Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. Purpose: investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. Methods: CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A ``Baseline'' generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. Results: The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE)=106$\pm$20.7 HU (mean$\pm\sigma$). Performance on FLAIR significantly improved for the DR model with MAE=99.0$\pm$14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE=72.6$\pm$10.1 HU). Similarly, an improvement in $\gamma$-pass rate was obtained for DR vs Baseline. Conclusions: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.Comment: Preprint submitted to Physica Medica on 2023-02-16 for review. Also published in Zenodo at https://doi.org/10.5281/zenodo.774264

SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy

Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered CBCT and MRI images to facilitate the development and evaluation of sCT generation for radiotherapy planning. Acquisition and validation methods: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data-providing centers. Details are available in CSV files provided with the datasets. Data format and usage notes: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260705) under the SynthRAD2023 collection. The images for each subject are available in nifti format. Potential applications: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi-center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning.Comment: 15 pages, 4 figures, 9 tables, pre-print submitted to Medical Physics - dataset. The training dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7260705 from April, 1st 202

Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy

Background and Purpose To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a heterogeneous set of imaging protocol for paediatric patients affected by brain tumours. Materials and methods Sixty paediatric patients undergoing brain radiotherapy were included. MR imaging protocols varied among patients, and data heterogeneity was maintained in train/validation/test sets. Three 2D conditional generative adversarial networks (cGANs) were trained to generate sCT from T1-weighted MRI, considering the three orthogonal planes and its combination (multi-plane sCT). For each patient, median and standard deviation (σ) of the three views were calculated, obtaining a combined sCT and a proxy for uncertainty map, respectively. The sCTs were evaluated against the planning CT in terms of image similarity and accuracy for photon and proton dose calculations.Results A mean absolute error of 61±14 HU (mean±1σ) was obtained in the intersection of the body contours between CT and sCT. The combined multi-plane sCTs performed better than sCTs from any single plane. Uncertainty maps highlighted that multi-plane sCTs differed at the body contours and air cavities. A dose difference of -0.1±0.3% and 0.1±0.4% was obtained on the D>90% of the prescribed dose and mean γ2%,2mm pass-rate of 99.5±0.8% and 99.2±1.1% for photon and proton planning, respectively. Conclusion Accurate MR-based dose calculation using a combination of three orthogonal planes for sCT generation is feasible for paediatric brain cancer patients, even when training on a heterogeneous dataset

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Dipole antennas for ultrahigh-field body imaging:a comparison with loop coils

\u3cp\u3eAlthough the potential of dipole antennas for ultrahigh-field (UHF) MRI is largely recognized, they are still relatively unknown to the larger part of the MRI community. This article intends to provide electromagnetic insight into the general operating principles of dipole antennas by numerical simulations. The major part focuses on a comparison study of dipole antennas and loop coils at frequencies of 128, 298 and 400 MHz. This study shows that dipole antennas are only efficient radiofrequency (RF) coils in the presence of a dielectric and/or conducting load. In addition, the conservative electric fields (E-fields) at the ends of a dipole are negligible in comparison with the induced E-fields in the center. Like loop coils, long dipole antennas perform better than short dipoles for deeply located imaging targets and vice versa. When the optimal element is chosen for each depth, loop coils have higher B1 (+) efficiency for shallow depths, whereas dipole antennas have higher B1 (+) efficiency for large depths. The cross-over point depth decreases with increasing frequency: 11.6, 6.2 and 5.0 cm for 128, 298 and 400 MHz, respectively. For single elements, loop coils demonstrate a better B1 (+) /√SARmax ratio for any target depth and any frequency. However, one example study shows that, in an array setup with loop coil overlap for decoupling, this relationship is not straightforward. The overlapping loop coils may generate increased specific absorption rate (SAR) levels under the overlapping parts of the loops, depending on the drive phase settings. Copyright © 2015 John Wiley & Sons, Ltd.\u3c/p\u3

Electrical Properties Tomography: A Methodological Review

Electrical properties tomography (EPT) is an imaging method that uses a magnetic resonance (MR) system to non-invasively determine the spatial distribution of the conductivity and permittivity of the imaged object. This manuscript starts by providing clear definitions about the data required for, and acquired in, EPT, followed by comprehensively formulating the physical equations underlying a large number of analytical EPT techniques. This thorough mathematical overview of EPT harmonizes several EPT techniques in a single type of formulation and gives insight into how they act on the data and what their data requirements are. Furthermore, the review describes machine learning-based algorithms. Matlab code of several differential and iterative integral methods is available upon request

Validating subject-specific RF and thermal simulations in the calf muscle using MR-based temperature measurements

\u3cp\u3ePurpose: Ongoing discussions occur to translate the safety restrictions on MR scanners from specific absorption rate (SAR) to thermal dose. Therefore, this research focuses on the accuracy of thermal simulations in human subjects during an MR exam, which is fundamental information in that debate. Methods: Radiofrequency (RF) heating experiments were performed on the calves of 13 healthy subjects using a dedicated transmit-receive coil while monitoring the temperature with proton resonance frequency shift (PRFS) thermometry. Subject-specific models and one generic model were used for electromagnetic and thermal simulations using Pennes' bioheat equation, with the blood equilibration constant equaling zero. The simulations were subsequently compared with the experimental results. Results: The mean B \u3csup\u3e+\u3c/sup\u3e \u3csub\u3e1\u3c/sub\u3e equaled 15 µT in the center slice of all volunteers, and 95% of the voxels had errors smaller than 2.8 µT between the simulation and measurement. The intersubject variation in RF power to achieve the required B \u3csup\u3e+\u3c/sup\u3e \u3csub\u3e1\u3c/sub\u3e was 11%. The resulting intersubject variation in median temperature rise was 14%. Thermal simulations underestimated the median temperature increase on average, with 34% in subject-specific models and 28% in the generic model. Conclusions: Although thermal measures are directly coupled to tissue damage and therefore suitable for RF safety assessment, insecurities in the applied thermal modeling limit their estimation accuracy. Magn Reson Med 77:1691–1700, 2017.\u3c/p\u3

MRI-based transfer function determination through the transfer matrix by jointly fitting the incident and scattered B1+ field

\u3cp\u3ePurpose: A purely experimental method for MRI-based transfer function (TF) determination is presented. A TF characterizes the potential for radiofrequency heating of a linear implant by relating the incident tangential electric field to a scattered electric field at its tip. We utilize the previously introduced transfer matrix (TM) to determine transfer functions solely from the MR measurable quantities, that is, the (Formula presented.) and transceive phase distributions. This technique can extend the current practice of phantom-based TF assessment with dedicated experimental setup toward MR-based methods that have the potential to assess the TF in more realistic situations. Theory and Methods: An analytical description of the (Formula presented.) magnitude and transceive phase distribution around a wire-like implant was derived based on the TM. In this model, the background field is described using a superposition of spherical and cylindrical harmonics while the transfer matrix is parameterized using a previously introduced attenuated wave model. This analytical description can be used to estimate the transfer matrix and transfer function based on the measured (Formula presented.) distribution. Results: The TF was successfully determined for 2 mock-up implants: a 20-cm bare copper wire and a 20-cm insulated copper wire with 10 mm of insulation stripped at both endings in respectively 4 and 3 different trajectories. The measured TFs show a strong correlation with a reference determined from simulations and between the separate experiments with correlation coefficients above 0.96 between all TFs. Compared to the simulated TF, the maximum deviation in the estimated tip field is 9.4% and 12.2% for the bare and insulated wire, respectively. Conclusions: A method has been developed to measure the TF of medical implants using MRI experiments. Jointly fitting the incident and scattered (Formula presented.) distributions with an analytical description based on the transfer matrix enables accurate determination of the TF of 2 test implants. The presented method no longer needs input from simulated data and can therefore, in principle, be used to measure TF's in test animals or corpses.\u3c/p\u3

Combining a reduced field of excitation with SENSE-based parallel imaging for maximum imaging efficiency

\u3cp\u3ePURPOSE: To show that a combination of parallel imaging using sensitivity encoding (SENSE) and inner volume imaging (IVI) combines the known benefits of both techniques. SENSE with a reduced field of excitation (rFOX) is termed rSENSE.\u3c/p\u3e\u3cp\u3eTHEORY AND METHODS: The noise level in SENSE reconstructions is reduced by removing voxels from the unfolding process that are rendered silent by using rFOX. The silent voxels need to be identified beforehand, this is done by using rFOX in the coil sensitivity maps. In vivo experiments were performed at 7 Tesla using a 32-channel receive coil.\u3c/p\u3e\u3cp\u3eRESULTS: Good image quality could be obtained in vivo with rSENSE at acceleration factors that are higher than could be obtained using SENSE or IVI alone. With rSENSE we were also able to accelerate scans using an rFOX that was purposely designed to be imperfect or incompatible at all with IVI.\u3c/p\u3e\u3cp\u3eCONCLUSION: rSENSE has been demonstrated in vivo with two-dimensionally selective radiofrequency pulses. Besides allowing additional scan acceleration, it offers a greater robustness and flexibility than IVI. The proposed method can be used with other field strengths, anatomies and other rFOX techniques. Magn Reson Med 78:88-96, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.\u3c/p\u3