were generated using combinations of b-values 0 and 1000, 0 and 2000, and 0 and 4000 s/mm2. Based on the quantitative imaging biomarker alliance profile, the signal-to-noise ratio (SNRs) in DWIs was calculated, and the accuracy, precision, and within-subject parameter variance (wCV) of the ADCs were evaluated. DLR improved the SNR in DWIs with b-values ranging from 0 to 2000s/mm2; however, its effectiveness was diminished at 4000 s/mm2. There was no noticeable difference in the ADCs of images generated with or without implementing DLR. For a slice thickness of 1.5 mm and combined b-values of 0 and 4000 s/mm2, the ADC values were 0.97 × 10–3and 0.98 × 10–3mm2/s with and without DLR, respectively, both being lower than the true ADC value. Furthermore, DLR enhanced the precision and wCV of the ADC measurements. DLR can enhance the SNR, repeatability, and precision of ADC measurements; however, it does not improve their accuracies.

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Acknowledgements

We thank Naoko Mita and Shimpei Yano at Teikyo University Hospital and Seiko Shimizu at Canon Medical Systems Corporation for their technical support. We would like to thank Editage (www.editage.jp) for English language editing.

Funding

This study was partly supported by JSPS KAKENHI (grant number 23K07142).

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Authors and Affiliations

  1. Graduate School of Medical Technology, Teikyo University, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan

    Tatsuya Hayashi, Shinya Kojima & Toshimune Ito

  2. Department of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamiokimachi, Maebashi, Gunma, 371-0052, Japan

    Norio Hayashi

  3. Department of Radiology, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-Ku, Tokyo, 173-8605, Japan

    Hiroshi Kondo, Asako Yamamoto & Hiroshi Oba

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  1. Tatsuya Hayashi
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Correspondence to Tatsuya Hayashi.

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Hayashi, T., Kojima, S., Ito, T. et al. Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom. Radiol Phys Technol 17, 186–194 (2024). https://doi.org/10.1007/s12194-023-00765-8

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  • DOI: https://doi.org/10.1007/s12194-023-00765-8

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