Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations

Landry, Guillaume; Hansen, David C.; Kamp, Florian; Li, Minglun; Hoyle, B.; Weller, J.; Parodi, Katia; Belka, Claus; Kurz, Christopher

doi:10.1088/1361-6560/aaf496

Cited by 72 publications

(108 citation statements)

References 35 publications

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“…Studies using DL for pCT generation from CBCT are scarce for brain, 55 H&N, [13][14][15] pancreas, 38 or prostate cancer. [35][36][37]55,56 The studies involved an imaging analysis (pCT vs. reference CT), but only half of them evaluated the dose accuracy. Among the three H&N studies using DL for pCT generation from CBCT, [13][14][15] one involved training a U-Net neural network on 50 coregistered CBCT/CT images and performing a test based on data from 10 patients.…”

Section: Discussionmentioning

confidence: 99%

“…32 Some studies have recently proposed DLMs for pCT generation from CBCT, mainly for scatter correction. 33,34 Other studies proposed the DLM for pCT generation from CBCT in prostate, [35][36][37] pancreas, 38 and H&N, [13][14][15] for dose calculation. H&N studies have been performed using either U-Net or cycleGAN architectures to generate pCT from CBCT.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

et al. 2020

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Purpose: Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy. Methods: Forty-four patients received VMAT for H&N cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-airbone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CT ref). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CT ref and pCT of each method. Results: In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and −36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 AE 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland D mean and 5 AE 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (D mean). For the parotid gland D mean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate AE standard deviation was 98.1 AE 1.2%, 91.0 AE 5.3%, 97.9 AE 1.6%, and 98.8 AE 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM. The mean calculation time to generate one pCT was 30 sec for the deep learning and DIR methods. Conclusions: For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCTbased dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

et al. 2020

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“…Finally, Landry et al . evaluated the performance of a Unet on projection domain and image domain CBCT data and found that CBCT corrections in the image domain achieved lower mean error (ME) and MAE …”

Section: Introductionmentioning

confidence: 99%

“…23 Finally, Landry et al evaluated the performance of a Unet on projection domain and image domain CBCT data and found that CBCT corrections in the image domain achieved lower mean error (ME) and MAE. 24 In this work, we propose a deep learning method, a cycleconsistent adversarial network (cycle-GAN), based on paired CT and CBCT images to correct CBCT images. 25 The cycle-GAN framework is appealing for CBCT correction because it can efficiently convert images between the source domain and the target domain when the underlying structures are similar, even if the mapping between domains is nonlinear.…”

Section: Introductionmentioning

confidence: 99%

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

et al. 2019

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Purpose The incorporation of cone‐beam computed tomography (CBCT) has allowed for enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning‐based method for generating high quality corrected CBCT (CCBCT) images is proposed. Methods The proposed method integrates a residual block concept into a cycle‐consistent adversarial network (cycle‐GAN) framework, called res‐cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle‐GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end‐to‐end CBCT‐to‐CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indices, and spatial non‐uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning‐based CBCT correction method. Results Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning‐based method. Conclusions The authors have developed a novel deep learning‐based method to generate high‐quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.

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“…The use of in-room CT currently guarantees the most accurate definition of proton stopping power, and with a low-dose protocol, it is possible to limit the imaging dose to the patient to below 1 mGy per CT [23,24], similar to that delivered with CBCT [25]. CBCT for adaption is currently under investigation but extracting density information with the same accuracy as with CT [26][27][28][29][30] remains challenging. The use of MRI for daily imaging clearly has advantages of no imaging dose, but is currently only under investigation [31,32].…”

Section: Patientmentioning

confidence: 99%

Daily adaptive proton therapy – the key to innovative planning approaches for paranasal cancer treatments

et al. 2019

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Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations

Abstract: The colorbar range in figures 9(c), (e) and (g) in our article was unfortunately incorrect. The correct range is −15% to 15%, and not −2% to 2%. This error does not impact the quantitative analyses reported in the main body of the article or in other figures.

Cited by 72 publications

References 35 publications

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

Comparison of CBCT‐based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

Daily adaptive proton therapy – the key to innovative planning approaches for paranasal cancer treatments

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