Deep Learning Methods for Image Guidance in Radiation Therapy

Paysan, Pascal; Roggen, Toon; Zhu, Liangjia; Wessels, C.; Schreier, Jan; Buchacek, Martin; Scheib, Stefan

doi:10.1007/978-3-030-58309-5_1

Cited by 4 publications

(4 citation statements)

References 72 publications

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“…Much work has been done [232,25,86] regarding the characterization and mitigation of the various kinds of artifacts that negatively impact image quality in CT and CBCT scans. In recent years, DL-based approaches have shown promising results, including applications for IGRT and adaptive radiation therapy [203].…”

Section: Related Workmentioning

confidence: 99%

Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database

Thiam

Kessler

Amirian

et al. 2021

IEEE Trans. Affective Comput.

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sential roles in my academic journey while completing my Ph.D. dissertation. Their support, mentorship, and presence have been invaluable, and I am deeply appreciative. I must begin by acknowledging the support of my family: Edalat, Parivash, and Milad. Their belief in my aspirations and constant encouragement has strengthened me throughout this demanding journey.I am profoundly grateful to my distinguished Ph.D. committee, particularly Professor Friedhelm Schwenker, to whom I owe special thanks for his mentorship.

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Section: Related Workmentioning

confidence: 99%

Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database

Thiam

Kessler

Amirian

et al. 2021

IEEE Trans. Affective Comput.

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“…In recent years, deeplearning based approaches have shown promising results, including applications for IGRT and adaptive radiation therapy. 21 As the existing literature on deeplearning based CBCT motion compensation is scarce, and the developed methods generally are often applicable to artifact types other than motion, as well as for both CT and CBCT,we extend the discussion beyond the field of CBCT motion artifacts, which is the main focus of our study.…”

Section: Related Workmentioning

confidence: 99%

Mitigation of motion‐induced artifacts in cone beam computed tomography using deep convolutional neural networks

et al. 2023

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BackgroundCone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image‐guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto‐segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep‐learning based approaches promise ways to mitigate such artifacts.PurposeWe propose a novel deep‐learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre‐ and/or post‐processing steps during CBCT reconstruction.MethodsOur approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp‐Davis‐Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART‐TV). The neural networks, which are based on refined U‐net architectures, are trained end‐to‐end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time‐dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts.ResultsThe presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state‐of‐the‐art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal‐to‐noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction).ConclusionsFor the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre‐ and post‐processing plugins in the existing 3D CBCT reconstruction and trained end‐to‐end yield significant improvements in image quality and reduction of motion artifacts.

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“…Deep learning methods for image guidance in radiation therapy have been under progress to highlight tracks toward ART. 16,17 However, its application in routine clinical practice is limited due to the demanding nature of the processes involved (re-delineation and re-planning) and increased dependence on the available human resources. The re-planning options range from simple adaptations, such as adjustments of beam weights, to complete re-planning that involves re-optimization of the intensity modulation.…”

Section: Adaptive Radiotherapy (Art) Strategiesmentioning

confidence: 99%

Adaptive radiotherapy from past to future frontiers

Tunçel¹

2021

IJRRT

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Deep Learning Methods for Image Guidance in Radiation Therapy

Cited by 4 publications

References 72 publications

Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database

Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database

Mitigation of motion‐induced artifacts in cone beam computed tomography using deep convolutional neural networks

Adaptive radiotherapy from past to future frontiers

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