A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy

Liu, Yaru; Yue, Chenxi; Zhu, Jian; Yu, Haining; Cheng, Yang; Yin, Yong; Li, Baosheng; Dong, Jiwen

doi:10.3389/fonc.2019.00362

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…For better clinical use in patient alignment and for implementing adaptive helical tomotherapy, the quality of MVCT images must be improved owing to the image noise and low contrast. Conventional algorithms for enhancing the MVCT image quality have mostly been applied to alleviate noise through filtering‐based image processing 10–13 . However, the initial MVCT image quality considerably affects the filtering‐based method.…”

Section: Discussionmentioning

confidence: 99%

“…Conventional algorithms for enhancing the MVCT image quality have mostly been applied to alleviate noise through filtering-based image processing. [10][11][12][13] However, the initial MVCT image quality considerably affects the filtering-based method. Unlike conventional methods, the proposed method is free from the initial MVCT image quality because we create a new image through a nonlinear mapping function trained using deep learning.…”

Section: Discussionmentioning

confidence: 99%

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Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN‐based image synthesis with small datasets

et al. 2021

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Megavoltage computed tomography (MVCT) offers an opportunity for adaptive helical tomotherapy. However, high noise and reduced contrast in the MVCT images due to a decrease in the imaging dose to patients limits its usability. Therefore, we propose an algorithm to improve the image quality of MVCT. Methods:The proposed algorithm generates kilovoltage CT (kVCT)-like images from MVCT images using a cycle-consistency generative adversarial network (cycleGAN)-based image synthesis model. Data augmentation using an affine transformation was applied to the training data to overcome the lack of data diversity in the network training. The mean absolute error (MAE), root-meansquare error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were used to quantify the correction accuracy of the images generated by the proposed algorithm. The proposed method was validated by comparing the images generated with those obtained from conventional and deep learning-based image processing method through nonaugmented datasets. Results: The average MAE, RMSE, PSNR, and SSIM values were 18.91 HU, 69.35 HU, 32.73 dB, and 95.48 using the proposed method, respectively, whereas cycleGAN with non-augmented data showed inferior results (19.88 HU, 70.55 HU, 32.62 dB, 95.19, respectively). The voxel values of the image obtained by the proposed method also indicated similar distributions to those of the kVCT image. The dose-volume histogram of the proposed method was also similar to that of electron density corrected MVCT. Conclusions: The proposed algorithm generates synthetic kVCT images from MVCT images using cycleGAN with small patient datasets. The image quality achieved by the proposed method was correspondingly improved to the level of a kVCT image while maintaining the anatomical structure of an MVCT image. The evaluation of dosimetric effectiveness of the proposed method indicates the applicability of accurate treatment planning in adaptive radiation therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN‐based image synthesis with small datasets

et al. 2021

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“…As we increased the SNR in this study by roughly an order of magnitude, similar SNR improvements by their method would necessitate a 100-fold increase in radiation dose. Block-matching and framelet algorithms 5,6,36,37 are strong choices for denoising MVCT at nominal radiation doses but are limited in improving contrast due to MVCT having limited true contrast differences between different soft tissues. Liu et al 5 utilized a block-matching algorithm equipped with a discriminatory feature dictionary to improve H&N MVCT contrast from 1.45 AE 1.51 to 2.09 AE 1.68.…”

Section: Discussionmentioning

confidence: 99%

“…Block‐matching and framelet algorithms 5,6,36,37 are strong choices for denoising MVCT at nominal radiation doses but are limited in improving contrast due to MVCT having limited true contrast differences between different soft tissues. Liu et al 5 utilized a block‐matching algorithm equipped with a discriminatory feature dictionary to improve H&N MVCT contrast from 1.45 ± 1.51 to 2.09 ± 1.68. In comparison since skVCT mimics the contrast of kVCT images, skVCT is able to improve fat to muscle contrast for H&N from 1.6 ± 0.3 to 14.8 ± 0.4.…”

Section: Discussionmentioning

confidence: 99%

“…With these advantages of MVCT, groups have worked to mitigate these shortcomings in contrast and noise. There have been recent attempts to minimize the MVCT noise signature through statistical methods 5,6 ; however, even with low noise implementations, MVCT has lower tissue contrast values when compared to kVCT tissue contrast. Ideally, we would acquire MVCT's robustness to implanted metal devices while maintaining kVCT's high SNR and CNR.…”

Section: Introductionmentioning

confidence: 99%

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Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle‐consistent generative machine learning

et al. 2020

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Purpose: Megavoltage computed tomography (MVCT) has been implemented on many radiation therapy treatment machines as a tomographic imaging modality that allows for three-dimensional visualization and localization of patient anatomy. Yet MVCT images exhibit lower contrast and greater noise than its kilovoltage CT (kVCT) counterpart. In this work, we sought to improve these disadvantages of MVCT images through an image-to-image-based machine learning transformation of MVCT and kVCT images. We demonstrated that by learning the style of kVCT images, MVCT images can be converted into high-quality synthetic kVCT (skVCT) images with higher contrast and lower noise, when compared to the original MVCT. Methods: Kilovoltage CT and MVCT images of 120 head and neck (H&N) cancer patients treated on an Accuray TomoHD system were retrospectively analyzed in this study. A cycle-consistent generative adversarial network (CycleGAN) machine learning, a variant of the generative adversarial network (GAN), was used to learn Hounsfield Unit (HU) transformations from MVCT to kVCT images, creating skVCT images. A formal mathematical proof is given describing the interplay between function sensitivity and input noise and how it applies to the error variance of a high-capacity function trained with noisy input data. Finally, we show how skVCT shares distributional similarity to kVCT for various macro-structures found in the body. Results: Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were improved in skVCT images relative to the original MVCT images and were consistent with kVCT images. Specifically, skVCT CNR for muscle-fat, bone-fat, and bone-muscle improved to 14.8 AE 0.4, 122.7 AE 22.6, and 107.9 AE 22.4 compared with 1.6 AE 0.3, 7.6 AE 1.9, and 6.0 AE 1.7, respectively, in the original MVCT images and was more consistent with kVCT CNR values of 15.2 AE 0.8, 124.9 AE 27.0, and 109.7 AE 26.5, respectively. Noise was significantly reduced in skVCT images with SNR values improving by roughly an order of magnitude and consistent with kVCT SNR values. Axial slice mean (S-ME) and mean absolute error (S-MAE) agreement between kVCT and MVCT/skVCT improved, on average, from −16.0 and 109.1 HU to 8.4 and 76.9 HU, respectively. Conclusions: A kVCT-like qualitative aid was generated from input MVCT data through a Cycle-GAN instance. This qualitative aid, skVCT, was robust toward embedded metallic material, dramatically improves HU alignment from MVCT, and appears perceptually similar to kVCT with SNR and CNR values equivalent to that of kVCT images.

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Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

et al. 2022

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Background: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MRonly workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation. Purpose: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning. Methods: MVCTs and T1-weighted MRIs for 120 patients treated for head-andneck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs). Results: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning,or when immobilization devices were absent from diagnostic MRIs.Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively. Conclusions: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

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A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapy

Cited by 8 publications

References 26 publications

Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN‐based image synthesis with small datasets

Improvement of megavoltage computed tomography image quality for adaptive helical tomotherapy using cycleGAN‐based image synthesis with small datasets

Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle‐consistent generative machine learning

Generation of synthetic megavoltage CT for MRI‐only radiotherapy treatment planning using a 3D deep convolutional neural network

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