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Register a new userA fast field-cycling MRI relaxometer for physical contrasts design and pre-clinical studies in small animals
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We present a fast field-cycling NMR relaxometer with added magnetic resonance imaging capabilities. The instrument operates at a maximum proton Larmor frequency of 5 MHz for a sample volume of 35 mL. The magnetic field homogeneity across the sample is 1400 ppm. The main field is generated with a notch-coil electromagnet of own design, fed with a current whose stability is 220 ppm. We show that images of reasonable quality can still be produced under such conditions. The machine is being designed for concept testing of the involved instrumentation and specific contrast agents aimed for field-cycling magnetic resonance imaging applications. The general performance of the prototype was tested through localized relaxometry experiments, T1-dispersion weighted images, temperature maps and T1-weighted images at different magnetic field intensities. We introduce the concept of positive and negative contrast depending on the use of pre-polarized or non-polarized sequences. The system is being improved for pre-clinical studies in small animals.
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Contrast agents with a strong $R_1$ dispersion have been shown to be effective in generating target-specific contrast in MRI. The utilization of this $R_1$ field dependence requires the adaptation of a MRI scanner for fast field-cycling (FFC). Here, we present the first implementation and validation of FFC-MRI at a clinical field strength of 3 T. A field-cycling range of $pm$100 mT around the nominal $B_0$ field was realized by inserting an additional insert coil into an otherwise conventional MRI system. System validation was successfully performed with selected iron oxide magnetic nanoparticles and comparison to FFC-NMR relaxometry measurements. Furthermore, we show proof-of-principle $R_1$ dispersion imaging and demonstrate the capability of generating R1 dispersion contrast at high field with suppressed background signal. With the presented ready-to-use hardware setup it is possible to investigate MRI contrast agents with a strong R1 dispersion at a field strength of 3 T.
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisition protocols, the inverse problem that recovers the SM parameters from a set of MR diffusion measurements using pairs of short pulsed field gradients was shown to be ill-posed. Multidimensional dMRI was shown to solve this problem by combining linear and planar tensor encoding data. Given sufficient measurements, multiple choices of b-tensor sets provide enough information to estimate all SM parameters. However, in the presence of noise, some sets will provide better results. In this work, we develop a framework for optimal experimental design of multidimensional dMRI sequences applicable to the SM. This framework is based on maximising the determinant of the Fisher information matrix, which is averaged over the full SM parameter space. This averaging provides a fairly objective information metric tailored for the expected signal but that only depends on the acquisition configuration. The optimisation of this metric can be further restricted to any subclass of desirable design constraints like, for instance, hardware-specific constraints. In this work, we compute the optimal acquisitions over the set of all b-tensors with fixed eigenvectors.
Objective: Accurate estimation of SAR is critical to safeguarding vulnerable patients who require an MRI procedure. The increased static field strength and RF duty cycle capabilities in modern MRI scanners mean that systems can easily exceed safe SAR levels for patients. Advisory protocols routinely used to establish quality assurance protocols are not required to advise on the testing of MRI SAR levels and is not routinely measured in annual medical physics quality assurance checks. This study aims to develop a head phantom and protocol that can independently verify global SAR for MRI clinical scanners. Methods: A four-channel birdcage head coil was used for RF transmission and signal reception. Proton resonance shift thermometry was used to estimate SAR. The SAR estimates were verified by comparing results against two other independent measures, then applied to a further four scanners at field strengths of 1.5 T and 3 T. Results: Scanner output SAR values ranged from 0.42 to 1.52 W/kg. Percentage SAR differences between independently estimated values and those calculated by the scanners differed by 0-2.3%. Conclusion: We have developed a quality assurance protocol to independently verify the SAR output of MRI scanners.
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets---collected with different scanners, protocols and disease populations---and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens scale of medial temporal atrophy (MTA), were included in this study. By training multip
In radial fast spin-echo MRI, a set of overlapping spokes with an inconsistent T2 weighting is acquired, which results in an averaged image contrast when employing conventional image reconstruction techniques. This work demonstrates that the problem may be overcome with the use of a dedicated reconstruction method that further allows for T2 quantification by extracting the embedded relaxation information. Thus, the proposed reconstruction method directly yields a spin-density and relaxivity map from only a single radial data set. The method is based on an inverse formulation of the problem and involves a modeling of the received MRI signal. Because the solution is found by numerical optimization, the approach exploits all data acquired. Further, it handles multi-coil data and optionally allows for the incorporation of additional prior knowledge. Simulations and experimental results for a phantom and human brain in vivo demonstrate that the method yields spin-density and relaxivity maps that are neither affected by the typical artifacts from TE mixing, nor by streaking artifacts from the incomplete k-space coverage at individual echo times.
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