Search

Abstract 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 (

In the field of knee imaging, the incorporation of MR-based pseudo-CT synthesis holds the potential to mitigate the need for separate CT scans, simplifying workflows, enhancing patient comfort, and reducing radiation exposure. In this work, we present a novel DL framework, grounded in the development of the Cycle-Consistent Generative Adversarial Network (CycleGAN) method, tailored specifically for the synthesis of pseudo-CT images in knee imaging to surmount the limitations of current methods. Upon visually examining the outcomes, it is evident that the synthesized pseudo-CTs show an excellent quality and high robustness. Despite the limited dataset employed, the method is able to capture the particularities of the bone contours in the resulting image. The experimental Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Zero-Normalized Cross Correlation (ZNCC), Mutual Information (MI), Relative Change (RC), and absolute Relative Change (|RC|) report values of 30.4638 ± 7.4770, 28.1168 ± 1.5245, 0.9230 ± 0.0217, 0.9807 ± 0.0071, 0.8548 ± 0.1019, 0.0055 ± 0.0265, and 0.0302 ± 0.0218 (median ± median absolute deviation), respectively. The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield units (m = 0.9785; adjusted R2 = 0.9988; ρ = 0.9849; p < 0.001). The Bland–Altman plot shows that the average of the differences is low ((HUCT−HUpseudo−CT = 0.7199 ± 35.2490; 95% confidence interval [−68.3681, 69.8079]). This study represents the first reported effort in the field of MR-based knee pseudo-CT synthesis, shedding light to significantly advance the field of knee imaging.

Purpose/objectivesAn 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 methodIn 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.ResultsThe 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.ConclusionThis 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.

Background: Magnetic Resonance Imaging (MRI) applications offer superior soft tissue contrast compared with computed tomography (CT) for accurate radiotherapy planning. Although, MRI images suffer from poor image quality and lack electron density for radiation dose calculation. The present study aims to use the deep learning (DL) approach to 1) enhance the quality of MRI images and 2) generate synthetic CT images using MRI images for more accurate radiotherapy planning. Methods: In this paper, the pix2pix Generative Adversarial Network was utilized to synthesize CT images from noisy MRI images of 20 arbitrarily patients with brain disease. The standard statistical measurements investigated the accuracy comparison of the modeled Hounsfield unit (HU) value from MRI images and referenced CT of each patient. The famous quality metrics that were used to compare synthetic CTs and referenced CTs were the mean absolute error (MAE), the structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR(. Results: The higher quality measurements between the synthetic pseudo-CT and the referenced CT images as PSNR, and SSIM, should correlate to the lower MAE value. For the overall brain among blind test data, the measured peak signal-to-noise ratio, mean absolute error, and structural similarity index values were about 16.5, 28.13, and 93.46, respectively. Conclusion: The proposed method provides an acceptable level of statistical measurements computed on the Pseudo-CT and referenced CT, and it could be concluded that the p-CT can be implemented in radiotherapy treatment planning with acceptable accuracy.

In recent years, research has been showing a great interest in using magnetic resonance imaging (MRI) as the only modality for radiation therapy (RT) for its superior soft-tissue visualisation and non-ionizing proprieties. Furthermore, MRI-only RT would be of great benefit to eliminate image registration errors, reduce cost and workload. In addition, machine-learning algorithms have been taking the lead in many fields. For instance, in MRI-only RT, machine learning is showing a notable performance compared to other methods owed to the flexibility of these methods towards data regardless of model complexity. In this paper, we present an ensemble learning approach with stacked generalisation to simulate a CT scan from multi-modal MR images from which patch-based shape, texture and spatial features were considered. Feature extraction, fusion and reduction were performed to get the most descriptive and informative features. The ensemble learning model was constructed with two levels of learning were the basic level consisted of three base learners namely: artificial neural networks (ANN), random forests (RF) and k-nearest neighbours (kNN) and the second level representing the stacking learner that takes predictions from the base learner and generates the final predictions. Multiple linear regression (MLR) was used for the stacked generalisation. The proposed ensemble learning with stacked generalisation (ES) approach produced an average mean absolute error (MAE) of 87.60 ± 19.70 and an average mean error (ME) of −4.68 ± 16.43 outperforming the RF method, which produced an average MAE of 106.88 ± 33.20 and an average ME of −5.38 ± 20.77. In addition, average Pearson correlation was 0.92 for the proposed approach compared to 0.89 for RF. Evaluation of the proposed approach shows that stacked generalisation can greatly improve prediction accuracy and reduce bias in electron density estimation.

This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy.

Magnetic resonance (MR) images have distinctive advantages in radiation treatment (RT) planning due to their superior, anatomic and functional information compared with computed tomography (CT). For the RT dose calculation, MR images cannot be directly used because of the lack of electron density information. To address this issue, we propose to generate pseudo-CT (pCT) in terms of multiple matching Dixon MR images to support MR-only RT, particularly in the challenging body section of the abdomen. To this end, we design the dedicated multichannel residual conditional generative adversarial network (MCRCGAN). The significance of our efforts is three-fold: 1) The MCRCGAN organically incorporates multiple theories and techniques, such as multichannel residual network (ResNet) and conditional generative adversarial network (cGAN), which facilitate its more authentic pCT generation than many existing methods. 2) The usage of residual modules effectively deepens the network without performance degradation, and the multichannel ResNet helps to simultaneously capture the substance of images, as extensively as possible, which is implicitly contained in the multiple different MR images of the same subject. 3) Due to the designed dedicated network structure, the MCRCGAN is capable of generating satisfactory pCTs under the condition of limited training data as well as prompt prediction response. Experimental studies on ten patients' paired MR-CT images demonstrate the effectiveness of our proposed MCRCGAN model on both the pCT generation quality and the performance stability.

In this study, an anatomic semantic guided neural style transfer (ASGNST) algorithm was developed and pseudo-computed tomography (CT) images synthesized in steps. CT images and ultrasound (US) images of 20 cervical cancer patients to be treated were selected. The foreground (FG) and background (BG) regions of the US images were segmented by the region growth method, and three objective functions for content, style, and contour loss were defined. Based on the two types of regions, a local pseudo-CT image synthesis model based on a convolution neural network was established. Then, global 2D pseudo-CT images were obtained using the weighted average fusing algorithm, and the final pseudo-CT images were obtained through 3D reconstruction. US phantom and data of five additional cervical cancer patients were used for prediction. Furthermore, three image synthesis algorithms-global deformation field (GDF), stepwise local deformation field (SLDF), and neural style transfer (NST)-were selected for comparative verification. The pseudo-CT images synthesized by the four algorithms were compared with the ground-truth CT images obtained during treatment. The structural similarity index between the ground-truth CT and pseudo-CT synthesized by the improved algorithm significantly differed from those synthesized by the other three algorithms (tGDF_bg = 7.175, tSLDF_bg = 4.513, tNST_bg = 3.228, tGDF_fg = 10.518, tSLDF_fg = 5.522, tNST_fg = 2.869, p

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.

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.

Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512); the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87; adjusted R2 = 0.91; p < 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100; 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.