Developments in deep learning based corrections of cone beam computed tomography to enable dose calculations for adaptive radiotherapy

Taasti, V.; Klages, Peter; Parodi, Katia; Muren, L.P.

doi:10.1016/j.phro.2020.07.012

Cited by 16 publications

(18 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 Additionally, similar techniques to derive sCT from a different imaging modality have been envisioned to improve the quality of CBCT. 29 CBCT plays a vital role in image-guided adaptive radiation therapy (IGART) for photon and proton therapy. However, due to the severe scatter noise and truncated projections, image reconstruction is affected by several artifacts, such as shading, streaking, and cupping.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

View full text Add to dashboard Cite

Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The investigated NNs have yielded results with better spatial resolution [37,122], higher uniformity across the crystal volume [98] or faster implementation [149] compared to other existing methods (e.g. maximum likelihood [112] or nearest neighbours [141], among many others).…”

Section: Deep Learning In Nuclear Imagingmentioning

confidence: 97%

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

et al. 2021

View full text Add to dashboard Cite

Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.

show abstract

“…A plan of the day concept as proposed by van de Schoot et al [2] or a real-time re-planning strategy as reported by Jagt et al [11] for cervix cancer with a prior plan-library may further improve target coverage and OARs sparing for patients subject to large interfractional anatomical variations. Nevertheless, the workflow of the latter strategy is still not clinically available and some challenges remain to be addressed before implementation of on-line adaptive RT/PT in clinical routine: improved image quality and automatic contouring would be required for this approach to become feasible and cost-effective [23] , [24] .…”

Section: Discussionmentioning

confidence: 99%

Impact of interfractional target motion in locally advanced cervical cancer patients treated with spot scanning proton therapy using an internal target volume strategy

Niyoteka

Berger

Fokdal

et al. 2021

Physics and Imaging in Radiation Oncology

View full text Add to dashboard Cite

Background and purpose The more localized dose deposition of proton therapy (PT) compared to photon therapy might allow a reduction in treatment-related side effects but induces additional challenges to address. The aim of this study was to evaluate the impact of interfractional motion on the target and organs at risk (OARs) in cervical cancer patients treated with spot scanning PT using an internal target volume (ITV) strategy. Methods and materials For ten locally advanced cervical cancer patients, empty and full bladder planning computed tomography (pCT) as well as 25 daily cone beam CTs (CBCTs) were available. The Clinical Target Volume (CTV), the High Risk CTV (CTV HR ) (gross tumor volume and whole cervix), the non-involved uterus as well as the OARs (bowel, bladder and rectum) were contoured on the daily CBCTs and transferred to the pCT through rigid bony match. Using synthetic CTs derived from pCTs, four-beam spot scanning PT plans were generated to target the patient-specific ITV with 45 Gy(RBE) in 25 fractions. This structure was defined based on pre-treatment MRI and CT to anticipate potential target motion throughout the treatment. D98% of the targets and V40Gy(RBE) of the OARs were extracted from the daily anatomies, accumulated and analyzed. In addition, the impact of bladder volume deviations from planning values on target and bowel dose was investigated. Results The ITV strategy ensured a total accumulated dose >42.75 Gy(RBE) to the CTV HR for all ten patients. Two patients with large bladder-related uterus motion had accumulated dose to the non-involved uterus of 35.7 Gy(RBE) and 41.1 Gy(RBE). Variations in bowel V40Gy(RBE) were found to be correlated (Pearson r = −0.55; p-value <0.0001) with changes in bladder volume during treatment. Conclusion The ITV concept ensured adequate dose to the CTV HR , but was insufficient for the non-involved uterus of patients subject to large target interfractional motion. CBCT monitoring and occasional replanning is recommended along the same lines as with photon radiotherapy in cervical cancer.

show abstract

Developments in deep learning based corrections of cone beam computed tomography to enable dose calculations for adaptive radiotherapy

Cited by 16 publications

References 31 publications

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Impact of interfractional target motion in locally advanced cervical cancer patients treated with spot scanning proton therapy using an internal target volume strategy

Contact Info

Product

Resources

About