Head-and-Neck MRI-only radiotherapy treatment planning: From acquisition in treatment position to pseudo-CT generation

Largent, A.; Marage, Louis; Gicquiau, I.; Nunes, Jean‐Claude; Reynaert, N.; Castelli, J.; Chajon, É.; Acosta, Oscar; Gambarota, Giulio; Crevoisier, R. de; Saint‐Jalmes, Hervé

doi:10.1016/j.canrad.2020.01.008

Cited by 15 publications

(18 citation statements)

References 24 publications

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“…In this review, six studies used classical GAN-based architectures to generate sCT from MRI [20,29,62,77,84,85]. The adversarial loss functions of the generator evaluating sCT and the original CTs used in these GANs are :…”

Section: I) Ganmentioning

confidence: 99%

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

et al. 2021

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Section: I) Ganmentioning

confidence: 99%

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

et al. 2021

Self Cite

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“…36 More recently, deep learning approaches are gaining momentum and showing potential for success in converting MRI data to synthetic CT, especially where bone and air are present. [37][38][39] Synthetic CT From MRI Data…”

Section: Mri For Pretreatment Planningmentioning

confidence: 99%

“…The MRI-only pathway for RT has been suggested and implemented by many groups. 26,39,[90][91][92][93][94][95] Such approaches have patient comfort, time delay, and financial burden for the multiple imaging in mind. However, many technical hurdles remain unsolved, such as geometric distortion and creation of universal synthetic CT. 25,[96][97][98] An MR-only workflow, with or without MR-LINAC, will require suitable MRIcompatible immobilization devices, MRI sequences for disease-specific imaging for target and OAR delineation, synthetic CT generation, treatment planning, and then finally treatment.…”

Section: Mr Guidance During Treatmentmentioning

confidence: 99%

Emerging role of MRI in radiation therapy

Chandarana

Wang

Tijssen

et al. 2018

Magnetic Resonance Imaging

114

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Advances in multimodality imaging, providing accurate information of the irradiated target volume and the adjacent critical structures or organs at risk (OAR), has made significant improvements in delivery of the external beam radiation dose. Radiation therapy conventionally has used computed tomography (CT) imaging for treatment planning and dose delivery. However, magnetic resonance imaging (MRI) provides unique advantages: added contrast information that can improve segmentation of the areas of interest, motion information that can help to better target and deliver radiation therapy, and posttreatment outcome analysis to better understand the biologic effect of radiation. To take advantage of these and other potential advantages of MRI in radiation therapy, radiologists and MRI physicists will need to understand the current radiation therapy workflow and speak the same language as our radiation therapy colleagues. This review article highlights the emerging role of MRI in radiation dose planning and delivery, but more so for MR-only treatment planning and delivery. Some of the areas of interest and challenges in implementing MRI in radiation therapy workflow are also briefly discussed. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1468-1478.

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“…However, segmentations of the surrounding brain tissues may aid in understanding the compressive and vascular effects of PHH (du Plessis, 1998; Maertzdorf, Vles, Beuls, Mulder, & Blanco, 2002; Robinson, 2012). Although MR imaging can provide more accurate 3D measurements than ultrasound and CT imaging due to better soft‐tissue contrast (Glenn & Barkovich, 2006; Largent et al, 2020; Qiu et al, 2015), only one study (Qiu et al, 2015) has investigated (on a small cohort) MRI‐based automatic brain segmentation of PHH in preterm infants. In addition, none of the methods presented in these studies provided an assessment of their uncertainty (i.e., values indicating whether the models are certain or not about their segmentation predictions).…”

Section: Introductionmentioning

confidence: 99%

Automatic brain segmentation in preterm infants with post‐hemorrhagic hydrocephalus using 3D Bayesian U‐Net

Largent

Asis-Cruz

Kapse

et al. 2022

Human Brain Mapping

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Post‐hemorrhagic hydrocephalus (PHH) is a severe complication of intraventricular hemorrhage (IVH) in very preterm infants. PHH monitoring and treatment decisions rely heavily on manual and subjective two‐dimensional measurements of the ventricles. Automatic and reliable three‐dimensional (3D) measurements of the ventricles may provide a more accurate assessment of PHH, and lead to improved monitoring and treatment decisions. To accurately and efficiently obtain these 3D measurements, automatic segmentation of the ventricles can be explored. However, this segmentation is challenging due to the large ventricular anatomical shape variability in preterm infants diagnosed with PHH. This study aims to (a) propose a Bayesian U‐Net method using 3D spatial concrete dropout for automatic brain segmentation (with uncertainty assessment) of preterm infants with PHH; and (b) compare the Bayesian method to three reference methods: DenseNet, U‐Net, and ensemble learning using DenseNets and U‐Nets. A total of 41 T2‐weighted MRIs from 27 preterm infants were manually segmented into lateral ventricles, external CSF, white and cortical gray matter, brainstem, and cerebellum. These segmentations were used as ground truth for model evaluation. All methods were trained and evaluated using 4‐fold cross‐validation and segmentation endpoints, with additional uncertainty endpoints for the Bayesian method. In the lateral ventricles, segmentation endpoint values for the DenseNet, U‐Net, ensemble learning, and Bayesian U‐Net methods were mean Dice score = 0.814 ± 0.213, 0.944 ± 0.041, 0.942 ± 0.042, and 0.948 ± 0.034 respectively. Uncertainty endpoint values for the Bayesian U‐Net were mean recall = 0.953 ± 0.037, mean negative predictive value = 0.998 ± 0.005, mean accuracy = 0.906 ± 0.032, and mean AUC = 0.949 ± 0.031. To conclude, the Bayesian U‐Net showed the best segmentation results across all methods and provided accurate uncertainty maps. This method may be used in clinical practice for automatic brain segmentation of preterm infants with PHH, and lead to better PHH monitoring and more informed treatment decisions.

show abstract

Head-and-Neck MRI-only radiotherapy treatment planning: From acquisition in treatment position to pseudo-CT generation

Cited by 15 publications

References 24 publications

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

Emerging role of MRI in radiation therapy

Automatic brain segmentation in preterm infants with post‐hemorrhagic hydrocephalus using 3D Bayesian U‐Net

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