A reusable 3D printed brain-like phantom for benchmarking electrical properties tomography reconstructions.

PURPOSE: In MR electrical properties tomography (MR-EPT), electrical properties (EPs, conductivity and permittivity) are reconstructed from MR measurements. Phantom measurements are important to characterize the performance of MR-EPT reconstruction methods, since they allow knowledge of reference EPs values. To assess reconstruction methods in a more realistic scenario, it is important to test the methods using phantoms with realistic shapes, internal structures, and dielectric properties. In this work, we present a 3D printing procedure for the creation of realistic brain-like phantoms to benchmark MR-EPT reconstructions. METHODS: We created two brain-like geometries with three different compartments using 3D printing. The first geometry was filled once, while the second geometry was filled three times with different saline-gelatin solutions, resulting in a total of four phantoms with different EPs. The saline solutions were characterized using a probe. 3D MR-EPT reconstructions were performed from MR measurements at 3T. The reconstructed conductivity values were compared to reference values of the saline-gelatin solutions. The measured fields were also compared to simulated fields using the same phantom geometry and electrical properties. RESULTS: The measured fields were consistent with simulated fields. Reconstructed conductivity values were consistent with the reference (probe) conductivity values. This indicated the suitability of such phantoms for benchmarking MR-EPT reconstructions. CONCLUSION: We presented a new workflow to 3D print realistic brain-like phantoms in an easy and affordable way. These phantoms are suitable to benchmark MR-EPT reconstructions, but can also be used for benchmarking other quantitative MR methods.

A database for MR-based electrical properties tomography with in silico brain data-ADEPT.

PURPOSE: Several reconstruction methods for MR-based electrical properties tomography (EPT) have been developed. However, the lack of common data makes it difficult to objectively compare their performances. This is, however, a necessary precursor for standardizing and introducing this technique in the clinical setting. To enable objective comparison of the performances of reconstruction methods and provide common data for their training and testing, we created ADEPT, a database of simulated data for brain MR-EPT reconstructions. METHODS: ADEPT is a database containing in silico data for brain EPT reconstructions. This database was created from 25 different brain models, with and without tumors. Rigid geometric augmentations were applied, and different electrical properties were assigned to white matter, gray matter, CSF, and tumors to generate 120 different brain models. These models were used as input for finite-difference time-domain simulations in Sim4Life, used to compute the electromagnetic fields needed for MR-EPT reconstructions. RESULTS: Electromagnetic fields from 84 healthy and 36 tumor brain models were simulated. The simulated fields relevant for MR-EPT reconstructions (transmit and receive RF fields and transceive phase) and their ground-truth electrical properties are made publicly available through ADEPT. Additionally, nonattainable fields such as the total magnetic field and the electric field are available upon request. CONCLUSION: ADEPT will serve as reference database for objective comparisons of reconstruction methods and will be a first step toward standardization of MR-EPT reconstructions. Furthermore, it provides a large amount of data that can be exploited to train data-driven methods. It can be accessed from  https://doi.org/10.34894/V0HBJ8.

Technical feasibility of magnetic resonance fingerprinting on a 1.5T MRI-linac.

Hybrid MRI-linac (MRL) systems enable daily multiparametric quantitative MRI to assess tumor response to radiotherapy. Magnetic resonance fingerprinting (MRF) may provide time efficient means of rapid multiparametric quantitative MRI. The accuracy of MRF, however, relies on adequate control over system imperfections, such as eddy currents and [Formula: see text], which are different and not as well established on MRL systems compared to diagnostic systems. In this study we investigate the technical feasibility of gradient spoiled 2D MRF on a 1.5T MRL. We show with phantom experiments that the MRL generates reliable MRF signals that are temporally stable during the day and have good agreement with spin-echo reference measurements. Subsequent in-vivo MRF scans in healthy volunteers and a patient with a colorectal liver metastasis showed good image quality, where the quantitative values of selected organs corresponded with the values reported in literature. Therefore we conclude that gradient spoiled 2D MRF is feasible on a 1.5T MRL with similar performance as on a diagnostic system. The precision and accuracy of the parametric maps are sufficient for further investigation of the clinical utility of MRF for online quantitatively MRI-guided radiotherapy.

The noise navigator for MRI-guided radiotherapy: an independent method to detect physiological motion.

Motion is problematic during radiotherapy as it could lead to potential underdosage of the tumor, and/or overdosage in organs-at-risk. A solution is adaptive radiotherapy guided by magnetic resonance imaging (MRI). MRI allows for imaging of target volumes and organs-at-risk before and during treatment delivery with superb soft tissue contrast in any desired orientation, enabling motion management by means of (real-time) adaptive radiotherapy. The noise navigator, which is independent of the MR signal, could serve as a secondary motion detection method in synergy with MR imaging. The feasibility of respiratory motion detection by means of the noise navigator was demonstrated previously. Furthermore, from electromagnetic simulations we know that the noise navigator is sensitive to tissue displacement and thus could in principle be used for the detection of various types of motion. In this study we demonstrate the detection of various types of motion for three anatomical use cases of MRI-guided radiotherapy, i.e. torso (bulk movement and variable breathing), head-and-neck (swallowing) and cardiac. Furthermore, it is shown that the noise navigator can detect bulk movement, variable breathing and swallowing on a hybrid 1.5 T MRI-linac system. Cardiac activity detection through the noise navigator seems feasible in an MRI-guided radiotherapy setting, but needs further optimization. The noise navigator is a versatile and fast (millisecond temporal resolution) motion detection method independent of MR signal that could serve as an independent verification method to detect the occurrence of motion in synergy with real-time MRI-guided radiotherapy.

Fast contour propagation for MR-guided prostate radiotherapy using convolutional neural networks.

PURPOSE: To quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)-guided prostate external-beam radiotherapy. METHODS: Five prostate cancer patients underwent 20 fractions of image-guided external-beam radiotherapy on a 1.5 T MR-Linac system. For each patient, a pretreatment T2-weighted three-dimensional (3D) MR imaging (MRI) scan was used to delineate the clinical target volume (CTV) contours. The same scan was repeated during each fraction, with the CTV contour being manually adapted if necessary. A convolutional neural network (CNN) was trained for combined image registration and contour propagation. The network estimated the propagated contour and a deformation field between the two input images. The training set consisted of a synthetically generated ground truth of randomly deformed images and prostate segmentations. We performed a leave-one-out cross-validation on the five patients and propagated the prostate segmentations from the pretreatment to the fraction scans. Three variants of the CNN, aimed at investigating supervision based on optimizing segmentation overlap, optimizing the registration, and a combination of the two were compared to results of the open-source deformable registration software package Elastix. RESULTS: The neural networks trained on segmentation overlap or the combined objective achieved significantly better Hausdorff distances between predicted and ground truth contours than Elastix, at the much faster registration speed of 0.5 s. The CNN variant trained to optimize both the prostate overlap and deformation field, and the variant trained to only maximize the prostate overlap, produced the best propagation results. CONCLUSIONS: A CNN trained on maximizing prostate overlap and minimizing registration errors provides a fast and accurate method for deformable contour propagation for prostate MR-guided radiotherapy.

Dosimetric impact of soft-tissue based intrafraction motion from 3D cine-MR in prostate SBRT.

To investigate the dosimetric impact of intrafraction translation and rotation motion of the prostate, as extracted from daily acquired post-treatment 3D cine-MR based on soft-tissue contrast, in extremely hypofractionated (SBRT) prostate patients. Accurate dose reconstruction is performed by using a prostate intrafraction motion trace which is obtained with a soft-tissue based rigid registration method on 3D cine-MR dynamics with a temporal resolution of 11 s. The recorded motion of each time-point was applied to the planning CT, resulting in the respective dynamic volume used for dose calculation. For each treatment fraction, the treatment delivery record was generated by proportionally splitting the plan into 11 s intervals based on the delivered monitor units. For each fraction the doses of all partial plan/dynamic volume combinations were calculated and were summed to lead to the motion-affected fraction dose. Finally, for each patient the five fraction doses were summed, yielding the total treatment dose. Both daily and total doses were compared to the original reference dose of the respective patient to assess the impact of the intrafraction motion. Depending on the underlying motion of the prostate, different types of motion-affected dose distributions were observed. The planning target volumes (PTVs) ensured CTV_30 (seminal vesicles) D99% coverage for all patients, CTV_35 (prostate corpus) coverage for 97% of the patients and GTV_50 (local boost) for 83% of the patients when compared against the strict planning target D99% value. The dosimetric impact due to prostate intrafraction motion in extremely hypofractionated treatments was determined. The presented study is an essential step towards establishing the actual delivered dose to the patient during radiotherapy fractions.

The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy.

Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal. We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy. The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers. For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.

A deep learning method for image-based subject-specific local SAR assessment.

PURPOSE: Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning-based method for image-based subject-specific local SAR assessment. We propose to train a convolutional neural network to learn a "surrogate SAR model" to map the relation between subject-specific B1+ maps and the corresponding local SAR. METHOD: Our database of 23 subject-specific models with an 8-transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex B1+ maps and local SAR distributions were used to train a conditional generative adversarial network. Extra penalization for local SAR underestimation errors was included in the loss function. In silico and in vivo validation were performed. RESULTS: In silico cross-validation shows a good qualitative and quantitative match between predicted and ground-truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations. CONCLUSION: The proposed deep learning method allows online image-based subject-specific local SAR assessment. It greatly reduces the uncertainty in current state-of-the-art SAR assessment methods, reducing the time in the examination protocol by almost 25%.

Soft-tissue prostate intrafraction motion tracking in 3D cine-MR for MR-guided radiotherapy.

To develop a method to automatically determine intrafraction motion of the prostate based on soft tissue contrast on 3D cine-magnetic resonance (MR) images with high spatial and temporal resolution. Twenty-nine patients who underwent prostate stereotactic body radiotherapy (SBRT), with four implanted cylindrical gold fiducial markers (FMs), had cine-MR imaging sessions after each of five weekly fractions. Each cine-MR session consisted of 55 sequentially obtained 3D data sets ('dynamics') and was acquired over an 11 s period, covering a total of 10 min. The prostate was delineated on the first dynamic of every dataset and this delineation was used as the starting position for the soft tissue tracking (SST). Each subsequent dynamic was rigidly aligned to the first dynamic, based on the contrast of the prostate. The obtained translation and rotation describes the intrafraction motion of the prostate. The algorithm was applied to 6270 dynamics over 114 scans of 29 patients and the results were validated by comparing to previously obtained fiducial marker tracking data of the same dataset. Our proposed tracking method was also retro-perspectively applied to cine-MR images acquired during MR-guided radiotherapy of our first prostate patient treated on the MR-Linac. The difference in the 3D translation results between the soft tissue and marker tracking was below 1 mm for 98.2% of the time. The mean translation at 10 min were X: 0.0 [Formula: see text] 0.8 mm, Y: 1.0 [Formula: see text] 1.8 mm and Z: [Formula: see text] mm. The mean rotation results at 10 min were X: [Formula: see text], Y: 0.1 [Formula: see text] 0.6° and Z: 0.0 [Formula: see text] 0.7°. A fast, robust and accurate SST algorithm was developed which obviates the need for FMs during MR-guided prostate radiotherapy. To our knowledge, this is the first data using full 3D cine-MR images for real-time soft tissue prostate tracking, which is validated against previously obtained marker tracking data.

Understanding the physical relations governing the noise navigator.

PURPOSE: The noise navigator is a passive way to detect physiological motion occurring in a patient through thermal noise modulations measured by standard clinical radiofrequency receive coils. The aim is to gain a deeper understanding of the potential and applications of physiologically induced thermal noise modulations. METHODS: Numerical electromagnetic simulations and MR measurements were performed to investigate the relative contribution of tissue displacement versus modulation of the dielectric lung properties over the respiratory cycle, the impact of coil diameter and position with respect to the body. Furthermore, the spatial motion sensitivity of specific noise covariance matrix elements of a receive array was investigated. RESULTS: The influence of dielectric lung property variations on the noise variance is negligible compared to tissue displacement. Coil size affected the thermal noise variance modulation, but the location of the coil with respect to the body had a larger impact. The modulation depth of a 15 cm diameter stationary coil approximately 3 cm away from the chest (i.e. radiotherapy setup) was 39.7% compared to 4.2% for a coil of the same size on the chest, moving along with respiratory motion. A combination of particular noise covariance matrix elements creates a specific spatial sensitivity for motion. CONCLUSIONS: The insight gained on the physical relations governing the noise navigator will allow for optimized use and development of new applications. An optimized combination of elements from the noise covariance matrix offer new ways of performing, e.g. motion tracking.

Fiducial marker based intra-fraction motion assessment on cine-MR for MR-linac treatment of prostate cancer.

We have developed a method to determine intrafraction motion of the prostate through automatic fiducial marker (FM) tracking on 3D cine-magnetic resonance (MR) images with high spatial and temporal resolution. Twenty-nine patients undergoing prostate stereotactic body radiotherapy (SBRT), with four implanted cylindrical gold FMs, had cine-MR imaging sessions after each of five weekly fractions. Each cine-MR examination consisted of 55 sequentially obtained 3D datasets ('dynamics'), acquired over a 11 s period, covering a total of 10 min. FM locations in the first dynamic were manually identified by a clinician, FM centers in subsequent dynamics were automatically determined. Center of mass (COM) translations and rotations were determined by calculating the rigid transformations between the FM template of the first and subsequent dynamics. The algorithm was applied to 7315 dynamics over 133 scans of 29 patients and the obtained results were validated by comparing the COM locations recorded by the clinician at the halfway-dynamic (after 5 min) and end dynamic (after 10 min). The mean COM translations at 10 min were X: 0.0 [Formula: see text] 0.8 mm, Y: 1.0 [Formula: see text] 1.9 mm and Z: 0.9 [Formula: see text] 2.0 mm. The mean rotation results at 10 min were X: 0.1 [Formula: see text] 3.9°, Y: 0.0 [Formula: see text] 1.3° and Z: 0.1 [Formula: see text] 1.2°. The tracking success rate was 97.7% with a mean 3D COM error of 1.1 mm. We have developed a robust, fast and accurate FM tracking algorithm for cine-MR data, which allows for continuous monitoring of prostate motion during MR-guided radiotherapy (MRgRT). These results will be used to validate automatic prostate tracking based on soft-tissue contrast.

Magnetic Resonance Imaging only Workflow for Radiotherapy Simulation and Planning in Prostate Cancer.

Magnetic resonance imaging (MRI) is often combined with computed tomography (CT) in prostate radiotherapy to optimise delineation of the target and organs-at-risk (OAR) while maintaining accurate dose calculation. Such a dual-modality workflow requires two separate imaging sessions, and it has some fundamental and logistical drawbacks. Due to the availability of new MRI hardware and software solutions, CT examinations can be omitted for prostate radiotherapy simulations. All information for treatment planning, including electron density maps and bony anatomy, can nowadays be obtained with MRI. Such an MRI-only simulation workflow reduces delineation ambiguities, eases planning logistics, and improves patient comfort; however, careful validation of the complete MRI-only workflow is warranted. The first institutes are now adopting this MRI-only workflow for prostate radiotherapy. In this article, we will review technology and workflow requirements for an MRI-only prostate simulation workflow.

Respiratory motion model based on the noise covariance matrix of a receive array.

PURPOSE: Tracking of the internal anatomy by means of a motion model that uses the MR-derived motion fields and noise covariance matrix (NCM) dynamic as a surrogate signal. METHODS: A 2D respiratory motion model was developed based on the MR-derived motion fields and the NCM of a receive array used in MRI. Temporal dynamics of the NCM were used as a motion surrogate for a linear correspondence motion model. The model performance was tested on five healthy volunteers with a liver as the target. The motion fields were calculated from the cineMR frames with an optical flow registration tool. RESULTS: The model estimated the liver motion with an average residual error of 2.3 mm (13% of the motion amplitude). The model formation takes 3 min and the model latency was 0.5 s in the current implementation. The limiting factor for the latency is the current update time of the NCM (0.48 s), which in principle can be reduced to 0.004 s with an alternative way to determine the NCM. CONCLUSIONS: The 2D respiratory motion of the liver can be effectively estimated with the linear motion model that uses the temporal behavior of the NCM as motion surrogate. Magn Reson Med 79:1730-1735, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

MRI-based transfer function determination for the assessment of implant safety.

PURPOSE: We introduce a new MR-based method to determine the transfer function (TF) for radiofrequency (RF) safety assessment of active implantable medical devices. Transfer functions are implant-specific measures that relate the incident tangential electric field on an (elongated) implant to a scattered electric field at its tip. The proposed method allows for TF determination with a high spatial resolution in relatively fast measurements without requiring dedicated bench setups from MRI images. THEORY AND METHODS: The principle of reciprocity is used in conjunction with the potential to measure currents with MRI to determine TF. Low-flip angle 3D dual gradient echo MRI data are acquired with an implant as transceive antenna, which requires minimal hardware adaptations. The implant-specific TF is determined from the acquired MRI data, with two different postprocessing methods for comparison. RESULTS: TFs of linear and helical implants can be determined accurately (with a Pearson correlation coefficient R ≥ 0.7 between measurements and simulations, and a difference in field at the tip ΔEtip ≤ 19%) from relatively quick (t < 20 minutes) MRI acquisitions with (several) millimeter spatial resolution. CONCLUSION: Transfer function determination with MRI for RF safety assessment of implantable medical devices is possible. The proposed MR-based method allows for TF determination in more realistic exposure scenarios and solid media. Magn Reson Med 78:2449-2459, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Recommendations for MRI-based contouring of gross tumor volume and organs at risk for radiation therapy of pancreatic cancer.

PURPOSE: Local recurrence is a common and morbid event in patients with unresectable pancreatic adenocarcinoma. A more conformal and targeted radiation dose to the macroscopic tumor in nonmetastatic pancreatic cancer is likely to reduce acute toxicity and improve local control. Optimal soft tissue contrast is required to facilitate delineation of a target and creation of a planning target volume with margin reduction and motion management. Magnetic resonance imaging (MRI) offers considerable advantages in optimizing soft tissue delineation and is an ideal modality for imaging and delineating a gross tumor volume (GTV) within the pancreas, particularly as it relates to conformal radiation planning. Currently, no guidelines have been defined for the delineation of pancreatic tumors for radiation therapy treatment planning. Moreover, abdominal MRI sequences are complex and the anatomy relevant to the radiation oncologist can be challenging. The purpose of this study is to provide recommendations for delineation of GTV and organs at risk (OARs) using MRI and incorporating multiple MRI sequences. METHODS AND MATERIALS: Five patients with pancreatic cancer and 1 healthy subject were imaged with MRI scans either on 1.5T or on 3T magnets in 2 separate institutes. The GTV and OARs were contoured for all patients in a consensus meeting. RESULTS: An overview of MRI-based anatomy of the GTV and OARs is provided. Practical contouring instructions for the GTV and the OARs with the aid of MRI were developed and included in these recommendations. In addition, practical suggestions for implementation of MRI in pancreatic radiation treatment planning are provided. CONCLUSIONS: With this report, we attempt to provide recommendations for MRI-based contouring of pancreatic tumors and OARs. This could lead to better uniformity in defining the GTV and OARs for clinical trials and in radiation therapy treatment planning, with the ultimate goal of improving local control while minimizing morbidity.

Thermal noise variance of a receive radiofrequency coil as a respiratory motion sensor.

PURPOSE: Development of a passive respiratory motion sensor based on the noise variance of the receive coil array. METHODS: Respiratory motion alters the body resistance. The noise variance of an RF coil depends on the body resistance and, thus, is also modulated by respiration. For the noise variance monitoring, the noise samples were acquired without and with MR signal excitation on clinical 1.5/3 T MR scanners. The performance of the noise sensor was compared with the respiratory bellow and with the diaphragm displacement visible on MR images. Several breathing patterns were tested. RESULTS: The noise variance demonstrated a periodic, temporal modulation that was synchronized with the respiratory bellow signal. The modulation depth of the noise variance resulting from the respiration varied between the channels of the array and depended on the channel's location with respect to the body. The noise sensor combined with MR acquisition was able to detect the respiratory motion for every k-space read-out line. CONCLUSION: Within clinical MR systems, the respiratory motion can be detected by the noise in receive array. The noise sensor does not require careful positioning unlike the bellow, any additional hardware, and/or MR acquisition. Magn Reson Med 77:221-228, 2017. © 2016 Wiley Periodicals, Inc.

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

PURPOSE: 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 B1+ 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 B1+ 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. © 2016 International Society for Magnetic Resonance in Medicine.

(1) H MRS in the human spinal cord at 7 T using a dielectric waveguide transmitter, RF shimming and a high density receive array.

Multimodal MRI is the state of the art method for clinical diagnostics and therapy monitoring of the spinal cord, with MRS being an emerging modality that has the potential to detect relevant changes of the spinal cord tissue at an earlier stage and to enhance specificity. Methodological challenges related to the small dimensions and deep location of the human spinal cord inside the human body, field fluctuations due to respiratory motion, susceptibility differences to adjacent tissue such as vertebras and pulsatile flow of the cerebrospinal fluid hinder the clinical application of (1) H MRS to the human spinal cord. Complementary to previous studies that partly addressed these problems, this work aims at enhancing the signal-to-noise ratio (SNR) of (1) H MRS in the human spinal cord. To this end a flexible tight fit high density receiver array and ultra-high field strength (7 T) were combined. A dielectric waveguide and dipole antenna transmission coil allowed for dual channel RF shimming, focusing the RF field in the spinal cord, and an inner-volume saturated semi-LASER sequence was used for robust localization in the presence of B1 (+) inhomogeneity. Herein we report the first 7 T spinal cord (1) H MR spectra, which were obtained in seven independent measurements of 128 averages each in three healthy volunteers. The spectra exhibit high quality (full width at half maximum 0.09 ppm, SNR 7.6) and absence of artifacts and allow for reliable quantification of N-acetyl aspartate (NAA) (NAA/Cr (creatine) 1.31 ± 0.20; Cramér-Rao lower bound (CRLB) 5), total choline containing compounds (Cho) (Cho/Cr 0.32 ± 0.07; CRLB 7), Cr (CRLB 5) and myo-inositol (mI) (mI/Cr 1.08 ± 0.22; CRLB 6) in 7.5 min in the human cervical spinal cord. Thus metabolic information from the spinal cord can be obtained in clinically feasible scan times at 7 T, and its benefit for clinical decision making in spinal cord disorders will be investigated in the future using the presented methodology. Copyright © 2016 John Wiley & Sons, Ltd.

4D MR imaging using robust internal respiratory signal.

The purpose of this study is to investigate the feasibility of using internal respiratory (IR) surrogates to sort four-dimensional (4D) magnetic resonance (MR) images. The 4D MR images were constructed by acquiring fast 2D cine MR images sequentially, with each slice scanned for more than one breathing cycle. The 4D volume was then sorted retrospectively using the IR signal. In this study, we propose to use multiple low-frequency components in the Fourier space as well as the anterior body boundary as potential IR surrogates. From these potential IR surrogates, we used a clustering algorithm to identify those that best represented the respiratory pattern to derive the IR signal. A study with healthy volunteers was performed to assess the feasibility of the proposed IR signal. We compared this proposed IR signal with the respiratory signal obtained using respiratory bellows. Overall, 99% of the IR signals matched the bellows signals. The average difference between the end inspiration times in the IR signal and bellows signal was 0.18 s in this cohort of matching signals. For the acquired images corresponding to the other 1% of non-matching signal pairs, the respiratory motion shown in the images was coherent with the respiratory phases determined by the IR signal, but not the bellows signal. This suggested that the IR signal determined by the proposed method could potentially correct the faulty bellows signal. The sorted 4D images showed minimal mismatched artefacts and potential clinical applicability. The proposed IR signal therefore provides a feasible alternative to effectively sort MR images in 4D.