A Learning-Based Approach to Derive Electron Density from Anatomical MRI for Radiation Therapy Treatment Planning

Yang, Xiaofeng; Lei, Yang; Shu, Hui‐Kuo; Rossi, Peter J.; Mao, Hui; Shim, Hyunsuk; Liu, T.

doi:10.1016/j.ijrobp.2017.06.437

Cited by 17 publications

(14 citation statements)

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“…In the training stage, we first extracted anatomical features on voxel, sub-region and whole-patch levels from each MR image, identified the most salient and informative features and utilize them together with the corresponding CT image to train an initial structured random forest based on an integrated auto-context model (ACM). 34,36 We then used the resulting forest to generate the SCT image for each MR image in the training set, leading to an initial set of predictions/generations. Together with the features from original CT images, we further extracted context features from the predicted CT images to train a new structured random forest to perform another prediction.…”

Section: Methodsmentioning

confidence: 99%

“…[30][31][32] These methods use a large number of pairs of registered planning CT (pCT) and MR images of each patient, with which the model learns the conversion between the MR signal and attenuation coefficient in CT. After the training phase, the algorithm uses MR images to predict the corresponding SCT. Recently, our group has developed a method to obtain SCTs from MR images 33,34 using machine learning with an auto-context model, with promising results for brain stereotactic radiosurgery. 35 In this retrospective study, we present the accuracy of our method 33,34 in dose calculation for prostate Volumetric Arc Therapy (VMAT) planning.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, our group has developed a method to obtain SCTs from MR images 33,34 using machine learning with an auto-context model, with promising results for brain stereotactic radiosurgery. 35 In this retrospective study, we present the accuracy of our method 33,34 in dose calculation for prostate Volumetric Arc Therapy (VMAT) planning. We compare dose volume histogram (DVH) endpoints for clinically relevant structures, and dose distributions obtained from SCT against those from the ground truth pCT.…”

Section: Introductionmentioning

confidence: 99%

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Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy

et al. 2019

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Purpose: MRI-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over CTs, without the ionizing radiation exposure. However, it requires the generation of a synthetic CT (SCT) from MRIs for patient setup and dose calculation. In this study, we aim to investigate the accuracy of dose calculation in prostate cancer radiotherapy using SCTs generated from MRIs using our learning-based method. Materials and Methods: We retrospectively investigated a total of 17 treatment plans from 10 patients, each having both planning CTs (pCT) and MRIs acquired before treatment. The SCT was registered to the pCT for generating SCT-based treatment plans. The original pCT-based plans served as ground truth. Clinically-relevant dose volume histogram (DVH) metrics were extracted from both ground truth and SCT-based plans for comparison and evaluation. Gamma analysis was performed for the comparison of absorbed dose distributions between SCT-and pCT-based plans of each patient. Results: Gamma analysis of dose distribution on pCT and SCT within 1%/1 mm at 10% dose threshold showed greater than 99% pass rate. The average differences in dose volume histogram (DVH) metrics for planning target volumes (PTVs) were less than 1%, and similar metrics for organs at risk (OAR) were not statistically different. Conclusion: The SCT images created from MR images using our proposed machine learning method are accurate for dose calculation in prostate cancer radiation treatment planning. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning. However, MR images are needed to further analyze geometric distortion effects. Digitally reconstructed radiograph (DRR) can be generated within our method, and their accuracy in patient setup needs further analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy

et al. 2019

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“…The electron density can then be derived from this sCT image. Based on different training models, these methods can be broadly classified into three categories: dictionary learning-based methods, [21][22][23][24] random forest-based methods, [25][26][27][28][29] and deep learning-based methods. [30][31][32][33][34][35][36][37] Dictionary learning-based methods rely on the similarity between different MRIs.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…Although this characteristic results in fast and parallel training capabilities and thus lead to low computational costs, the training procedure of random forest is not globally controlled by an appropriate metric of the regressors’ performance, and the entire state of training model could not be checked and improved at each node. This makes the training procedure theoretically difficult and unintuitive to comprehend the success of learning method and unveils several practical disadvantages (16): 1) In training, there is no guarantee that all thresholds of splitting functions have been properly learned by the entire model; 2) It is difficult to apply random forest to the CBCT correction, since the extracted CBCT features are usually in a high dimensional representation space and only a small fraction of randomly chosen features could be used for binary splitting. This would diminish the performance of training, thus weaken the inference ability when a new CBCT feature arrives.…”

Section: Methodsmentioning

confidence: 99%

Improving image quality of cone-beam CT using alternating regression forest

Lei

Tang

Higgins

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients’ data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.

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A Learning-Based Approach to Derive Electron Density from Anatomical MRI for Radiation Therapy Treatment Planning

Cited by 17 publications

References 0 publications

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy

Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy

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

Improving image quality of cone-beam CT using alternating regression forest

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