A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections

Rusanov, Branimir; Ebert, Martin A.; Mukwada, Godfrey; Hassan, Ghulam Mubashar; Sabet, Mahsheed

doi:10.1088/1361-6560/ac27b6

Cited by 8 publications

(14 citation statements)

References 32 publications

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“…These authors also investigated the use of MSE and MAE loss functions, with Lalonde et al 80 . and Rusanov et al 81 . both reporting an improved MAE for the latter loss (13.41 vs. 15.48 HU and 74 vs. 86 HU, respectively).…”

Section: Resultsmentioning

confidence: 99%

“…Rusanov et al 81 in their scatter correction study (74 vs. 77 HU). These authors also investigated the use of MSE and MAE loss functions, with Lalonde et al 80 and Rusanov et al 81 both reporting an improved MAE for the latter loss (13.41 vs. 15.48 HU and 74 vs. 86 HU, respectively). Nomura et al concluded that MAE penalized anatomic regions more than MSE, which tended to penalize noisy regions primarily in air, thereby leading to more inaccurate scatter correction in anatomic regions.…”

Section: ] -mentioning

confidence: 96%

“…Studies operating strictly in the projection domain attempted to approximate the scatter signal contained in raw projections by using either MC-derived scatter maps, 79,80 or a CT-priorbased correction approach as the ground truth. 73,75,81 Meanwhile, one study looked at predicting MC-corrected CBCT images from uncorrected CBCT images. 74 For all studies, U-Net was used as training was performed using paired data.…”

Section: Projection Domain Correctionsmentioning

confidence: 99%

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Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

et al. 2022

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The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone‐beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings—with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state‐of‐the‐art methods utilized in radiation oncology.

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Section: Resultsmentioning

confidence: 99%

Section: ] -mentioning

confidence: 96%

Section: Projection Domain Correctionsmentioning

confidence: 99%

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Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

et al. 2022

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“…Moreover, it was not validated on real‐patient data, whose distribution of scattering signals may be more complex than the phantom data 16–18 . Finally, Rusanov proposed to correct the projections by training a U‐Net with the digitally reconstructed radiograph (DRR) of phantoms 17 . But, the relation between CBCT projections and DRRs for real patients may be more complicated than the phantoms 33–36 …”

Section: Introductionmentioning

confidence: 99%

A projection‐domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle‐GAN and nonlocal means filter

et al. 2023

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BackgroundIn radiation treatments for head and neck tumors, cone‐beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation.PurposeTo improve the quality of CBCT for patients with head and neck cancer, a projection‐domain CBCT correction method was proposed using a cycle‐consistent generative adversarial network (cycle‐GAN) and a nonlocal means filter (NLMF) based on a reference digitally reconstructed radiograph (DRR).MethodsA cycle‐GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle‐GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a NLMF based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, the image quality of CBCT corrected with the proposed method was objectively assessed with an five‐point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies.ResultsThe mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image quality showed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only.ConclusionsThe proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients.

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“…U-Net is used for training by pairing CT and CBCT images, which requires a high registration accuracy between CBCT and CT images; it is usually used in head-and-neck sCT generation. [14][15][16] Rusanov 17 and Landry 18 trained U-Net in a projection domain to correct CBCT projection and then obtained sCT images through Feldkamp-Davis-Kress (FDK) reconstruction from the corrected projection. Wu 19 proposed an sCT generation method using multiresolution neural networks, in which a large number of low-resolution images are used to train the network to obtain a preliminary model and then final sCT images are generated by fine-tuning the coarse model using a fewer number of high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Streaking artifact reduction for CBCT‐based synthetic CT generation in adaptive radiotherapy

et al. 2022

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Background: Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT-based ART. Purpose: To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate highquality sCT images from CBCT and achieve an accurate dose calculation. Methods: The proposed SARN was trained in a self -supervised manner. Artifact-CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact-CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U-Net, cycle-GAN, attention-gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. Results: The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIU m ) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U-Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively.

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A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections

Cited by 8 publications

References 32 publications

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review

A projection‐domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle‐GAN and nonlocal means filter

Streaking artifact reduction for CBCT‐based synthetic CT generation in adaptive radiotherapy

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