Dosimetric characterization of MRI‐only treatment planning for brain tumors in atlas‐based pseudo‐CT images generated from standard<i>T</i>1‐weighted MR images

Demol, B.; Boydev, Christine; Korhonen, Juha; Reynaert, N.

doi:10.1118/1.4967480

Cited by 54 publications

(45 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Atlas‐based methods are typically used to generate sCT images. These rely on a deformable image registration to bring the sCT atlas to the current MRI . These methods are inherently limited by the performance of deformable registration.…”

Section: Introductionmentioning

confidence: 99%

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

et al. 2019

View full text Add to dashboard Cite

Purpose Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI‐only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle‐consistent generative adversarial networks (cycle GAN), a deep‐learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. Methods and materials The cycle GAN‐based model was developed to generate sCT images in a patch‐based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one‐to‐one mapping. Dense block‐based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Results Leave‐one‐out cross‐validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. Conclusion We developed and validated a novel learning‐based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high‐quality sCT in minutes. The proposed method offers strong potential for supporting near real‐time MRI‐only treatment planning in the brain and pelvis.

show abstract

Section: Introductionmentioning

confidence: 99%

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The gamma index could be calculated on 3D dose volumes or 2D dose planes that may yield more stringent results . Overall, gamma analysis across different methods and anatomical sites showed a >95% passing rate with clinically used criteria of 2% and 2 mm (dose‐difference/DTA) …”

Section: Synthetic Ct From Mri Datamentioning

confidence: 99%

“…The metrics are calculated from cumulative dose volume histograms of structures based on the guidelines of Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) . The differences in reported metrics mostly were less than 2% and statistically insignificant (Table ). Wang et al further applied the graphic technique to assess the equivalence of dose‐volume metrics between the two image modalities.…”

Section: Synthetic Ct From Mri Datamentioning

confidence: 99%

Emerging role of MRI in radiation therapy

Chandarana

Wang

Tijssen

et al. 2018

Magnetic Resonance Imaging

114

View full text Add to dashboard Cite

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.

show abstract

“…Their dose results were in close agreement with the dose from standard CT planning. The main drawback is that they may not be robust to inter-individual variability [13] and several deformable registrations are necessary. In addition, registration errors could introduce bias in dose calculation.…”

Section: Introductionmentioning

confidence: 99%

“…The atlas-based methods, involve the registration of several MRI and CT atlases with a target MRI, followed by a CT atlas fusion step [2], [3], [13], [14]. Dowling et al [2] applied this methodology to prostate localization.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning

Largent

Nunes

Saint‐Jalmes

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-Dose calculation from MRI is a topical issue. New treatment systems combining a linear accelerator with a MRI have been recently being developed. MRI has good soft tissue contrast without ionizing radiation exposure. However, unlike CT, MRI does not provide electron density information necessary for dose calculation. We propose in this paper a machine learning method to simulate a CT from a target MRI and co-registered CT-MRI training set. Ten prostate MR and CT images have been considered. Firstly, a reference image was randomly selected in the training set. A common space has been built thanks to affine registrations between the training set and the reference image. Multiscale image descriptors such as spatial information, gradients and texture features were extracted from MRI patches at different levels of a Gaussian pyramid and used as voxel-wise characteristics in the learning scheme. A Conditional Inference Random Forest (CIRF) modelled the relation between MRI descriptors and CT patches. For validation, test images were spatially normalized and the same descriptors were computed to generate a new pCT. Leave-one out experiments were performed. We obtained a MAE = 45.79 (pCT vs CT). Dose volume histograms inside PTV and organs at risk are in close agreement. The D98% was 0.45 % (inside PTV) and the 3D gamma pass rate (1mm, 1%) was 99,2%. Our method has better results than direct bulk assignment. And the results suggest that the method may be used for dose calculations in an MR based planning system.

show abstract

Dosimetric characterization of MRI‐only treatment planning for brain tumors in atlas‐based pseudo‐CT images generated from standardT1‐weighted MR images

Cited by 54 publications

References 41 publications

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

Emerging role of MRI in radiation therapy

Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning

Contact Info

Product

Resources

About