Abdominal computed tomography localizer image generation: A deep learning approach. (February 2022)

• Record Type: Journal Article
• Authors: Liu, Zongxi; Zhao, Huimin; Fang, Xiang; Huo, Donglai
• Is Part Of: Computer methods and programs in biomedicine. Volume 214(2022)
• Publication Date: 2022-02
• Subjects: CT localizer image -- Image generation -- Deep learning -- Encoder-decoder network -- Scaled mixture loss; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106575
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: CT exams are usually led by two localization (AP and lateral) quick scans. We propose a deep learning model to reconstruct one localizer image from the other. The model was evaluated using 12, 487 clinical abdominal exams from a PACS system. Results show the effectiveness and potential clinical applicability of the model. The model can be used to reduce patient dose and improve workflow efficiency. Abstract: Background and Objective: Computed Tomography (CT) has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localization scans, which are used for patient positioning and diagnostic scan parameter definition. These two localization scans contribute to the patient dose but are not used for diagnosis purposes. In this study, we investigate the possibility of using deep learning models to reconstruct one localization scan image from the other, thus reducing the patient dose and simplifying the clinical workflow. Methods: We propose a modified encoder-decoder network and a scaled mixture loss function specifically for the focal task. In this study, 12, 487 clinical abdominal exams were retrieved from a clinical medical imaging storage system and randomly split for training, validation, and test in the ratio of 7:1:2. Reconstructed images were compared with the ground truth in terms of location prediction error, profile prediction error, and attenuation prediction error. Results: The average location error, profile error, and attenuation error were 1.02±3.37 mm, 4.43±2.02%, and 6.2 ± 2.94% for lateral prediction, and 6.46±6.43 mm, 3.9 ± 2.32%, and 7.12±3.54% for AP prediction, respectively. Conclusions: We conclude that although the reconstructed abdominal CT localization images may lack some details on the internal organ structures, they could be used effectively for tube current modulation calculation and patient positioning purposes, leading to a reduction of radiation dose and scan time in clinical CT exams.