دانلود مقاله انگلیسی رایگان:مقایسه روشهای مبتنی بر یادگیری عمیق و مبتنی بر پچ برای تولید شبه CT در برنامه ریزی دوز پروستات مبتنی بر MRI - 2019
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  • Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning
    Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning


    ترجمه فارسی عنوان مقاله:

    مقایسه روشهای مبتنی بر یادگیری عمیق و مبتنی بر پچ برای تولید شبه CT در برنامه ریزی دوز پروستات مبتنی بر MRI


    منبع:

    Sciencedirect - Elsevier - International Journal of Radiation Oncology • Biology • Physics, Journal Pre-proof: doi:10:1016/j:ijrobp:2019:08:049


    نویسنده:

    Axel Largent, PhD, Anaïs Barateau, MSc, Jean-Claude Nunes, PhD, Eugenia Mylona, MSc, Joël Castelli, MD, Caroline Lafond, PhD, Peter B. Greer, PhD, Jason A. Dowling, PhD, John Baxter, PhD, Hervé Saint-Jalmes, PhD, Oscar Acosta, PhD, Renaud de Crevoisier, MD


    چکیده انگلیسی:

    Purpose Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for MRI-based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network (GAN)) using various loss functions (L2, single-scale perceptual loss (PL), multiscale PL, weighted multiscale PL), and a patch-based method (PBM). Materials and Methods Thirty-nine patients received a VMAT for prostate cancer (78 Gy). T2-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using seven configurations: four GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), two U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error (MAE) and mean error (ME), in Hounsfield units (HU), between the reference CT (CTref) and the pCT. Dose uncertainties were quantified as mean absolute differences between the DVHs calculated from the CTref and pCT obtained by each method. 3D gamma indexes were analyzed Results Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest MAE (≤34.4 HU). The ME were not different than 0 (p≤0.05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CTref DVHs (p≤0.05). Their dose uncertainties were: ≤0.6% for the prostate PTV V95%, ≤0.5% for the rectum V70Gy, and ≤0.1% for the bladder V50Gy. The PBM, U-Net PL and GAN PL presented the highest systematic dose uncertainties. The gamma passrates were >99% for all DLMs. The mean calculation time to generate one pCT was 15 s for the DLMs and 62 min for the PBM. Conclusion Generating pCT for MRI dose planning with DLMs and PBM provided low dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time.
    Keywords: pseudo-CT generation | MRI-only radiotherapy | deep learning | dose calculation | prostate cancer


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 46
    حجم فایل: 3070 کیلوبایت

    قیمت: رایگان


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