Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging

Insights into Imaging

Zahra Mansouri¹  Dariush Askari²  Amirhossein Sanaat³  Abdollah Saberi Manesh³  Isaac Shiri³  Hossein Arabi³  Yazdan Salimi³  Azadeh Akhavanallaf³  Habib Zaidi⁴  Masoumeh Pakbin⁵  Saleh Sandoughdaran⁶  Ehsan Sharifipour⁷ 

[1] Department of Biomedical Engineering and Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran;Department of Radiology Technology, Shahid Beheshti University of Medical, Tehran, Iran;Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland;Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva, Switzerland;Geneva University Neurocenter, Geneva University, Geneva, Switzerland;Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands;Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark;Imaging Department, Qom University of Medical Sciences, Qom, Iran;Men’s Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran;Neuroscience Research Center, Qom University of Medical Sciences, Qom, Iran;

关键词: CT;    Radiation dose;    Overscanning;    Deep learning;    Chest imaging;   

DOI  :  10.1186/s13244-021-01105-3

来源: Springer

【 摘 要 】

BackgroundDespite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients.ResultsA significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior–posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and − 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was − 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose.ConclusionThe proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.

【 授权许可】

CC BY