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Background: 4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability., Purpose: In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas., Methods: The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. The study is based on 65 in-house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets., Results: Automated artifact detection revealed a ROC-AUC of 0.99 for INT and of 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52 % (INT) and 59 % (DS) for the in-house data. For the external test data sets, the RMSE improvement is similar (50 % and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72 % of the detectable artifacts were removed., Conclusions: The results highlight the potential of DL-based inpainting for restoration of artifact-affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient-specific prior image information., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Four-dimensional cone-beam computed tomography (4D CBCT) imaging has been suggested as a solution to account for interfraction motion variability of moving targets like lung and liver during radiotherapy (RT) of moving targets. However, due to severe sparse view sampling artifacts, current 4D CBCT data lack sufficient image quality for accurate motion quantification. In the present paper, we introduce a deep learning-based framework for boosting the image quality of 4D CBCT image data that can be combined with any CBCT reconstruction approach and clinical 4D CBCT workflow., Methods: Boosting is achieved by learning the relationship between so-called sparse view pseudo-time-average CBCT images obtained by a projection selection scheme introduced to mimic phase image sparse view artifact characteristics and corresponding time-average CBCT images obtained by full view reconstruction. The employed convolutional neural network architecture is the residual dense network (RDN). The underlying hypothesis is that the RDN learns the appearance of the streaking artifacts that is typical for 4D CBCT phase images - and removes them without influencing the anatomical image information. After training the RDN, it can be applied to the 4D CBCT phase images to enhance the image quality without affecting the contained temporal and motion information. Different to existing approaches, no patient-specific prior knowledge about anatomy or motion characteristics is needed, that is, the proposed approach is self-contained., Results: Application of the trained network to reconstructed phase images of an external (SPARE challenge) as well as in-house 4D CBCT patient and motion phantom data set reduces the phase image streak artifacts consistently for all patients and state-of-the-art reconstruction approaches. Using the SPARE data set, we show that the root mean squared error compared to ground truth data provided by the challenge is reduced by approximately 50% while normalized cross correlation of reconstruction and ground truth is improved up to 10%. Compared to direct deep learning-based 4D CBCT to 4D CT mapping, our proposed method performs better because inappropriate prior knowledge about the patient anatomy and physiology is taken into account. Moreover, the image quality enhancement leads to more plausible motion fields estimated by deformable image registration (DIR) in the 4D CBCT image sequences., Conclusions: The presented framework enables significantly boosting of 4D CBCT image quality as well as improved DIR and motion field consistency. Thus, the proposed method facilitates extraction of motion information from severely artifact-affected images, which is one of the key challenges of integrating 4D CBCT imaging into RT workflows., (© 2020 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632)., Methods: Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data., Results: Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning., Conclusions: The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners., (© 2020 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: Four-dimensional (4D) CT imaging is a central part of current treatment planning workflows in 4D radiotherapy (RT). However, clinical 4D CT image data often suffer from severe artifacts caused by insufficient projection data coverage due to the inability of current commercial 4D CT imaging protocols to adapt to breathing irregularity. We propose an intelligent sequence mode 4D CT imaging protocol (i4DCT) that builds on online breathing curve analysis and respiratory signal-guided selection of beam on/off periods during scan time in order to fulfill projection data coverage requirements. i4DCT performance is evaluated and compared to standard clinical sequence mode 4D CT (seq4DCT) and spiral 4D CT (spiral4DCT) approaches., Methods: i4DCT consists of three main blocks: (a) an initial learning period to establish a patient-specific reference breathing cycle representation for data-driven i4DCT parameter selection, (b) online respiratory signal-guided sequence mode scanning (i4DCT core), (c) rapid breathing record analysis and quality control after scanning to trigger potential local rescanning (i4DCT rescan). Based on a phase space representation of the patient's breathing signal, i4DCT core implements real-time analysis of the signal to appropriately switch on and off projection data acquisition even during irregular breathing. Performance evaluation was based on 189 clinical breathing records acquired during spiral 4D CT scanning for RT planning (data acquisition period: 2013-2017; Siemens Somatom with Varian RPM system). For each breathing record, i4DCT, seq4DCT, and spiral4DCT scanning protocol variants were simulated. Evaluation measures were local projection data coverage β cov ; number ϵ total of local projection data coverage failures; and number ϵ pat of patients with coverage failures; average beam on time t beam on as a surrogate for imaging dose and total patient on table time t table as the time between first and last beam on signal., Results: Using i4DCT, mean inhalation and exhalation projection data coverage β cov increased significantly compared to standard spiral 4D CT scanning as applied for the original clinical data acquisition and conventional 4D CT sequence scanning modes. The improved projection data coverage translated into a reduction of coverage failures ϵ total by 89% without and 93% when allowing for a rescanning at up to five z-positions compared to spiral scanning and between 76% and 82% without and 85% and 89% with rescanning when compared to seq4DCT. Similar numbers were observed for ϵ pat . Simultaneously, i4DCT (without rescanning) reduced the beam on time on average by 3%-17% compared to standard spiral 4D CT. In turn, the patient on table time increased by between 35% and 66%. Allowing for rescanning led on average to additional 5.9 s beam on and 10.6 s patient on table time., Conclusions: i4DCT outperformed currently implemented clinical fixed beam on period 4D CT scanning approaches by means of a significantly smaller data coverage failure rate without requiring additional beam on time compared to, for example, conventional spiral 4D CT protocols., (© 2019 American Association of Physicists in Medicine.)