Introduction: Cancer incidence is expected to increase in Europe by 18% in eighteen years. To account for the increasing patient numbers, the workload per patient needs to be reduced. One step towards future-proof radiotherapy is automated MR-only radiotherapy as it could eliminate the need for (i) a planning CT and (ii) for manual organ at risk (OAR) delineations. The aim of this study was to evaluate the feasibility of an automated MR-only workflow for head-and-neck radiotherapy. Method: Automated MR-only radiotherapy consisted of a Zero-Echo-Time-based synthetic CT for dose calculations and automated T2w-based OAR delineations. Automated MR-Only RT was compared to the clinical workflow consisting of CT-based dose calculation and CT-based OAR delineations. Both approaches were benchmarked to a gold standard consisting of the planning CT for dose calculations and manual delineations on the T2w MR scan. Dice similarity coefficients (DSC), 95% Hausdorff distances and absolute DVH metrics were compared between the clinical and MR-only workflow using a linear mixed-effect model. A p-value < 0.05 was deemed significant. Results: Seventeen head-and-neck cancer patients were included. The automated MR-only delineations were more accurate compared to the clinical CT delineations (DSC of 0.79 vs. 0.67; 95% Hausdorff distance 4.0 vs 5.8 mm (p-values < 0.001)). The average dose calculation errors of the automated MR only RT were smaller than the clinical workflow (+0.34 Gy vs. −1.39 (p-value < 0.01)). Discussion: The automated MR-only head-and-neck radiotherapy workflow was more accurate than the standard CT based clinical workflow, demonstrating the feasibility of automated MR-only RT to decrease the workload for head-and-neck RT treatment preparation.

Background and Purpose: MRI-based synthetic CTs (synCTs) show promise to replace planning CT scans in various anatomical regions. However, the head-and-neck region remains challenging because of patient-specific air, bone and soft tissues interfaces and oropharynx cavities. Zero-Echo-Time (ZTE) MRI can be fast and silent, accurately discriminate bone and air, and could potentially lead to high dose calculation accuracy, but is relatively unexplored for the head-and-neck region. Here, we prospectively evaluated the dosimetric accuracy of a novel, fast ZTE sequence for synCT generation. Materials and Methods: The method was developed based on 127 patients and validated in an independent test (n = 17). synCTs were generated using a multi-task 2D U-net from ZTE MRIs (scanning time: 2:33 min (normal scan) or 56 s (accelerated scan)). Clinical treatment plans were recalculated on the synCT. The Hounsfield Units (HU) and dose-volume-histogram metrics were compared between the synCT and CT. Subsequently, synthetic treatment plans were generated to systematically assess dosimetry accuracy in different anatomical regions using dose-volume-histogram metrics. Results: The mean absolute error between the synCT and CT was 94 ± 11 HU inside the patient contour. For the clinical plans, 98.8% of PTV metrics deviated less than 2% between synCT and CT and all OAR metrics deviated less than 1 Gy. The synthetic plans showed larger dose differences depending on the location of the PTV. Conclusions: Excellent dose agreement was found based on clinical plans between the CT and a ZTE-MR-based synCT in the head-and-neck region. Synthetic plans are an important addition to clinical plans to evaluate the dosimetric accuracy of synCT scans.

Background: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is clinically used to quantify flow. The quantification accuracy is diminished by background phase errors. Image-based background phase correction algorithms are commercially available, but their accuracy is still under evaluation. Here, we validate a recently developed non-linear phase contrast correction (nPCcor) algorithm that includes automatic failure mode classification in a large single-vendor multi-scanner retrospective study. Methods: Three hundred forty-six through-plane PC images at the aortic valve (AAo) and pulmonary artery (PA) were acquired on three different GE HealthCare 1.5T clinical MRI scanners. Each PC scan was repeated on a static phantom, and the static phantom-corrected PC series was considered as the reference standard. Two image-based static tissue background phase corrections were applied on each PC series: a linear and the nPCcor. Accuracy of nPCcor was studied by comparing the net flow in the vessel of interest for the uncorrected, linear-corrected, and nPCcor images with respect to the static phantom-corrected series. Accuracy was defined as a difference in net flow ≤10% with respect to the static phantom corrected net flow. Results: Flow measurements using the nPCcor images after nPCcor automatic classification were found to be accurate for 87% (281/323) of PC datasets, 6% and 17% better than using uncorrected and linear-corrected (p<0.05), respectively. Most importantly, nPCcor was able to correctly identify 70% (16/23) PC cases likely to provide inaccurate flow measurements. Flow measurements after nPCcor in the scanner with the largest phase offsets were found to be accurate for 74% (62/84) of PC datasets, 22% better than using the uncorrected images (p<0.05). nPCcor correction was statistically significant more accurate than linear correction for all scanners (p<0.05). The percentage of regurgitation reclassification of ≥1 category decreased to 8% (8/323) after nPCcor correction, 3% better than for uncorrected images. Conclusion: nPCcor with automatic failure mode evaluation improved accuracy with respect to no correction and linear correction and successfully identified PC scans that are likely to result in unreliable flow measurements. nPCcor performance and phase offset errors varied greatly among scanners using the same CMR protocol. nPCcor has higher impact in scanners exhibiting the largest background phase offsets.

Background: Gadolinium-based contrast agents (GBCAs) are usually employed for glioma diagnosis. However, GBCAs raise safety concerns, lead to patient discomfort and increase costs. Parametric maps offer a potential solution by enabling quantification of subtle tissue changes without GBCAs, but they are not commonly used in clinical practice due to the need for specifically targeted sequences. This work proposes to predict post-contrast T1-weighted enhancement without GBCAs from pre-contrast conventional weighted images through synthetic parametric maps computed with generative artificial intelligence (deep learning). Methods: In this retrospective study, three datasets have been employed: (I) a proprietary dataset with 15 glioma patients (hereafter, GLIOMA dataset); (II) relaxometry maps from 5 healthy volunteers; and (III) UPenn-GBM, a public dataset with 493 glioblastoma patients. A deep learning method for synthesizing parametric maps from only two conventional weighted images is proposed. Particularly, we synthesize longitudinal relaxation time (T1), transversal relaxation time (T2), and proton density (PD) maps. The deep learning method is trained in a supervised manner with the GLIOMA dataset, which comprises weighted images and parametric maps obtained with magnetic resonance image compilation (MAGiC). Thus, MAGiC maps were used as references for the training. For testing, a leave-one-out scheme is followed. Finally, the synthesized maps are employed to predict T1-weighted enhancement without GBCAs. Our results are compared with those obtained by MAGiC; specifically, both the maps obtained with MAGiC and the synthesized maps are used to distinguish between healthy and abnormal tissue (ABN) and, particularly, tissues with and without T1-weighted enhancement. The generalization capability of the method was also tested on two additional datasets (healthy volunteers and the UPenn-GBM). Results: Parametric maps synthesized with deep learning obtained similar performance compared to MAGiC for discriminating normal from ABN (sensitivities: 88.37% vs. 89.35%) and tissue with and without T1-weighted enhancement (sensitivities: 93.26% vs. 87.29%) on the GLIOMA dataset. These values were comparable to those obtained on UPenn-GBM (sensitivities of 91.23% and 81.04% for each classification). Conclusions: Our results suggest the feasibility to predict T1-weighted-enhanced tissues from pre-contrast conventional weighted images using deep learning for the synthesis of parametric maps.

Magnetic resonance thermometry (MRT) can measure in-vivo 3D-temperature changes in real-time and noninvasively. However, for the oropharynx region and the entire head and neck, motion potentially introduces large artifacts. Considering long treatment times of 60–90 min, this study aims to evaluate whether MRT around the oropharynx is clinically feasible for hyperthermia treatments and quantify the effects of breathing and swallowing on MRT performance. A 3D-ME-FGRE sequence was used in a phantom cooling down and around the oropharynx of five volunteers over ∼75 min. The imaging protocol consisted of imaging with acceleration (ARC = 2), number of image averages (NEX = 1,2 and 3). For volunteers, the acquisitions included a breath-hold scan and scans with deliberate swallowing. MRT performance was quantified in neck muscle, spinal cord and masseter muscle, using mean average error (MAE), mean error (ME) and spatial standard deviation (SD). In phantom, an increase in NEX leads to a significant decrease in SD, but MAE and ME were unchanged. No significant difference was found in volunteers between the different scans. There was a significant difference between the regions evaluated: neck muscle had the best MAE (=1.96 °C) and SD (=0.82 °C), followed by spinal cord (MAE = 3.17 °C, SD = 0.92 °C) and masseter muscle (MAE = 4.53 °C, SD = 1.16 °C). Concerning the ME, spinal cord did best, then neck muscle and masseter muscle, with values of −0.64 °C, 1.15 °C and −3.05 °C respectively. Breathing, swallowing, and different ways of imaging (acceleration and NEX) do not significantly influence the MRT performance in the oropharynx region. The ROI selected however, leads to significant differences.

Characterization of tumor microvasculature is important in tumor assessment and studying treatment response. This is possible by acquiring vascular biomarkers with magnetic resonance imaging (MRI) based on dynamic susceptibility contrast (DSC). We propose magnetic resonance vascular fingerprinting (MRVF) for hybrid echo planar imaging (HEPI) acquired during the first passage of the contrast agent (CA). The proposed approach was evaluated in patients with gliomas, and we simultaneously estimated vessel radius and relative cerebral blood volume. These parameters were also compared to the respective values estimated using the previously introduced vessel size imaging (VSI) technique. The results of both methods were found to be consistent. MRVF was also found to be robust to noise in the estimation of the parameters. DSC-HEPI-based MRVF provides characterization of microvasculature in gliomas with a short acquisition time and can be further improved in several ways to increase our understanding of tumor physiology.

Purpose: MR thermometry (MRT) enables noninvasive temperature monitoring during hyperthermia treatments. MRT is already clinically applied for hyperthermia treatments in the abdomen and extremities, and devices for the head are under development. In order to optimally exploit MRT in all anatomical regions, the best sequence setup and post-processing must be selected, and the accuracy needs to be demonstrated. Methods: MRT performance of the traditionally used double-echo gradient-echo sequence (DE-GRE, 2 echoes, 2D) was compared to multi-echo sequences: a 2D fast gradient-echo (ME-FGRE, 11 echoes) and a 3D fast gradient-echo sequence (3D-ME-FGRE, 11 echoes). The different methods were assessed on a 1.5 T MR scanner (GE Healthcare) using a phantom cooling down from 59 °C to 34 °C and unheated brains of 10 volunteers. In-plane motion of volunteers was compensated by rigid body image registration. For the ME sequences, the off-resonance frequency was calculated using a multi-peak fitting tool. To correct for B0 drift, the internal body fat was selected automatically using water/fat density maps. Results: The accuracy of the best performing 3D-ME-FGRE sequence was 0.20 °C in phantom (in the clinical temperature range) and 0.75 °C in volunteers, compared to DE-GRE values of 0.37 °C and 1.96 °C, respectively. Conclusion: For hyperthermia applications, where accuracy is more important than resolution or scan-time, the 3D-ME-FGRE sequence is deemed the most promising candidate. Beyond its convincing MRT performance, the ME nature enables automatic selection of internal body fat for B0 drift correction, an important feature for clinical application.

Purpose: We studied the differences between planning and treatment position, their impact on the accuracy of hyperthermia treatment planning (HTP) predictions, and the relevance of including true treatment anatomy and position in HTP based on magnetic resonance (MR) images. Materials and methods: All volunteers were scanned with an MR-compatible hyperthermia device, including a filled waterbolus, to replicate the treatment setup. In the planning setup, the volunteers were scanned without the device to reproduce the imaging in the current HTP. First, we used rigid registration to investigate the patient position displacements between the planning and treatment setup. Second, we performed HTP for the planning anatomy at both positions and the treatment mimicking anatomy to study the effects of positioning and anatomy on the quality of the simulated hyperthermia treatment. Treatment quality was evaluated using SAR-based parameters. Results: We found an average displacement of 2 cm between planning and treatment positions. These displacements caused average absolute differences of ∼12% for TC25 and 10.4%–15.9% in THQ. Furthermore, we found that including the accurate treatment position and anatomy in treatment planning led to an improvement of 2% in TC25 and 4.6%–10.6% in THQ. Conclusions: This study showed that precise patient position and anatomy are relevant since these affect the accuracy of HTP predictions. The major part of improved accuracy is related to implementing the correct position of the patient in the applicator. Hence, our study shows a clear incentive to accurately match the patient position in HTP with the actual treatment.

Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. Level of Evidence: NA. Technical Efficacy: Stage 2.

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmentations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL- and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray matter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.

Temperature monitoring plays a central role in improving clinical effectiveness of adjuvant hyperthermia. The potential of magnetic resonance thermometry for treatment monitoring purposes led to several MR-guided hyperthermia approaches. However, the proposed solutions were sub-optimal due to technological and intrinsic limitations. These hamper achieving target conformal heating possibilities (applicator limitations) and accurate thermometry (inadequate signal-to-noise-ratio (SNR)). In this work, we studied proof of principle of a dual-function hyperthermia approach based on a coil array (64 MHz, 1.5 T) that is integrated in-between a phased array for heating (434 MHz) for maximum signal receive in order to improve thermometry accuracy. Hereto, we designed and fabricated a superficial hyperthermia mimicking planar array setup to study the most challenging interactions of generic phased-array setups in order to validate the integrated approach. Experiments demonstrated that the setup complies with the superficial hyperthermia guidelines for heating and is able to improve SNR at 2-4 cm depth by 17%, as compared to imaging using the body coil. Hence, the results showed the feasibility of our dual-function MR-guided hyperthermia approach as basis for the development of application specific setups.

Hyperthermia treatments in the clinic rely on accurate temperature measurements to guide treatments and evaluate clinical outcome. Currently, magnetic resonance thermometry (MRT) is the only clinical option to non-invasively measure 3D temperature distributions. In this review, we evaluate the status quo and emerging approaches in this evolving technology for replacing conventional dosimetry based on intraluminal or invasively placed probes. First, we define standard-ized MRT performance thresholds, aiming at facilitating transparency in this field when comparing MR temperature mapping performance for the various scenarios that hyperthermia is currently applied in the clinic. This is based upon our clinical experience of treating nearly 4000 patients with superficial and deep hyperthermia. Second, we perform a systematic literature review, assessing MRT performance in (I) clinical and (II) pre-clinical papers. From (I) we identify the current clinical status of MRT, including the problems faced and from (II) we extract promising new techniques with the potential to accelerate progress. From (I) we found that the basic requirements for MRT during hyperthermia in the clinic are largely met for regions without motion, for example extremities. In more challenging regions (abdomen and thorax), progress has been stagnating after the clinical introduction of MRT-guided hyperthermia over 20 years ago. One clear difficulty for advancement is that performance is not or not uniformly reported, but also that studies often omit important details regarding their approach. Motion was found to be the common main issue hindering accurate MRT. Based on (II), we reported and highlighted promising developments to tackle the issues resulting from motion (directly or indirectly), including new developments as well as optimization of already existing strategies. Combined, these may have the potential to facilitate improvement in MRT in the form of more stable and reliable measurements via better stability and accuracy.