Data-driven electrical conductivity brain imaging using 3 T MRI.

Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignore B 1 + magnitude information, such as Savitzky-Golay kernel combined with Gaussian filter (S-G Kernel), phase-based convection-reaction EPT (cr-EPT), magnitude-weighted polynomial-fitting phase-based EPT (Poly-Fit), and integral-based phase-based EPT (Integral-based). From the in-silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in-silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground-truth conductivity (2.00, 0.30, 0.50 S/m) than the integral-based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In-vivo ANN-based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in-vivo data and pathologies. The reported in-vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in-vivo pathologies, thus demonstrating its potential for clinical applications.

Synthetic MRI with Magnetic Resonance Spin TomogrAphy in Time-Domain (MR-STAT): Results from a Prospective Cross-Sectional Clinical Trial.

BACKGROUND: Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) can reconstruct whole-brain multi-parametric quantitative maps (eg, T1 , T2 ) from a 5-minute MR acquisition. These quantitative maps can be leveraged for synthetization of clinical image contrasts. PURPOSE: The objective was to assess image quality and overall diagnostic accuracy of synthetic MR-STAT contrasts compared to conventional contrast-weighted images. STUDY TYPE: Prospective cross-sectional clinical trial. POPULATION: Fifty participants with a median age of 45 years (range: 21-79 years) consisting of 10 healthy participants and 40 patients with neurological diseases (brain tumor, epilepsy, multiple sclerosis or stroke). FIELD STRENGTH/SEQUENCE: 3T/Conventional contrast-weighted imaging (T1 /T2 weighted, proton density [PD] weighted, and fluid-attenuated inversion recovery [FLAIR]) and a MR-STAT acquisition (2D Cartesian spoiled gradient echo with varying flip angle preceded by a non-selective inversion pulse). ASSESSMENT: Quantitative T1 , T2 , and PD maps were computed from the MR-STAT acquisition, from which synthetic contrasts were generated. Three neuroradiologists blinded for image type and disease randomly and independently evaluated synthetic and conventional datasets for image quality and diagnostic accuracy, which was assessed by comparison with the clinically confirmed diagnosis. STATISTICAL TESTS: Image quality and consequent acceptability for diagnostic use was assessed with a McNemar's test (one-sided α = 0.025). Wilcoxon signed rank test with a one-sided α = 0.025 and a margin of Δ = 0.5 on the 5-level Likert scale was used to assess non-inferiority. RESULTS: All data sets were similar in acceptability for diagnostic use (≥3 Likert-scale) between techniques (T1 w:P = 0.105, PDw:P = 1.000, FLAIR:P = 0.564). However, only the synthetic MR-STAT T2 weighted images were significantly non-inferior to their conventional counterpart; all other synthetic datasets were inferior (T1 w:P = 0.260, PDw:P = 1.000, FLAIR:P = 1.000). Moreover, true positive/negative rates were similar between techniques (conventional: 88%, MR-STAT: 84%). DATA CONCLUSION: MR-STAT is a quantitative technique that may provide radiologists with clinically useful synthetic contrast images within substantially reduced scan time. EVIDENCE LEVEL: 1 Technical Efficacy: Stage 2.

STereotactic Arrhythmia Radioablation (STAR): the Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary consortium (STOPSTORM.eu) and review of current patterns of STAR practice in Europe.

The EU Horizon 2020 Framework-funded Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary (STOPSTORM) consortium has been established as a large research network for investigating STereotactic Arrhythmia Radioablation (STAR) for ventricular tachycardia (VT). The aim is to provide a pooled treatment database to evaluate patterns of practice and outcomes of STAR and finally to harmonize STAR within Europe. The consortium comprises 31 clinical and research institutions. The project is divided into nine work packages (WPs): (i) observational cohort; (ii) standardization and harmonization of target delineation; (iii) harmonized prospective cohort; (iv) quality assurance (QA); (v) analysis and evaluation; (vi, ix) ethics and regulations; and (vii, viii) project coordination and dissemination. To provide a review of current clinical STAR practice in Europe, a comprehensive questionnaire was performed at project start. The STOPSTORM Institutions' experience in VT catheter ablation (83% ≥ 20 ann.) and stereotactic body radiotherapy (59% > 200 ann.) was adequate, and 84 STAR treatments were performed until project launch, while 8/22 centres already recruited VT patients in national clinical trials. The majority currently base their target definition on mapping during VT (96%) and/or pace mapping (75%), reduced voltage areas (63%), or late ventricular potentials (75%) during sinus rhythm. The majority currently apply a single-fraction dose of 25 Gy while planning techniques and dose prescription methods vary greatly. The current clinical STAR practice in the STOPSTORM consortium highlights potential areas of optimization and harmonization for substrate mapping, target delineation, motion management, dosimetry, and QA, which will be addressed in the various WPs.

Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography.

Magnetic resonance electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available; however, all these methods present several limitations, which hamper the clinical applicability. Standard Helmholtz-based MR-EPT methods are severely affected by noise. Iterative reconstruction methods such as contrast source inversion electrical properties tomography (CSI-EPT) are typically time-consuming and are dependent on their initialization. Deep learning (DL) based methods require a large amount of training data before sufficient generalization can be achieved. Here, we investigate the benefits achievable using a hybrid approach, that is, using MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using realistic electromagnetic simulations at 3 and 7 T, the accuracy and precision of hybrid CSI reconstructions are compared with those of standard 3D CSI-EPT reconstructions. Our results indicate that a hybrid method consisting of an initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data-driven DL-based EPT reconstructions, while the subsequent CSI-EPT facilitates a better generalization by providing data consistency.

Improving phase-based conductivity reconstruction by means of deep learning-based denoising of B1+ phase data for 3T MRI.

PURPOSE: To denoise B1+ phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system. METHODS: For B1+ phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the B1+ phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T1 , T2 , and proton density-weighted brain images and proton density-weighted breast images. In addition, conductivity reconstructions from deep learning-based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering). RESULTS: The proposed deep learning-based denoising approach showed improvement for B1+ phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised B1+ phase with deep learning. CONCLUSION: The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise B1+ maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.

Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study.

First in vivo brain conductivity reconstructions using Helmholtz MR-Electrical Properties Tomography (MR-EPT) have been published. However, a large variation in the reconstructed conductivity values is reported and these values differ from ex vivo conductivity measurements. Given this lack of agreement, we performed an in vivo study on eight healthy subjects to provide reference in vivo brain conductivity values. MR-EPT reconstructions were performed at 3 T for eight healthy subjects. Mean conductivity and standard deviation values in the white matter, gray matter and cerebrospinal fluid (σWM, σGM, and σCSF) were computed for each subject before and after erosion of regions at tissue boundaries, which are affected by typical MR-EPT reconstruction errors. The obtained values were compared to the reported ex vivo literature values. To benchmark the accuracy of in vivo conductivity reconstructions, the same pipeline was applied to simulated data, which allow knowledge of ground truth conductivity. Provided sufficient boundary erosion, the in vivo σWM and σGM values obtained in this study agree for the first time with literature values measured ex vivo. This could not be verified for the CSF due to its limited spatial extension. Conductivity reconstructions from simulated data verified conductivity reconstructions from in vivo data and demonstrated the importance of discarding voxels at tissue boundaries. The presented σWM and σGM values can therefore be used for comparison in future studies employing different MR-EPT techniques.

A novel concurrent TMS-fMRI method to reveal propagation patterns of prefrontal magnetic brain stimulation.

Major depressive disorder (MDD) is a severe mental disorder associated with high morbidity and mortality rates, which remains difficult to treat, as both resistance and recurrence rates are high. Repetitive transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC) provides a safe and effective treatment for selected patients with treatment-resistant MDD. Little is known about the mechanisms of action of TMS provided to the left DLPFC in MDD and we can currently not predict who will respond to this type of treatment, precluding effective patient selection. In order to shed some light on the mechanism of action, we applied single pulse TMS to the left DLPFC in 10 healthy participants using a unique TMS-fMRI set-up, in which we could record the direct effects of TMS. Stimulation of the DLPFC triggered activity in a number of connected brain regions, including the subgenual anterior cingulate cortex (sgACC) in four out of nine participants. The sgACC is of particular interest, because normalization of activity in this region has been associated with relief of depressive symptoms in MDD patients. This is the first direct evidence that TMS pulses delivered to the DLPFC can propagate to the sgACC. The propagation of TMS-induced activity from the DLPFC to sgACC may be an accurate biomarker for rTMS efficacy. Further research is required to determine whether this method can contribute to the selection of patients with treatment resistant MDD who will respond to rTMS treatment.

Error analysis of helmholtz-based MR-electrical properties tomography.

PURPOSE: MR electrical properties tomography (MR-EPT) aims to measure tissue electrical properties by computing spatial derivatives of measured B1+ data. This computation is very sensitive to spatial fluctuations caused, for example, by noise and Gibbs ringing. In this work, the error arising from the computation of spatial derivatives using finite difference kernels (FD error) has been investigated. In relation to this FD error, it has also been investigated whether mitigation strategies such as Gibbs ringing correction and Gaussian apodization can be beneficial for conductivity reconstructions. METHODS: Conductivity reconstructions were performed on a phantom (by means of simulations and MR measurements at 3T) and on a human brain model. The accuracy was evaluated as a function of image resolution, FD kernel size, k-space windowing, and signal-to-noise ratio. The impact of mitigation strategies was also investigated. RESULTS: The adopted small FD kernel is highly sensitive to spatial fluctuations, whereas the large FD kernel is more noise-robust. However, large FD kernels lead to extended numerical boundary error propagation, which severely hampers the MR-EPT reconstruction accuracy for highly spatially convoluted tissue structures such as the human brain. Mitigation strategies slightly improve the accuracy of conductivity reconstructions. For the adopted derivative kernels and the investigated scenario, MR-EPT conductivity reconstructions show low accuracy: less than 37% of the voxels have a relative error lower than 30%. CONCLUSION: The numerical error introduced by the computation of spatial derivatives using FD kernels is one of the major causes of limited accuracy in Helmholtz-based MR-EPT reconstructions. Magn Reson Med 80:90-100, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A geometrical shift results in erroneous appearance of low frequency tissue eddy current induced phase maps.

PURPOSE: Knowledge on low frequency (LF) tissue conductivity is relevant for various biomedical purposes. To obtain this information, LF phase maps arising from time-varying imaging gradients have been demonstrated to create a LF conductivity contrast. Essential in this methodology is the subtraction of phase images acquired with opposite gradient polarities to separate LF and RF phase effects. Here we demonstrate how sensitive these subtractions are with respect to geometrical distortions. THEORY AND METHODS: The effect of geometrical distortions on LF phase maps is mathematically defined. After quantifying typical geometrical distortions, their effects on LF phase maps are evaluated using conductive phantoms. For validation, electromagnetic simulations of LF phase maps were performed. RESULTS: Even sub-voxel distortions of 10% of the voxel size, measured for a typical LF MR sequence, cause leakage of RF phase into LF phase of several milli-radians, leading to a misleading pattern of LF phase maps. This leakage is mathematically confirmed, while simulations indicate that the expected LF phase should be in order of micro-radians. CONCLUSION: The conductivity scaling of LF phase maps is attributable to the RF phase leakage, thus dependent on the RF conductivity. In fact, simulations show that the LF phase is not measurable. Magn Reson Med 76:905-912, 2016. © 2015 Wiley Periodicals, Inc.

MR-based measurements and simulations of the magnetic field created by a realistic transcranial magnetic stimulation (TMS) coil and stimulator.

Transcranial magnetic stimulation (TMS) is an emerging technique that allows non-invasive neurostimulation. However, the correct validation of electromagnetic models of typical TMS coils and the correct assessment of the incident TMS field (BTMS ) produced by standard TMS stimulators are still lacking. Such a validation can be performed by mapping BTMS produced by a realistic TMS setup. In this study, we show that MRI can provide precise quantification of the magnetic field produced by a realistic TMS coil and a clinically used TMS stimulator in the region in which neurostimulation occurs. Measurements of the phase accumulation created by TMS pulses applied during a tailored MR sequence were performed in a phantom. Dedicated hardware was developed to synchronize a typical, clinically used, TMS setup with a 3-T MR scanner. For comparison purposes, electromagnetic simulations of BTMS were performed. MR-based measurements allow the mapping and quantification of BTMS starting 2.5 cm from the TMS coil. For closer regions, the intra-voxel dephasing induced by BTMS prohibits TMS field measurements. For 1% TMS output, the maximum measured value was ~0.1 mT. Simulations reflect quantitatively the experimental data. These measurements can be used to validate electromagnetic models of TMS coils, to guide TMS coil positioning, and for dosimetry and quality assessment of concurrent TMS-MRI studies without the need for crude methods, such as motor threshold, for stimulation dose determination.

Experimental demonstration of real-time cardiac physiology-based radiotherapy gating for improved cardiac radioablation on an MR-linac.

BACKGROUND: Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high-dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition. PURPOSE: We demonstrate real-time cardiac physiology-based radiotherapy beam gating within a preset cardiac phase on an MR-linac. METHODS: MR images were acquired in healthy volunteers (n = 5, mean age = 29.6 years, mean heart-rate (HR) = 56.2 bpm) on the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) after obtaining written informed consent. The images were acquired using a single-slice balance steady-state free precession (bSSFP) sequence in the coronal or sagittal plane (TR/TE = 3/1.48 ms, flip angle = 48 ∘ $^{\circ }$ , SENSE = 1.5, field-of-view = 400 × 207 $\text{field-of-view} = {400}\times {207}$ mm 2 ${\text{mm}}^{2}$ , voxel size = 3 × 3 × 15 $3\times 3\times 15$ mm 3 ${\rm mm}^{3}$ , partial Fourier factor = 0.65, frame rate = 13.3 Hz). In parallel, a 4-lead ECG-signal was acquired using MR-compatible equipment. The feasibility of ECG-based beam gating was demonstrated with a prototype gating workflow using a Quasar MRI4D motion phantom (IBA Quasar, London, ON, Canada), which was deployed in the bore of the MR-linac. Two volunteer-derived combined ECG-motion traces (n = 2, mean age = 26 years, mean HR = 57.4 bpm, peak-to-peak amplitude = 14.7 mm) were programmed into the phantom to mimic dose delivery on a cardiac target in breath-hold. Clinical ECG-equipment was connected to the phantom for ECG-voltage-streaming in real-time using research software. Treatment beam gating was performed in the quiescent phase (end-diastole). System latencies were compensated by delay time correction. A previously developed MRI-based gating workflow was used as a benchmark in this study. A 15-beam intensity-modulated radiotherapy (IMRT) plan ( 1 × 6.25 ${1}\times {6.25}$ Gy) was delivered for different motion scenarios onto radiochromic films. Next, cardiac motion was then estimated at the basal anterolateral myocardial wall via normalized cross-correlation-based template matching. The estimated motion signal was temporally aligned with the ECG-signal, which were then used for position- and ECG-based gating simulations in the cranial-caudal (CC), anterior-posterior (AP), and right-left (RL) directions. The effect of gating was investigated by analyzing the differences in residual motion at 30, 50, and 70% treatment beam duty cycles. RESULTS: ECG-based (MRI-based) beam gating was performed with effective duty cycles of 60.5% (68.8%) and 47.7% (50.4%) with residual motion reductions of 62.5% (44.7%) and 43.9% (59.3%). Local gamma analyses (1%/1 mm) returned pass rates of 97.6% (94.1%) and 90.5% (98.3%) for gated scenarios, which exceed the pass rates of 70.3% and 82.0% for nongated scenarios, respectively. In average, the gating simulations returned maximum residual motion reductions of 88%, 74%, and 81% at 30%, 50%, and 70% duty cycles, respectively, in favor of MRI-based gating. CONCLUSIONS: Real-time ECG-based beam gating is a feasible alternative to MRI-based gating, resulting in improved dose delivery in terms of high γ -pass $\gamma {\text{-pass}}$ rates, decreased dose deposition outside the PTV and residual motion reduction, while by-passing cardiac MRI challenges.

A Framework for Assessing the Effect of Cardiac and Respiratory Motion for Stereotactic Arrhythmia Radioablation Using a Digital Phantom With a 17-Segment Model: A STOPSTORM.eu Consortium Study.

PURPOSE: The optimal motion management strategy for patients receiving stereotactic arrhythmia radioablation (STAR) for the treatment of ventricular tachycardia (VT) is not fully known. We developed a framework using a digital phantom to simulate cardiorespiratory motion in combination with different motion management strategies to gain insight into the effect of cardiorespiratory motion on STAR. METHODS AND MATERIALS: The 4-dimensional (4D) extended cardiac-torso (XCAT) phantom was expanded with the 17-segment left ventricular (LV) model, which allowed placement of STAR targets in standardized ventricular regions. Cardiac- and respiratory-binned 4D computed tomography (CT) scans were simulated for free-breathing, reduced free-breathing, respiratory-gating, and breath-hold scenarios. Respiratory motion of the heart was set to population-averaged values of patients with VT: 6, 2, and 1 mm in the superior-inferior, posterior-anterior, and left-right direction, respectively. Cardiac contraction was adjusted by reducing LV ejection fraction to 35%. Target displacement was evaluated for all segments using envelopes encompassing the cardiorespiratory motion. Envelopes incorporating only the diastole plus respiratory motion were created to simulate the scenario where cardiac motion is not fully captured on 4D respiratory CT scans used for radiation therapy planning. RESULTS: The average volume of the 17 segments was 6 cm3 (1-9 cm3). Cardiac contraction-relaxation resulted in maximum segment (centroid) motion of 4, 6, and 3.5 mm in the superior-inferior, posterior-anterior, and left-right direction, respectively. Cardiac contraction-relaxation resulted in a motion envelope increase of 49% (24%-79%) compared with individual segment volumes, whereas envelopes increased by 126% (79%-167%) if respiratory motion also was considered. Envelopes incorporating only the diastole and respiration motion covered on average 68% to 75% of the motion envelope. CONCLUSIONS: The developed LV-segmental XCAT framework showed that free-wall regions display the most cardiorespiratory displacement. Our framework supports the optimization of STAR by evaluating the effect of (cardio)respiratory motion and motion management strategies for patients with VT.

Generalizable synthetic MRI with physics-informed convolutional networks.

BACKGROUND: Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a single acquisition. PURPOSE: We developed a physics-informed deep learning-based method to synthesize multiple brain MRI contrasts from a single 5-min acquisition and investigate its ability to generalize to arbitrary contrasts. METHODS: A dataset of 55 subjects acquired with a clinical MRI protocol and a 5-min transient-state sequence was used. The model, based on a generative adversarial network, maps data acquired from the five-minute scan to "effective" quantitative parameter maps (q*-maps), feeding the generated PD, T1 , and T2 maps into a signal model to synthesize four clinical contrasts (proton density-weighted, T1 -weighted, T2 -weighted, and T2 -weighted fluid-attenuated inversion recovery), from which losses are computed. The synthetic contrasts are compared to an end-to-end deep learning-based method proposed by literature. The generalizability of the proposed method is investigated for five volunteers by synthesizing three contrasts unseen during training and comparing these to ground truth acquisitions via qualitative assessment and contrast-to-noise ratio (CNR) assessment. RESULTS: The physics-informed method matched the quality of the end-to-end method for the four standard contrasts, with structural similarity metrics above 0.75 ± 0.08 (±std), peak signal-to-noise ratios above 22.4 ± 1.9, representing a portion of compact lesions comparable to standard MRI. Additionally, the physics-informed method enabled contrast adjustment, and similar signal contrast and comparable CNRs to the ground truth acquisitions for three sequences unseen during model training. CONCLUSIONS: The study demonstrated the feasibility of physics-informed, deep learning-based synthetic MRI to generate high-quality contrasts and generalize to contrasts beyond the training data. This technology has the potential to accelerate neuroimaging protocols.

Stereotactic Arrhythmia Radioablation (STAR): Assessment of cardiac and respiratory heart motion in ventricular tachycardia patients - A STOPSTORM.eu consortium review.

AIM: To identify the optimal STereotactic Arrhythmia Radioablation (STAR) strategy for individual patients, cardiorespiratory motion of the target volume in combination with different treatment methodologies needs to be evaluated. However, an authoritative overview of the amount of cardiorespiratory motion in ventricular tachycardia (VT) patients is missing. METHODS: In this STOPSTORM consortium study, we performed a literature review to gain insight into cardiorespiratory motion of target volumes for STAR. Motion data and target volumes were extracted and summarized. RESULTS: Out of the 232 studies screened, 56 provided data on cardiorespiratory motion, of which 8 provided motion amplitudes in VT patients (n = 94) and 10 described (cardiac/cardiorespiratory) internal target volumes (ITVs) obtained in VT patients (n = 59). Average cardiac motion of target volumes was < 5 mm in all directions, with maximum values of 8.0, 5.2 and 6.5 mm in Superior-Inferior (SI), Left-Right (LR), Anterior-Posterior (AP) direction, respectively. Cardiorespiratory motion of cardiac (sub)structures showed average motion between 5-8 mm in the SI direction, whereas, LR and AP motions were comparable to the cardiac motion of the target volumes. Cardiorespiratory ITVs were on average 120-284% of the gross target volume. Healthy subjects showed average cardiorespiratory motion of 10-17 mm in SI and 2.4-7 mm in the AP direction. CONCLUSION: This review suggests that despite growing numbers of patients being treated, detailed data on cardiorespiratory motion for STAR is still limited. Moreover, data comparison between studies is difficult due to inconsistency in parameters reported. Cardiorespiratory motion is highly patient-specific even under motion-compensation techniques. Therefore, individual motion management strategies during imaging, planning, and treatment for STAR are highly recommended.

Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes.

Objective.The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments.Approach.We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference.Main results.The framework is demonstrated in 2D on a motion phantom, andin vivoon volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated byin silico3D experiments with a digital motion phantom. The framework was compared with template matching-a reference, image-based, method-and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement.Significance.The high accuracy in combination with a total latency of less than 10 ms-including data acquisition and processing-make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments.

Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC Within 3 Minutes.

MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done on high performance CPU clusters with tens of minutes. In the current work, we propose an accelerated Cartesian MR-STAT algorithm incorporating two different strategies: firstly, a neural network is trained as a fast surrogate to learn the magnetization signal not only in the full time-domain but also in the compressed low-rank domain; secondly, based on the surrogate model, the Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems by the alternating direction method of multipliers. The proposed method substantially reduces the computational requirements for runtime and memory. Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction results using the proposed algorithm compared to the previous sparse Hessian method, and the reconstruction times are at least 40 times shorter. Incorporating sensitivity encoding and regularization terms is straightforward, and allows for better image quality with a negligible increase in reconstruction time. The proposed algorithm could reconstruct both balanced and gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could thereby facilitate the translation of MR-STAT in clinical settings.

MRI of the intraspinal nerve roots in patients with chronic inflammatory neuropathies: abnormalities correlate with clinical phenotypes.

OBJECTIVE: Chronic inflammatory demyelinating polyneuropathy (CIDP) and multifocal motor neuropathy (MMN) are caused by inflammatory changes of peripheral nerves. It is unknown if the intra-spinal roots are also affected. This MRI study systematically visualized intra-spinal nerve roots, i.e., the ventral and dorsal roots, in patients with CIDP, MMN and motor neuron disease (MND). METHODS: We performed a cross-sectional study in 40 patients with CIDP, 27 with MMN and 34 with MND. All patients underwent an MRI scan of the cervical intra-spinal roots. We systematically measured intra-spinal nerve root sizes bilaterally in the transversal plane at C5, C6 and C7 level. We calculated mean nerve root sizes and compared them between study groups and between different clinical phenotypes using a univariate general linear model. RESULTS: Patients with MMN and CIDP with a motor phenotype had thicker ventral roots compared to patients with CIDP with a sensorimotor phenotype (p = 0.012), while patients with CIDP with a sensory phenotype had thicker dorsal roots compared to patients with a sensorimotor phenotype (p = 0.001) and with MND (p = 0.004). CONCLUSION: We here show changes in the morphology of intra-spinal nerve roots in patients with chronic inflammatory neuropathies, compatible with their clinical phenotype.

Intrafraction motion during radiotherapy of breast tumor, breast tumor bed, and individual axillary lymph nodes on cine magnetic resonance imaging.

Background and purpose: In (ultra-)hypofractionation, the contribution of intrafraction motion to treatment accuracy becomes increasingly important. Our purpose was to evaluate intrafraction motion and resulting geometric uncertainties for breast tumor (bed) and individual axillary lymph nodes, and to compare prone and supine position for the breast tumor (bed). Materials and methods: During 1-3 min of free breathing, we acquired transverse/sagittal interleaved 1.5 T cine magnetic resonance imaging (MRI) of the breast tumor (bed) in prone and supine position and coronal/sagittal cine MRI of individual axillary lymph nodes in supine position. A total of 31 prone and 23 supine breast cine MRI (in 23 women) and 52 lymph node cine MRI (in 24 women) were included. Maximum displacement, breathing amplitude, and drift were analyzed using deformable image registration. Geometric uncertainties were calculated for all displacements and for breathing motion only. Results: Median maximum displacements (range over the three orthogonal orientations) were 1.1-1.5 mm for the breast tumor (bed) in prone and 1.8-3.0 mm in supine position, and 2.2-2.4 mm for lymph nodes. Maximum displacements were significantly smaller in prone than in supine position, mainly due to smaller breathing amplitude: 0.6-0.9 mm in prone vs. 0.9-1.4 mm in supine. Systematic and random uncertainties were 0.1-0.4 mm in prone position and 0.2-0.8 mm in supine position for the tumor (bed), and 0.4-0.6 mm for the lymph nodes. Conclusion: Intrafraction motion of breast tumor (bed) and individual lymph nodes was small. Motion of the tumor (bed) was smaller in prone than in supine position.