Purpose
To study the influence of gradient echo–based contrasts as input channels to a 3D patch‐based neural network trained for synthetic CT (sCT) generation in canine and human populations.
Methods
...Magnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired using nonrigid registration. Magnitude MR images and Dixon reconstructed water, fat, in‐phase and opposed‐phase images were obtained from a single T1‐weighted multi‐echo gradient‐echo acquisition. From this set, 6 input configurations were defined, each containing 1 to 4 MR images regarded as input channels. For each configuration, a UNet‐derived deep learning model was trained for synthetic CT generation. Reconstructed Hounsfield unit maps were evaluated with peak SNR, mean absolute error, and mean error. Dice similarity coefficient and surface distance maps assessed the geometric fidelity of bones. Repeatability was estimated by replicating the training up to 10 times.
Results
Seventeen canines and 23 human subjects were included in the study. Performance and repeatability of single‐channel models were dependent on the TE‐related water–fat interference with variations of up to 17% in mean absolute error, and variations of up to 28% specifically in bones. Repeatability, Dice similarity coefficient, and mean absolute error were statistically significantly better in multichannel models with mean absolute error ranging from 33 to 40 Hounsfield units in humans and from 35 to 47 Hounsfield units in canines.
Conclusion
Significant differences in performance and robustness of deep learning models for synthetic CT generation were observed depending on the input. In‐phase images outperformed opposed‐phase images, and Dixon reconstructed multichannel inputs outperformed single‐channel inputs.
Purpose
To demonstrate that mapping pelvis conductivity at 3T with deep learning (DL) is feasible.
Methods
210 dielectric pelvic models were generated based on CT scans of 42 cervical cancer ...patients. For all dielectric models, electromagnetic and MR simulations with realistic accuracy and precision were performed to obtain
B1+ and transceive phase (ϕ±). Simulated
B1+ and ϕ± served as input to a 3D patch‐based convolutional neural network, which was trained in a supervised fashion to retrieve the conductivity. The same network architecture was retrained using only ϕ± in input. Both network configurations were tested on simulated MR data and their conductivity reconstruction accuracy and precision were assessed. Furthermore, both network configurations were used to reconstruct conductivity maps from a healthy volunteer and two cervical cancer patients. DL‐based conductivity was compared in vivo and in silico to Helmholtz‐based (H‐EPT) conductivity.
Results
Conductivity maps obtained from both network configurations were comparable. Accuracy was assessed by mean error (ME) with respect to ground truth conductivity. On average, ME < 0.1 Sm−1 for all tissues. Maximum MEs were 0.2 Sm−1 for muscle and tumour, and 0.4 Sm−1 for bladder. Precision was indicated with the difference between 90th and 10th conductivity percentiles, and was below 0.1 Sm−1 for fat, bone and muscle, 0.2 Sm−1 for tumour and 0.3 Sm−1 for bladder. In vivo, DL‐based conductivity had median values in agreement with H‐EPT values, but a higher precision.
Conclusion
Anatomically detailed, noise‐robust 3D conductivity maps with good sensitivity to tissue conductivity variations were reconstructed in the pelvis with DL.
The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of ...linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.
Purpose
To demonstrate the feasibility and robustness of the Magnetic Resonance Spin TomogrAphy in Time‐domain (MR‐STAT) framework for fast, high SNR relaxometry at 7T.
Methods
To deploy MR‐STAT on ...7T‐systems, we designed optimized flip‐angles using the BLAKJac‐framework that incorporates the SAR‐constraints. Transmit RF‐inhomogeneities were mitigated by including a measured B1+$$ {B}_1^{+} $$‐map in the reconstruction. Experiments were performed on a gel‐phantom and on five volunteers to explore the robustness of the sequence and its sensitivity to B1+$$ {B}_1^{+} $$ inhomogeneities. The SNR‐gain at 7T was explored by comparing phantom and in vivo results to MR‐STAT at 3T in terms of SNR‐efficiency.
Results
The higher SNR at 7T enabled two‐fold acceleration with respect to current 2D MR‐STAT protocols at lower field strengths. The resulting scan had whole‐brain coverage, with 1 x 1 x 3 mm3 resolution (1.5 mm slice‐gap) and was acquired within 3 min including the B1+$$ {B}_1^{+} $$‐mapping. After B1+$$ {B}_1^{+} $$‐correction, the estimated T1 and T2 in a phantom showed a mean relative error of, respectively, 1.7% and 4.4%. In vivo, the estimated T1 and T2 in gray and white matter corresponded to the range of values reported in literature with a variation over the subjects of 1.0%–2.1% (WM‐GM) for T1 and 4.3%–5.3% (WM‐GM) for T2. We measured a higher SNR‐efficiency at 7T (R = 2) than at 3T for both T1 and T2 with, respectively, a 4.1 and 2.3 times increase in SNR‐efficiency.
Conclusion
We presented an accelerated version of MR‐STAT tailored to high field (7T) MRI using a low‐SAR flip‐angle train and showed high quality parameter maps with an increased SNR‐efficiency compared to MR‐STAT at 3T.
To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of ...MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. The average gamma pass rates using the 3%, 3 mm and 2%, 2 mm criteria were above 97 and 91%, respectively, for all volumes of interests considered. All DVH points calculated on sCT differed less than ±2.5% from the corresponding points on CT. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow.
In the radiofrequency (RF) range, the electrical properties of tissues (EPs: conductivity and permittivity) are modulated by the ionic and water content, which change for pathological conditions. ...Information on tissues EPs can be used e.g. in oncology as a biomarker. The inability of MR-Electrical Properties Tomography techniques (MR-EPT) to accurately reconstruct tissue EPs by relating MR measurements of the transmit RF field to the EPs limits their clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured MRI quantities, we propose a data driven approach where the electrical properties reconstruction problem can be casted as a supervised deep learning task (DL-EPT). DL-EPT reconstructions for simulations and MR measurements at 3 Tesla on phantoms and human brains using a conditional generative adversarial network demonstrate high quality EPs reconstructions and greatly improved precision compared to conventional MR-EPT. The supervised learning approach leverages the strength of electromagnetic simulations, allowing circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is more noise-robust than MR-EPT, the requirements for MR acquisitions can be relaxed. This could be a major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties.
In quantitative measurement of the
T2 value of tissues, the diffusion of water molecules has been recognized as a confounder. This is most notably so for transient‐state quantitative mapping ...techniques, which allow simultaneous estimation of
T1 and
T2. In prior work, apparently conflicting conclusions are presented on the level of diffusion‐induced bias on the T2 estimate. So far there is a lack of studies on the effect of the RF pulse angle sequence on the level of diffusion‐induced bias. In this work, we show that the specific transient‐state RF pulse sequence has a large effect on this level of bias. In particular, the bias level is strongly influenced by the mean value of the RF pulse angles. Also, for realistic values of the spoiling gradient area, we infer that the diffusion‐induced bias is negligible for non‐liquid human tissues; yet, for phantoms, the effect can be substantial (15% of the true
T2 value) for some RF pulse sequences. This should be taken into account in validation procedures.
It is shown that the specific transient‐state RF pulse sequence has a large effect on the diffusion‐induced level of bias in measuring
T2. For realistic values of the spoiling gradient area, for phantoms, the effect can be substantial (15% of the true
T2 value) for some RF pulse sequences.
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