Уважаемые коллеги, доброго времени суток! Представляем вам японское научное издание Radiological Physics and Technology. Журнал имеет третий квартиль, издаётся в Springer Japan, его SJR за 2022 г. равен 0,346, пятилетний импакт-фактор 1,8, печатный ISSN - 1865-0333, электронный - 1865-0341, предметные области - Радиация, Рентгенология, радиационная медицина, медицинская визуализация, Радиология и радиационная медицина, Медицина (общие вопросы), Спортивная терапия и реабилитация. Вот так выглядит обложка:
Редактором является Нобуюки Канемацу, контактные данные - kanematsu.noboyuki@gst.go.jp.
Цель журнала является обеспечение форума для обмена новыми знаниями, связанными с исследованиями и разработками в области радиологической науки и техники, включая медицинскую физику и радиологические технологии в диагностической радиологии, ядерной медицине и лучевой терапии среди многих других радиологических дисциплин, а также способствовать прогрессу и совершенствованию в медицинской практике и уходе за пациентами.
Адрес издания - https://www.springer.com/journal/12194
Пример статьи, название - Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study. Заголовок (Abstract) - This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECTNC) for AC in 99mTc-galactosyl human albumin diethylenetriamine pentaacetic acid (GSA) scintigraphy and to reduce patient dosage. A cycle-consistent generative network (CycleGAN) model was used to generate pseudo-CT images. The training datasets comprised approximately 850 liver phantom images obtained from SPECTNC and real CT images. The training datasets were then input to CycleGAN, and pseudo-CT images were output. SPECT images with real-time CT attenuation correction (SPECTCTAC) and pseudo-CT attenuation correction (SPECTGAN) were acquired. The difference in liver volume between real CT and pseudo-CT images was evaluated. Total counts and uniformity were then used to evaluate the effects of AC. Additionally, the similarity coefficients of SPECTCTAC and SPECTGAN were assessed using a structural similarity (SSIM) index. The pseudo-CT images produced a lower liver volume than the real CT images. SPECTCTAC exhibited a higher total count than SPECTNC and SPECTGAN, which were approximately 60% and 7% lower, respectively. The uniformities of SPECTCTAC and SPECTGAN were better than those of SPECTNC. The mean SSIM value for SPECTCTAC and SPECTGAN was 0.97. We proposed a deep learning-based AC approach to generate pseudo-CT images from SPECTNC images in 99mTc-GSA scintigraphy. SPECTGAN with AC using pseudo-CT images was similar to SPECTCTAC, demonstrating the possibility of SPECT/CT examination with reduced exposure to radiation.
Keywords: 99mTc-GSA; Single-photon emission computed tomography; Attenuation correction; Deep learning; Pseudo-CT image
