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Background: Cone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction., Purpose: A novel stacked coarse-to-fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial-frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images., Methods: The enhancement of low-quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U-ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo-CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT-CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing., Results: To evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross-correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t-test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p < 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAE does ) values for the maximum dose (D max ), the minimum dose (D min ), and the mean dose (D mean ) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (D min , D max , and D mean ) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann-Whitney test (p < 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation., Conclusion: The pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers., (© 2024 American Association of Physicists in Medicine.)

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Pseudo-computed tomography (pCT) quality is a crucial issue in magnetic resonance image (MRI)-only brain stereotactic radiotherapy (SRT), so this study systematically evaluated it from the multi-modal radiomics perspective., Methods: 34 cases (< 30 cm³) were retrospectively included (2021.9-2022.10). For each case, both CT and MRI scans were performed at simulation, and pCT was generated by a convolutional neural network (CNN) from planning MRI. Conformal arc or volumetric modulated arc technique was used to optimize the dose distribution. The SRT dose was compared between pCT and planning CT with dose volume histogram (DVH) metrics and gamma index. Wilcoxon test and Spearman analysis were used to identify key factors associated with dose deviations. Additionally, original image features were extracted for radiomic analysis. Tumor control probability (TCP) and normal tissue complication probability (NTCP) were employed for efficacy evaluation., Results: There was no significant difference between pCT and planning CT except for radiomics. The mean value of Hounsfield unit of the planning CT was slightly higher than that of pCT. The Gadolinium-based agents in planning MRI could increase DVH metrics deviation slightly. The median local gamma passing rates (1%/1 mm) between planning CTs and pCTs (non-contrast) was 92.6% (range 63.5-99.6%). Also, differences were observed in more than 85% of original radiomic features. The mean absolute deviation in TCP was 0.03%, and the NTCP difference was below 0.02%, except for the normal brain, which had a 0.16% difference. In addition, the number of SRT fractions and lesions, and lesion morphology could influence dose deviation., Conclusions: This is the first multi-modal radiomics analysis of CNN-based pCT from planning MRI for SRT of small brain lesions, covering dosiomics and radiomics. The findings suggest the potential of pCT in SRT plan design and efficacy prediction, but caution needs to be taken for radiomic analysis., (© 2024. The Author(s).)

Purpose/objectives: An artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care., Materials and Method: In collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics., Results: The observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs., Conclusion: This study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results., Competing Interests: NP: Chief Executive Officer; Therapanacea. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Prunaretty, Güngör, Gevaert, Azria, Valdenaire, Balermpas, Boldrini, Chuong, De Ridder, Hardy, Kandiban, Maingon, Mittauer, Ozyar, Roque, Colombo, Paragios, Pennell, Placidi, Shreshtha, Speiser, Tanadini-Lang, Valentini and Fenoglietto.)

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories., (© 2023. Springer Nature Switzerland AG.)

Introduction: The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction., Methods: A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ -map were used as input to the models. CT and PET/MR examinations for 22 patients ([ 18 F]FDG) were used for training and validation, and 17 patients were used for testing (6 [ 18 F]PSMA-1007 and 11 [ 68 Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance., Results: In the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUV max ) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%., Conclusion: The proposed method reduces the voxel-based error and SUV max errors in bone lesions when compared to the four- and five-class Dixon-based AC models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Abrahamsen, Knudtsen, Eikenes, Bathen and Elschot.)

Purpose: CT is routinely used to detect cranial abnormalities in pediatric patients with head trauma or craniosynostosis. This study aimed to develop a deep learning method to synthesize pseudo-CT (pCT) images for MR high-resolution pediatric cranial bone imaging to eliminating ionizing radiation from CT., Methods: 3D golden-angle stack-of-stars MRI were obtained from 44 pediatric participants. Two patch-based residual UNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures/sutures, or air (NetBA) to synthesize pCT. A third residual UNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCT NetWH ) in the brain area and NetBA (pCT NetBA ) in the nonbrain area were combined to generate pCT Com . A manual processing method using inverted MR images was also employed for comparison., Results: pCT Com (68.01 ± 14.83 HU) had significantly smaller mean absolute errors (MAEs) than pCT NetWH (82.58 ± 16.98 HU, P < 0.0001) and pCT NetBA (91.32 ± 17.2 HU, P < 0.0001) in the whole head. Within cranial bone, the MAE of pCT Com (227.92 ± 46.88 HU) was significantly lower than pCT NetWH (287.85 ± 59.46 HU, P < 0.0001) but similar to pCT NetBA (230.20 ± 46.17 HU). Dice similarity coefficient of the segmented bone was significantly higher in pCT Com (0.90 ± 0.02) than in pCT NetWH (0.86 ± 0.04, P < 0.0001), pCT NetBA (0.88 ± 0.03, P < 0.0001), and inverted MR (0.71 ± 0.09, P < 0.0001). Dice similarity coefficient from pCT Com demonstrated significantly reduced age dependence than inverted MRI. Furthermore, pCT Com provided excellent suture and fracture visibility comparable to CT., Conclusion: MR high-resolution pediatric cranial bone imaging may facilitate the clinical translation of a radiation-free MR cranial bone imaging method for pediatric patients., (© 2022 International Society for Magnetic Resonance in Medicine.)

The purpose of this article is to give a summary of the progress of magnetic resonance imaging (MRI) in radiotherapy. MRI is an important imaging modality for treatment planning in radiotherapy. However, the registration step with the simulation scanner can be a source of errors, motivating the implementation of all-MRI simulation methods and new accelerators coupled with on-board MRI. First, practical MRI imaging for radiotherapy is detailed, but also the importance of a coherent imaging workflow incorporating all imaging modalities. Second, future evolutions and research domains such as quantitative imaging biomarkers, MRI-only pseudo computed tomography and radiomics are discussed. Finally, the application of MRI during radiotherapy treatment is reviewed: the use of MR-linear accelerators. MRI is increasingly integrated into radiotherapy. Advances in diagnostic imaging can thus benefit radiotherapy, but specific radiotherapy constraints lead to additional challenges and require close collaboration between radiologists, radiation oncologists, technologists and physicists. The integration of quantitative imaging biomarkers in the radiotherapy process will result in mutual benefit for diagnostic imaging and radiotherapy. MRI-guided radiotherapy has already been used for several years in clinical routine. Abdominopelvic neoplasias (pancreas, liver, prostate) are the preferred locations for treatment because of their favourable contrast in MRI, their movement during irradiation and their proximity to organs at risk of radiation exposure, making the tracking and daily adaptation of the plan essential. MRI has emerged as an increasingly necessary imaging modality for radiotherapy planning. Inclusion of patients in clinical trials evaluating new MRI-guided radiotherapy techniques and associated quantitative imaging biomarkers will be necessary to assess the benefits., (Copyright © 2022 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, potentially introducing bias in the reconstructed PET images. The aims of this work were to develop deep learning-based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3-dimensional CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semiautomated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning-, model-, and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semiautomated segmentations, with a mean Dice similarity coefficient of 0.75. The volumetric similarity score between 2 segmentations was 0.85 ± 0.14. The mean absolute relative changes with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning-based and model-based μ-maps, respectively. The average relative change between PET images reconstructed with deep learning-based and CT-based μ-maps was 2.6%. Conclusion: We developed a deep learning-based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images, with accuracy comparable to that of semiautomatic segmentations. The μ-maps synthesized using a deep learning-based method from CAIPIRINHA-accelerated Dixon images were more accurate than those generated with the model-based approach available on integrated PET/MRI scanners., (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)

Objective. A multi-discriminator-based cycle generative adversarial network (MD-CycleGAN) model is proposed to synthesize higher-quality pseudo-CT from MRI images. Approach. MRI and CT images obtained at the simulation stage with cervical cancer were selected to train the model. The generator adopted DenseNet as the main architecture. The local and global discriminators based on a convolutional neural network jointly discriminated the authenticity of the input image data. In the testing phase, the model was verified by a fourfold cross-validation method. In the prediction stage, the data were selected to evaluate the accuracy of the pseudo-CT in anatomy and dosimetry, and they were compared with the pseudo-CT synthesized by GAN with the generator based on the architectures of ResNet, sUNet, and FCN. Main results. There are significant differences ( P  < 0.05) in the fourfold cross-validation results on the peak signal-to-noise ratio and structural similarity index metrics between the pseudo-CT obtained based on MD-CycleGAN and the ground truth CT (CT gt ). The pseudo-CT synthesized by MD-CycleGAN had closer anatomical information to the CT gt with a root mean square error of 47.83 ± 2.92 HU, a normalized mutual information value of 0.9014 ± 0.0212, and a mean absolute error value of 46.79 ± 2.76 HU. The differences in dose distribution between the pseudo-CT obtained by MD-CycleGAN and the CT gt were minimal. The mean absolute dose errors of Dose max , Dose min , and Dose mean based on the planning target volume were used to evaluate the dose uncertainty of the four pseudo-CT. The u-values of the Wilcoxon test were 55.407, 41.82, and 56.208, and the differences were statistically significant. The 2%/2 mm-based gamma pass rate (%) of the proposed method was 95.45 ± 1.91, and the comparison methods (ResNet&#95;GAN, sUnet&#95;GAN, and FCN&#95;GAN) were 93.33 ± 1.20, 89.64 ± 1.63, and 87.31 ± 1.94, respectively. Significance. The pseudo-CT images obtained based on MD-CycleGAN have higher imaging quality and are closer to the CT gt in terms of anatomy and dosimetry than other GAN models., (© 2022 Institute of Physics and Engineering in Medicine.)

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCT BD1 ); (2) volumes for sCT BD1 plus chest wall and ipsilateral breast (sCT BD2 ); (3) volumes for sCT BD2 plus ribs (sCT BD3 ); and a deep learning-generated sCT (sCT DL ) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCT BD1 /sCT BD2 /sCT BD3 /sCT DL respectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTV GTV was 0.15/0.00/0.00/-0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCT DL . Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCT BD2 showed the optimal number of bulk-density levels for a bulk-density approach., (© 2021 Institute of Physics and Engineering in Medicine.)

Aim: The aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning., Background: Despite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan., Materials and Methods: In this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated., Results: The MAE of a range of 45-55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4% based on the polar coordinate feature and proposed polynomial regression model., Conclusion: The proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria., (© 2020 Greater Poland Cancer Centre. Published by Elsevier B.V. All rights reserved.)

Purpose: In context of head-and-neck radiotherapy, this study aims to compare MR image quality according to diagnostic (DIAG) and radiotherapy (RT) setups; and to optimise an MRI-protocol (including 3D T 1 and T 2 -weighted sequences) for dose-planning (based on pseudo-CT generation)., Materials and Methods: To compare DIAG and RT setups, signal-to-noise-ratio (SNR) and percentage-image-uniformity (PIU) were computed on T 1 images of phantoms and volunteers. Influence of the sample conductivity on SNR was quantified using homemade phantoms. To obtain reliable T 1 and T 2 images for RT-planning, an experimental design was performed on volunteers by using SNR, contrast-to-noise-ratio (CNR) and mean-opinion-score (MOS). Further, pseudo-CTs were generated from 8 patients T 2 images with a state-of-art deep-learning method. These pseudo-CTs were evaluated by mean-absolute-error (MAE) and mean-error (ME)., Results: SNR was higher for DIAG-setup compared to RT-setup (SNR-ratio=1.3). A clear influence of the conductivity on SNR was observed. PIU was higher for DIAG-setup (38.8%) compared to RT-setup (33.5%). Regarding the protocol optimisation, SNR, CNR, and MOS were 20.6, 6.16, and 3.91 for the optimal T 1 sequence. For the optimal T 2 sequence, SNR, CNR and MOS were 25.6, 44.46 and 4.0. In the whole head-and-neck area, the mean MAE and ME of the pseudo-CTs were 82.8 and -3.9 HU., Conclusion: We quantified the image quality decrease induces by using an RT-setup for head-and-neck radiotherapy. To compensate this decrease, an MRI protocol was optimised by using an experimental design. This protocol of 15minutes provides accurate images which could be used for MRI-dose-planning in clinical practice., (Copyright © 2020 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

The generation of accurate attenuation correction (AC) maps is a basic step to allow for quantitative PET/MR imaging. However, generating MR-based AC maps is a challenge because there is no direct relationship between the PET attenuation coefficients (μ) and the intensity of the MR signal, contrary to what happens with the intensity of CT images. In fact, ignoring the bone causes a distorted and biased distribution of the calculated SUV values. To solve this problem, several MR-based AC methods have been proposed in the literature. In this paper we describe how these methods work, and the challenge they faced to translate into full body applications. Currently, in research environments, the accuracy of AC methods is no longer a limiting factor to solve in order to carry out quantitative in vivo molecular imaging studies. However, many of these methods present a series of limitations for their real implementation in the clinical practice due to insufficient clinical validation and the difficulty of their implementation in a real environment (as described in the examples of clinical applications). Thus, we need the PET/MR community to work on the standardization of the use and assessment of different AC methods. In this scenario, the opening and access by vendors to the implementation of new AC methods in their PET/MR scanners plays a crucial role., (Copyright © 2020 Sociedad Española de Medicina Nuclear e Imagen Molecular. Publicado por Elsevier España, S.L.U. All rights reserved.)

Purpose: To develop and evaluate a novel method for pseudo-CT generation from multi-parametric MR images using multi-channel multi-path generative adversarial network (MCMP-GAN)., Methods: Pre- and post-contrast T1-weighted (T1-w), T2-weighted (T2-w) MRI, and treatment planning CT images of 32 nasopharyngeal carcinoma (NPC) patients were employed to train a pixel-to-pixel MCMP-GAN. The network was developed based on a 5-level Residual U-Net (ResU-Net) with the channel-based independent feature extraction network to generate pseudo-CT images from multi-parametric MR images. The discriminator with five convolutional layers was added to distinguish between the real CT and pseudo-CT images, improving the nonlinearity and prediction accuracy of the model. Eightfold cross validation was implemented to validate the proposed MCMP-GAN. The pseudo-CT images were evaluated against the corresponding planning CT images based on mean absolute error (MAE), peak signal-to-noise ratio (PSNR), Dice similarity coefficient (DSC), and Structural similarity index (SSIM). Similar comparisons were also performed against the multi-channel single-path GAN (MCSP-GAN), the single-channel single-path GAN (SCSP-GAN)., Results: It took approximately 20 h to train the MCMP-GAN model on a Quadro P6000, and less than 10 s to generate all pseudo-CT images for the subjects in the test set. The average head MAE between pseudo-CT and planning CT was 75.7 ± 14.6 Hounsfield Units (HU) for MCMP-GAN, significantly (P-values < 0.05) lower than that for MCSP-GAN (79.2 ± 13.0 HU) and SCSP-GAN (85.8 ± 14.3 HU). For bone only, the MCMP-GAN yielded a smaller mean MAE (194.6 ± 38.9 HU) than MCSP-GAN (203.7 ± 33.1 HU), SCSP-GAN (227.0 ± 36.7 HU). The average PSNR of MCMP-GAN (29.1 ± 1.6) was found to be higher than that of MCSP-GAN (28.8 ± 1.2) and SCSP-GAN (28.2 ± 1.3). In terms of metrics for image similarity, MCMP-GAN achieved the highest SSIM (0.92 ± 0.02) but did not show significantly improved bone DSC results in comparison with MCSP-GAN., Conclusions: We developed a novel multi-channel GAN approach for generating pseudo-CT from multi-parametric MR images. Our preliminary results in NPC patients showed that the MCMP-GAN method performed apparently superior to the U-Net-GAN and SCSP-GAN, and slightly better than MCSP-GAN., (© 2020 American Association of Physicists in Medicine.)

Background: To study the effects of different CT value assignment methods on the dose calculations in radiotherapy plans for brain metastases, this study will provide a reference for radiotherapy planning design based on MR images., Methods: All fifty recruited patients underwent CT and MR simulated localization the same day as, but prior to, three dimensional conformal radiation therapy (3D-CRT) or intensity modulated radiation therapy (IMRT) for brain metastases. After rigid registration of both the CT and MR images, the main tissues and organs were delineated on the CT and MR images. The average CT value of each tissue or organ was calculated. Three groups of pseudo-CT were generated by three CT value assignment methods: (I) the whole tissue was assigned 140 HU; (II) cavity, bone and other tissues were assigned -700, 700 and 20 HU, respectively; (III) tissue- and organ-specific CT values were given. The dose distribution was recalculated based on the three groups of pseudo-CT to obtain Plan2, Plan3 and Plan4, accordingly. The resultant radiotherapy plans were considered the original plan (Plan1). Then, the dosimetric differences between these three plans and Plan1 were compared., Results: The average pseudo-CT values of bone and cavity were 731.7±69.3 and -725.5±26.1 HU, respectively. The range of average soft-tissue CT values was from -70 to 70 HU. The dose distribution between Plan1 and Plan2, Plan3 or Plan4 showed some differences, and the differences decreased in turn. The differences in the maximum dose of the lenses can reach 5.0%, 1.5% and 1.2%, respectively, while the differences in other dose parameters (maximum dose, mean dose and D 98% to the PTV, D 5% of the brainstem, and maximum dose of the brainstem, corpus callosum, left eye, right eye) were basically less than 2.0%, 1.2% and 0.8%, respectively. This shows that in the CT value assignment method, the dose calculation error can be greatly reduced by assigning the value to the bone and cavity separately, and if the different soft tissues are distinguished, the error of the dose calculation can be further reduced by more than 30%. In the pixel-by-pixel dosimetric comparison, the areas of more than 1% dose difference between Plan1 and Plan3 as well as Plan4 were mainly distributed near skin while those between Plan1 and Plan2 were mainly distributed at the bone, cavity, bone and soft tissues junction, and the skin near the field., Conclusions: In summary, a scheme for assigning specific CT values to MRI-based radiotherapy is established. The scheme will provide patients with a dose-free radiotherapy plan. Through the calculation of the differences between the new plans and the old plan, it is found that our scheme can basically control the dose error below 0.8% to meet the clinical requirements., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr.2019.12.54). The authors have no conflicts of interest to declare., (2020 Translational Cancer Research. All rights reserved.)

Objective: To propose a hybrid multiatlas fusion and correction approach to estimate a pseudo-computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy., Materials and Methods: A set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image., Results: The proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement., Conclusion: A hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images., (Copyright © 2019. Published by Elsevier Inc.)

Purpose: This project investigates the feasibility of implementation of MRI-only prostate planning in a prospective multi-center study. Method and Materials: A two-phase implementation model was utilized where centers performed retrospective analysis of MRI-only plans for five patients followed by prospective MRI-only planning for subsequent patients. Feasibility was assessed if at least 23/25 patients recruited to phase 2 received MRI-only treatment workflow. Whole-pelvic MRI scans (T2 weighted, isotropic 1.6 mm voxel 3D sequence) were converted to pseudo-CT using an established atlas-based method. Dose plans were generated using MRI contoured anatomy with pseudo-CT for dose calculation. A conventional CT scan was acquired subsequent to MRI-only plan approval for quality assurance purposes (QA-CT). 3D Gamma evaluation was performed between pseudo-CT calculated plan dose and recalculation on QA-CT. Criteria was 2%, 2 mm criteria with 20% low dose threshold. Gold fiducial marker positions for image guidance were compared between pseudo-CT and QA-CT scan prior to treatment. Results: All 25 patients recruited to phase 2 were treated using the MRI-only workflow. Isocenter dose differences between pseudo-CT and QA-CT were -0.04 ± 0.93% (mean ± SD). 3D Gamma dose comparison pass-rates were 99.7% ± 0.5% with mean gamma 0.22 ± 0.07. Results were similar for the two centers using two different scanners. All gamma comparisons exceeded the 90% pass-rate tolerance with a minimum gamma pass-rate of 98.0%. In all cases the gold fiducial markers were correctly identified on MRI and the distances of all seeds to centroid were within the tolerance of 1.0 mm of the distances on QA-CT (0.07 ± 0.41 mm), with a root-mean-square difference of 0.42 mm. Conclusion: The results support the hypothesis that an MRI-only prostate workflow can be implemented safely and accurately with appropriate quality assurance methods.

Recently, an exhaustive examination of 11 state of the art MR-based attenuation correction (AC) concluded that there are currently a few methods showing similar results compared to the gold-standard, CT-based AC. While the study presented a thorough portfolio of metrics to quantify accuracy (bias) and quality, it lacked one of the most important metrics to quantify robustness that is critical for its clinical applicability: intrascanner reproducibility (repeatability). In this work, we provide for the first time a study of the repeatability of one of the outperforming brain MR-based AC methods: the SPM-based pseudo-CT approach. 22 subjects undergoing 3 18 F-FDG PET/MRI visits within 2 months were retrospectively analyzed in this study. Pseudo-CT mu-maps were obtained from the coregistered MR images for all 3 visits and the PET data from visit 1 was reconstructed using all three mu-maps. Relative changes (RC), Intraclass correlation coefficient (ICC), Reproducibility coefficient (RDC 95% ) and Bland-Altman Limits of Agreement (LoA) were used to measure repeatability. Voxel-based and ROI-based results showed that absolute RC for the reconstructed PET images are within ~2%. The brain cortex and the cerebellum were the regions with the largest variability (~3%). The differences across visits were not statistically significant (p=0.90). In conclusion this study shows for the first time the repeatability of the SPM-based pseudo-CT approach for brain MR-AC. These results, in addition to the ease of implementation and the quality and robustness previously demonstrated, confer this SPM-based method an ideal candidate for routine brain PET/MRI research and clinical studies.

Whole-body attenuation correction (AC) is still challenging in combined PET/MR scanners. We describe Dixon-VIBE Deep Learning (DIVIDE), a deep-learning network that allows synthesizing pelvis pseudo-CT maps based only on the standard Dixon volumetric interpolated breath-hold examination (Dixon-VIBE) images currently acquired for AC in some commercial scanners. Methods: We propose a network that maps between the four 2-dimensional (2D) Dixon MR images (water, fat, in-phase, and out-of-phase) and their corresponding 2D CT image. In contrast to previous methods, we used transposed convolutions to learn the up-sampling parameters, we used whole 2D slices to provide context information, and we pretrained the network with brain images. Twenty-eight datasets obtained from 19 patients who underwent PET/CT and PET/MR examinations were used to evaluate the proposed method. We assessed the accuracy of the μ-maps and reconstructed PET images by performing voxel- and region-based analysis comparing the SUVs (in g/mL) obtained after AC using the Dixon-VIBE (PET Dixon ), DIVIDE (PET DIVIDE ), and CT-based (PET CT ) methods. Additionally, the bias in quantification was estimated in synthetic lesions defined in the prostate, rectum, pelvis, and spine. Results: Absolute mean relative change values relative to CT AC were lower than 2% on average for the DIVIDE method in every region of interest except for bone tissue, where it was lower than 4% and 6.75 times smaller than the relative change of the Dixon method. There was an excellent voxel-by-voxel correlation between PET CT and PET DIVIDE ( R 2 = 0.9998, P < 0.01). The Bland-Altman plot between PET CT and PET DIVIDE showed that the average of the differences and the variability were lower (mean PET CT -PET DIVIDE SUV, 0.0003; PET CT -PET DIVIDE SD, 0.0094; 95% confidence interval, [-0.0180,0.0188]) than the average of differences between PET CT and PET Dixon (mean PET CT -PET Dixon SUV, 0.0006; PET CT -PET Dixon The DIVIDE method can accurately synthesize a pelvis pseudo-CT scan from standard Dixon-VIBE images, allowing for accurate AC in combined PET/MR scanners. Additionally, our implementation allows rapid pseudo-CT synthesis, making it suitable for routine applications and even allowing retrospective processing of Dixon-VIBE data.Conclusion: The DIVIDE method can accurately synthesize a pelvis pseudo-CT scan from standard Dixon-VIBE images, allowing for accurate AC in combined PET/MR scanners. Additionally, our implementation allows rapid pseudo-CT synthesis, making it suitable for routine applications and even allowing retrospective processing of Dixon-VIBE data., (© 2019 by the Society of Nuclear Medicine and Molecular Imaging.)

The purpose of this study is to create a new pseudo-computed tomography (CT) imaging approach under superposed ultrasound (US) deformation fields based on step-by-step local registration. Scanned CT and US 3D image datasets of three patients with postoperative cervical carcinoma were selected, including CT (CTsim) and US images (USsim) acquired during simulated positioning process and cone beam CT (CBCT) and US images for positioning verification (USpv) acquired after treatment for 10 times. Regions of interest such as urinary bladders were segmented out and accepted local registration to obtain different deformation fields. These deformation fields were successively performed according to their order and then applied to localized CT images to obtain pseudo-CT (CTps). After filtering, we obtained the final correct pseudo-CT (CTpsf). The pseudo-CT based on the mask of the whole imaging region of US images (WCTps) were acquired as control. Then, we compared CTpsf, CTps, WCTps, and CBCT in terms of their similarity in anatomical structure and differences in pseudo-CT and CTsim in terms of dosimetry. Structural similarity degree between CTpsf and CBCT was larger compared with that between CTps and WCTps. Target regions and dosages of endangered organs between CTpsf and CTsim were different under the same calculation conditions based on the Monte Carlo algorithm. Compared with the VMAT plan of CTsim, the pass rate of CTpsf in γ analysis under the standards of 2% dosage difference and 2-mm distance difference was 91.8%. The imaging quality of CTpsf was better compared with WCTps and CTps. It exhibited high similarity with CBCT in anatomical structure and had favorable application prospect in adaptive radiotherapy. Graphical abstract The local deformation registration is performed between the ultrasound images based on different regions of interest, and then stepwise applied to localized CT images to obtain pseudo-CT. After filtering, the corrected pseudo CT image is obtained.

Purpose: To describe a method for converting Zero TE (ZTE) MR images into X-ray attenuation information in the form of pseudo-CT images and demonstrate its performance for (1) attenuation correction (AC) in PET/MR and (2) dose planning in MR-guided radiation therapy planning (RTP)., Methods: Proton density-weighted ZTE images were acquired as input for MR-based pseudo-CT conversion, providing (1) efficient capture of short-lived bone signals, (2) flat soft-tissue contrast, and (3) fast and robust 3D MR imaging. After bias correction and normalization, the images were segmented into bone, soft-tissue, and air by means of thresholding and morphological refinements. Fixed Hounsfield replacement values were assigned for air (-1000 HU) and soft-tissue (+42 HU), whereas continuous linear mapping was used for bone., Results: The obtained ZTE-derived pseudo-CT images accurately resembled the true CT images (i.e., Dice coefficient for bone overlap of 0.73 ± 0.08 and mean absolute error of 123 ± 25 HU evaluated over the whole head, including errors from residual registration mismatches in the neck and mouth regions). The linear bone mapping accounted for bone density variations. Averaged across five patients, ZTE-based AC demonstrated a PET error of -0.04 ± 1.68% relative to CT-based AC. Similarly, for RTP assessed in eight patients, the absolute dose difference over the target volume was found to be 0.23 ± 0.42%., Conclusion: The described method enables MR to pseudo-CT image conversion for the head in an accurate, robust, and fast manner without relying on anatomical prior knowledge. Potential applications include PET/MR-AC, and MR-guided RTP., (© 2018 International Society for Magnetic Resonance in Medicine.)

Background: One of the challenges of PET/MRI combined systems is to derive an attenuation map to correct the PET image. For that, the pseudo-CT image could be used to correct the attenuation. Until now, most existing scientific researches construct this pseudo-CT image using the registration techniques. However, these techniques suffer from the local minima of the non-rigid deformation energy function which leads to unsatisfactory results., Objective: We propose in this paper a new approach for the generation of a pseudo-CT image from an MR image., Materials and Methods: This approach is based on a dense stereo matching concept, for that, we encode each pixel according to a shape related coordinates method, and we apply a local texture descriptor to put into correspondence pixels between MRI patient and MRI atlas images. The proposed approach was tested on a real MRI data, and in order to show the effectiveness of the proposed local descriptor, it has been compared to three other local descriptors: SIFT, SURF and DAISY. Also it was compared to registration method., Results: The calculation of structural similarity (SSIM) index and DICE coefficients, between the pseudo-CT image and the corresponding real CT image show that the proposed stereo matching approach outperforms a registration one., Conclusion: The use of dense matching with atlas promises good results in the creation of pseudo-CT. The proposed approach can be recommended as an alternative to registration techniques.

Using magnetic resonance imaging (MRI) as the sole imaging modality for patient modeling in radiation therapy (RT) is a challenging task due to the need to derive electron density information from MRI and construct a so-called pseudo-computed tomography (pCT) image. We have previously published a new method to derive pCT images from head T1-weighted (T1-w) MR images using a single-atlas propagation scheme followed by a post hoc correction of the mapped CT numbers using local intensity information. The purpose of this study was to investigate the performance of our method with head zero echo time (ZTE) MR images. To evaluate results, the mean absolute error in bins of 20 HU was calculated with respect to the true planning CT scan of the patient. We demonstrated that applying our method using ZTE MR images instead of T1-w improved the correctness of the pCT in case of bone resection surgery prior to RT (that is, an example of large anatomical difference between the atlas and the patient)., (Copyright © 2017. Published by Elsevier Ltd.)

Purpose: To characterize the spectral parameters of tissues with high spectral and spatial resolution magnetic resonance images to be used as a foundation for a classification-based synthetic CT algorithm., Methods: A phantom was constructed consisting of a section of fresh beef leg with bone embedded in 1% agarose gel. The high spectral and spatial (HiSS) resolution MR imaging sequence used had 1.0 mm in-plane resolution and 11.1 Hz spectral resolution. This sequence was used to image the phantom and one patient. Post-processing was performed off-line with IDL and included Fourier transformation of the time-domain data, labeling of fat and water peaks, and fitting the magnitude spectra with Lorentzian functions. Images of the peak height and peak integral of both the water and fat resonances were generated and analyzed. Several regions-of-interest (ROIs) were identified in phantom: bone marrow, cortical bone, adipose tissue, muscle, agar gel, and air; in the patient, no agar gel was present but an ROI of saline in the bladder was analyzed. All spectra were normalized by the noise within each voxel; thus, all parameters are reported in terms of signal-to-noise (SNR). The distributions of tissue spectral parameters were analyzed and scatterplots generated. Water peak height in cortical bone was compared to air using a nonparametric t-test. Composition of the various ROIs in terms of water, fat, or fat and water was also reported., Results: In phantom, the scatterplot of peak height (water versus fat) showed good separation of bone marrow and adipose tissue. Water versus fat integral scatterplot showed better separation of muscle and cortical bone than the peak height scatterplot. In the patient data, the distributions of water and fat peak heights were similar to that in phantom, with more overlap of bone marrow and cortical bone than observed in phantom. The relationship between bone marrow and cortical bone for peak integral was better separated than those of peak heights in the patient data. For both the phantom and patient, there was a significant amount of overlap in spectral parameters of cortical bone versus air., Conclusion: These results show promising results for utilizing HiSS imaging in a classification-based synthetic CT algorithm. Cortical bone and air overlap was expected due to the short T2* of bone; reducing early echo times would improve the SNR in bone and image data from these early echoes could help differentiate these tissue types. Further studies need to be done with the goal of better separation of air and bone, and to extend the concept to volumetric imaging before it can be clinically applied., (© 2017 American Association of Physicists in Medicine.)

Unlabelled: Attenuation correction in hybrid PET/MR scanners is still a challenging task. This paper describes a methodology for synthesizing a pseudo-CT volume from a single T1-weighted volume, thus allowing us to create accurate attenuation correction maps., Methods: We propose a fast pseudo-CT volume generation from a patient-specific MR T1-weighted image using a groupwise patch-based approach and an MRI-CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel to the patches of all MR images in the database that lie in a certain anatomic neighborhood. The pseudo-CT volume is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a graphical processing unit (GPU)., Results: We evaluated our method both qualitatively and quantitatively for PET/MR correction. The approach performed successfully in all cases considered. We compared the SUVs of the PET image obtained after attenuation correction using the patient-specific CT volume and using the corresponding computed pseudo-CT volume. The patient-specific correlation between SUV obtained with both methods was high (R(2) = 0.9980, P < 0.0001), and the Bland-Altman test showed that the average of the differences was low (0.0006 ± 0.0594). A region-of-interest analysis was also performed. The correlation between SUVmean and SUVmax for every region was high (R(2) = 0.9989, P < 0.0001, and R(2) = 0.9904, P < 0.0001, respectively)., Conclusion: The results indicate that our method can accurately approximate the patient-specific CT volume and serves as a potential solution for accurate attenuation correction in hybrid PET/MR systems. The quality of the corrected PET scan using our pseudo-CT volume is comparable to having acquired a patient-specific CT scan, thus improving the results obtained with the ultrashort-echo-time-based attenuation correction maps currently used in the scanner. The GPU implementation substantially decreases computational time, making the approach suitable for real applications., (© 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)