Enhancement of Hounsfield unit distribution in cone-beam CT images for adaptive radiation therapy: Evaluation of a hybrid correction approach

Kidar, Halima Saadia; Azizi, H.

doi:10.1016/j.ejmp.2020.01.002

Cited by 11 publications

(9 citation statements)

References 38 publications

(45 reference statements)

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“…Using an MC-based methodology, HU correction of about 31% for five lung cancer patient images was gained [13]. Another proposal, which used histogram matching on ten prostate cancer patient scans, resulted in a 20% HU correction [24]. A phantom-based study was carried out on the Catphan 600 (The Phantom Laboratory, Salem, NY, USA) [25] achieved an overall accuracy of 95% in HU recovery.…”

Section: Comparison With the Literaturementioning

confidence: 99%

“…Prior-CT-based methods leverage information obtained from high-resolution planning CT applied to up-to-date information contained in CBCT. They can be based on processing techniques like deformable registration of CT to CBCT [22], or by linearly scaling the CBCT HU values to CT ones through histogram matching [23,24]. The shading artifacts inside CBCT can also be estimated by low-pass filtering the difference between the high-resolution CT projections and the raw CBCT ones and then subtracted from the CBCT during reconstruction [25].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Rossi

Cerveri

2021

Diagnostics

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Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks (CNNs) to convert CBCT to CT-like scans, comparing supervised to unsupervised training techniques, exploiting a pelvic CT/CBCT publicly available dataset. Interestingly, quantitative results were in favor of supervised against unsupervised approach showing improvements in the HU accuracy (62% vs. 50%), structural similarity index (2.5% vs. 1.1%) and peak signal-to-noise ratio (15% vs. 8%). Qualitative results conversely showcased higher anatomical artifacts in the synthetic CBCT generated by the supervised techniques. This was motivated by the higher sensitivity of the supervised training technique to the pixel-wise correspondence contained in the loss function. The unsupervised technique does not require correspondence and mitigates this drawback as it combines adversarial, cycle consistency, and identity loss functions. Overall, two main impacts qualify the paper: (a) the feasibility of CNN to generate accurate synthetic CT from CBCT images, which is fast and easy to use compared to traditional techniques applied in clinics; (b) the proposal of guidelines to drive the selection of the better training technique, which can be shifted to more general image-to-image translation.

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Section: Comparison With the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Rossi

Cerveri

2021

Diagnostics

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“…reported HU correction of about 31% for five lung cancer patient images 7 . Another method based on histogram matching on ten prostate cancer patient images provided HU correction of about 20% 47 . Phantom studies have reached up to a 95% overall accuracy in the HU recovery on the Catphan 600 48 alone, but no data were provided for patients.…”

Section: Discussionmentioning

confidence: 99%

“… 7 Another method based on histogram matching on ten prostate cancer patient images provided HU correction of about 20%. 47 Phantom studies have reached up to a 95% overall accuracy in the HU recovery on the Catphan 600 48 alone, but no data were provided for patients. In comparison, our FT

and noFT models respectively obtained around 69.38% and 62.29% average improvement on the whole image in the pelvic region.…”

Section: Discussionmentioning

confidence: 99%

Image‐based shading correction for narrow‐FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning

et al. 2021

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Cone beam computed tomography (CBCT) is a standard solution for in-room image guidance for radiation therapy. It is used to evaluate and compensate for anatomopathological changes between the dose delivery plan and the fraction delivery day. CBCT is a fast and versatile solution, but it suffers from drawbacks like low contrast and requires proper calibration to derive density values. Although these limitations are even more prominent with in-room customized CBCT systems, strategies based on deep learning have shown potential in improving image quality. As such, this article presents a method based on a convolutional neural network and a novel two-step supervised training based on the transfer learning paradigm for shading correction in CBCT volumes with narrow field of view (FOV) acquired with an ad hoc in-room system. Methods: We designed a U-Net convolutional neural network, trained on axial slices of corresponding CT/CBCT couples. To improve the generalization capability of the network, we exploited two-stage learning using two distinct data sets. At first, the network weights were trained using synthetic CBCT scans generated from a public data set, and then only the deepest layers of the network were trained again with real-world clinical data to fine-tune the weights. Synthetic data were generated according to real data acquisition parameters. The network takes a single grayscale volume as input and outputs the same volume with corrected shading and improved HU values. Results: Evaluation was carried out with a leave-one-out cross-validation, computed on 18 unique CT/CBCT pairs from six different patients from a real-world dataset. Comparing original CBCT to CT and improved CBCT to CT,we obtained an average improvement of 6 dB on peak signal-to-noise ratio (PSNR), +2% on structural similarity index measure (SSIM).The median interquartile range (IQR) Hounsfield unit (HU) difference between CBCT and CT improved from 161.37 (162.54) HU to 49.41 (66.70) HU. Region of interest (ROI)-based HU difference was narrowed by 75% in the spongy bone (femoral head), 89% in the bladder, 85% for fat, and 83% for muscle. The improvement in contrast-to-noise ratio for these ROIs was about 67%.GLOSSARY: CBCT, cone beam computed tomography; CNR, contrast-to-noise ratio; CT, computed tomography; CTV, clinical target volume; DIR, deformable image registration; D r , data set containing only real images; D s , data set containing synthetic CBCT images and real CT; FOV, field of view; FT x , model trained with transfer learning on x blocks, where x can be 1, 2 or 3; HU, Hounsfield unit; IQR, interquartile range; LOO-CV, leave-one-out cross-validation; MAE, mean absolute error; noFT, U-Net model trained without transfer learning; pCT, planning CT; PSNR, peak signal-to-noise ratio; ROI, region of interest; SSIM, structural similarity index measureThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is prope...

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“…Both methods require no additional software other than the TPS. Other CBCT conversion methods such as histogram matching, 42,43 scatter correction and image reconstruction methods, 44–46 deformable image registration based virtual CT creation methods, 47,48 and artificial intelligence based correction methods 49–51 could potentially increase the accuracy in dose calculation, but require additional software and computing power.…”

Section: Discussionmentioning

confidence: 99%

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

et al. 2021

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Purpose: In the past years, many different neural network-based conversion techniques for synthesising CTs (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, e.g. for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated.Methods: 41 patients with tumours in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method (CBCT RS ) and a population-based dose calculation method (CBCT P op ) were employed for CBCT based dose calculation. The dose distributions were analysed using 3D global gamma analysis applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs at risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs.Results: The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60 HU for MAE. Using the 2%/2mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0±0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0±0.8% and 99.1±0.8% for the CBCT RS and CBCT P op , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4±1.6% and 99.2±0.6% for CBCT RS and CBCT P op , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. Conclusion:The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed-up.

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Enhancement of Hounsfield unit distribution in cone-beam CT images for adaptive radiation therapy: Evaluation of a hybrid correction approach

Cited by 11 publications

References 38 publications

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT

Image‐based shading correction for narrow‐FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region

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