Dahlgaardebbesen0271

Clinical Relevance- We map hemodynamic lag using breathhold fMRI, providing insight into vascular transit times and improving the regional accuracy of cerebrovascular reactivity measurements.The susceptibility-based positive contrast MR technique was applied to estimate arbitrary magnetic susceptibility distributions of the metallic devices using a kernel deconvolution algorithm with a regularized L-1 minimization. Previously, the first-order primal-dual (PD) algorithm could provide a faster reconstruction time to solve the L-1 minimization, compared with other methods. Here, we propose to accelerate the PD algorithm of the positive contrast image using the multi-core multi-thread feature of graphics processor units (GPUs). The some experimental results showed that the GPU-based PD algorithm could achieve comparable accuracy of the metallic interventional devices in positive contrast imaging with less computational time. And the GPU-based PD approach was 4~15 times faster than the previous CPU-based scheme.Clinical Relevance-This can estimate arbitrary magnetic susceptibility distributions of the metallic devices with the processing efficacy of 4~15 times faster than before.Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly learned from data. These data-driven priors typically remain unchanged for future data in the testing phase once they are learned during training. In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach. While the proposed approach can compromise the extremely fast reconstruction time of deep learning MRI methods, our results on knee MRI indicate that such adaptation can substantially reduce the remaining artifacts in reconstructed images. In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data.Physiological parameters can be estimated from dynamic contrast enhanced magnetic resonance imaging (DCEMRI) data using pharmacokinetic models. This work evaluates the performance of various pharmacokinetic models through a retrospective study on cervix cancer, including two generalized kinetic models and three 2-compartment exchange models (2CXMs). In the current clinical practice, region of interest (ROI) is treated as a whole and the models are assessed by their top pharmacokinetic parameters. We explore the intervoxel relationship in the pharmacokinetic parameter maps and demonstrate that, for those insignificant parameters, texture descriptors can largely improve their discriminative power. Multi-parametric classifiers are developed to fuse the information carried by physiological parameters and the descriptors. Assessed merely by the top parameter, the DP (distributed parameter) model is the best one with an area under the ROC (receiver operating characteristic) curve (AUC) of 0.80; by combining multiple pharmacokinetic parameters, the ExTofts model is the winner with an AUC of 0.837. Finally, the classifier of the AATH (adiabatic approximation to the tissue homogeneity) model build on combined features achieves an AUC of 0.92.Clinical Relevance - Using data from 36 cervical cancer patients and 17 normal subjects, this work quantitatively compared the various pharmacokinetic models and provided recommendations for model selection in cervical cancer diagnosis.The benefits of array coils in MRI and MRS are well known. A key component of essentially all array coils used today is the decoupling preamplifier. Unlike conventional 50 ohm low-noise preamps, decoupling preamps present a reactive impedance to the coil, which can be used to 'block' currents from being induced in the receive coil, reducing the impact of any electromagnetic coupling between array elements. While available from a number of vendors, a lower-cost solution would be advantageous. We investigate the use of conventional operational amplifiers as low-noise decoupling preamplifiers. In this paper the performance of the op-amp preamplifier is compared to conventional 50 Ω. The op-amp preamp design shows promise for use as a decoupling preamplifier with array coils.Clinical Relevance- This work could facilitate the development of array coils for spectroscopy and imaging.We present methods to harvest wireless power directly from the MRI RF field. The system includes a harvester coil to capture RF energy and an RF-DC converter for rectification. Energy harvesting by the harvester coil is modeled as a function of the MRI B1 RF field. Avelumab mouse Rectification is modeled using power-dependent large signal S-parameter simulation. A novel reference impedance-based modeling approach is leveraged to cascade models for linear inductive coupling and nonlinear diode rectification, and validated. The method permits independent optimization of harvester coils and RF-DC converters to maximize harvesting efficiency. Feasibility of this technique is demonstrated by implementing concurrent in-bore wireless power harvesting and MRI scanning on a clinical system. The effect of artifacts on image quality is also investigated.Clinical Relevance- In-bore wireless harvesting can provide power for medical accessories during MRI, with minimal system modification and cost.This work presents a new method to achieve accelerated, high-resolution magnetic resonance spectroscopic imaging (MRSI) with spin-echo excitations. A new data acquisition strategy is proposed that integrates adiabatic refocusing, elimination of lipid suppression, rapid spatiospectral encoding with sparse (k,t)-space sampling, and interleaved water navigators. This integration leads to a significantly improved combination of volume coverage, spatial resolution (approximately 3 × 3.4 × 4 mm3) and speed ( less then 10 minutes), while eliminating additional scans for field mapping and coil sensitivity estimation. A data processing strategy that integrates parallel imaging reconstruction and subspace-based processing is devised to produce high-SNR spatiospectral reconstruction from the sparsely sampled, noisy and highresolution MRSI data. Promising in vivo results have been obtained to demonstrate the potential of the proposed method.Clinical relevance- The proposed method enabled volumetric MRSI with a nominal resolution of 3 × 3.