Noiseless Gravitational Lensing Simulations

The microphysical properties of the DM particle can, in principle, be constrained by the properties and abundance of substructures in DM halos, as measured through strong gravitational lensing. Unfortunately, there is a lack of accurate theoretical predictions for the lensing signal of substructures, mainly because of the discreteness noise inherent to N-body simulations. Here we present Recursive-TCM, a method that is able to provide lensing predictions with an arbitrarily low discreteness noise, without any free parameters or smoothing scale. This solution is based on a novel way of interpreting the results of N-body simulations, where particles simply trace the evolution and distortion of Lagrangian phase-space volume elements. We discuss the advantages of this method over the widely used cloud-in-cells and adaptive-kernel smoothing density estimators. Applying the new method to a cluster-sized DM halo simulated in warm and cold DM scenarios, we show how the expected differences in their substructure population translate into differences in the convergence and magnification maps. We anticipate that our method will provide the high-precision theoretical predictions required to interpret and fully exploit strong gravitational lensing observations.Comment: 13 pages, 13 figures. Updated fig 12, references adde

Accelerated Quantitative Mapping of Relaxation Times in MRI

Clinical diagnosis ideally relies on quantitative measures of disease. For a number of diseases, diagnostic guidelines require or at least recommend neuroimaging exams to support the clinical findings. As such, there is also an increasing interest to derive quantitative results from magnetic resonance imaging (MRI) examinations, i.e. images providing quantitative T1, T2, T2* tissue parameters. Quantitative MRI protocols, however, often require prohibitive long acquisition times (> 10 minutes), nor standards have been established to regulate and control MRI-based quantification. This work aims at exploring the technical feasibility to accelerate existing MRI acquisition schemes to enable a -3 minutes clinical imaging protocol of quantitative tissue parameters such as T2 and T2* and at identifying technical factors that are key elements to obtain accurate results. In the first part of this thesis, the signal model of an existing quantitative T2-mapping algorithm is expanded to explore the methodology for a broader use including the application to T2* and its use in the presence of imperfect imaging conditions and system related limitations of the acquisition process. The second part of this thesis is dedicated to optimize the iterative mapping algorithm for a robust clinical application including the integration on a clinical MR platform. This translation of technology is a major step to enable and validate such new methodology in a realistic clinical environment. The robustness and accuracy of the developed and implemented model is investigated by comparing with the "gold standard" information from fully sampled phantom and in-vivo MRI data

Development and Optimization of Methods for Accelerated Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) has yielded great success as a medical imaging modality in the past decades, and its excellent soft tissue contrast is used in clinical routine to support diagnosis today. However, MRI is still facing challenges. For example, the acquisition time is long in comparison to computed tomography, especially when directly measuring tissue properties with quantitative MRI. This thesis presents new approaches to accelerate quantitative MRI acquisitions without decreasing the accuracy, using analytical and numerical signal models. A quantitative acquisition to map the transverse relaxation T2 was first accelerated by combining parallel imaging with model-based reconstruction. It was demonstrated that the combination leads to an improved artifact behavior in comparison to a model-based reconstruction alone, facilitating higher acceleration factors. The technique was optimized to obtain T2 maps from the brain, knee, prostate and liver, with good initial results. The idea of combining methods was continued by introducing simultaneous multi slice acquisition to the T2 mapping approach. Furthermore, a numerical simulation rather than an analytical solution was used in the model-based reconstruction, resulting in a fast undersampled acquisition that also accounts for transmit field inhomogeneity. This approach yielded more accurate and faster acquired T2 values. Magnetic resonance fingerprinting (MRF) is a recently introduced model-based reconstruction that promises to provide multiple quantitative maps using a fast pseudo-random acquisition. However, similar to other model-based approaches, MRF depends on how well the model describes the measured signal. It was demonstrated in this work that the estimated quantitative maps may be systematically biased if the model does not account for magnetization transfer effects. To this end, a simplified numerical model was proposed, that includes magnetization transfer, and yields more accurate quantitative values. The same approach was translated to bSSFP acquisitions, where banding artifacts are a major limitation: the analytical model of a phase-cycle bSSFP acquisition was used to separate signal effects of the human tissue from signal effects due to magnetic field inhomogeneity. The separation allowed the removal of typical signal voids in bSSFP images. A compressed sensing reconstruction was employed to avoid additional acquisition time. In summary, this thesis has introduced new approaches to employ signal models in different applications, with the aim of either accelerating an acquisition, or improving the accuracy of an existing fast method. These approaches may help to make the next step away from qualitative towards a fully quantitative MR imaging modality, facilitating precision medicine and personalized treatment

Fat fraction mapping using bSSFP Signal Profile Asymmetries for Robust multi-Compartment Quantification (SPARCQ)

Purpose: To develop a novel quantitative method for detection of different tissue compartments based on bSSFP signal profile asymmetries (SPARCQ) and to provide a validation and proof-of-concept for voxel-wise water-fat separation and fat fraction mapping. Methods: The SPARCQ framework uses phase-cycled bSSFP acquisitions to obtain bSSFP signal profiles. For each voxel, the profile is decomposed into a weighted sum of simulated profiles with specific off-resonance and relaxation time ratios. From the obtained set of weights, voxel-wise estimations of the fractions of the different components and their equilibrium magnetization are extracted. For the entire image volume, component-specific quantitative maps as well as banding-artifact-free images are generated. A SPARCQ proof-of-concept was provided for water-fat separation and fat fraction mapping. Noise robustness was assessed using simulations. A dedicated water-fat phantom was used to validate fat fractions estimated with SPARCQ against gold-standard 1H MRS. Quantitative maps were obtained in knees of six healthy volunteers, and SPARCQ repeatability was evaluated in scan rescan experiments. Results: Simulations showed that fat fraction estimations are accurate and robust for signal-to-noise ratios above 20. Phantom experiments showed good agreement between SPARCQ and gold-standard (GS) fat fractions (fF(SPARCQ) = 1.02*fF(GS) + 0.00235). In volunteers, quantitative maps and banding-artifact-free water-fat-separated images obtained with SPARCQ demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ fat fraction was 0.0512. Conclusion: The SPARCQ framework was proposed as a novel quantitative mapping technique for detecting different tissue compartments, and its potential was demonstrated for quantitative water-fat separation.Comment: 20 pages, 7 figures, submitted to Magnetic Resonance in Medicin

Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

ObjectiveThe application value of T2 mapping in evaluating endometrial carcinoma (EMC) features remains unclear. The aim of the study was to determine the quantitative T2 values in EMC using a novel accelerated T2 mapping, and evaluate them for detection, classification,and grading of EMC.Materials and methodsFifty-six patients with pathologically confirmed EMC and 17 healthy volunteers were prospectively enrolled in this study. All participants underwent pelvic magnetic resonance imaging, including DWI and accelerated T2 mapping, before treatment. The T2 and apparent diffusion coefficient (ADC) values of different pathologic EMC features were extracted and compared. Receiver operating characteristic (ROC) curve analysis was performed to analyze the diagnostic efficacy of the T2 and ADC values in distinguishing different pathological features of EMC.ResultsThe T2 values and ADC values were significantly lower in EMC than in normal endometrium (bothl p < 0.05). The T2 and ADC values were significantly different between endometrioid adenocarcinoma (EA) and non-EA (both p < 0.05) and EMC tumor grades (all p < 0.05) but not for EMC clinical types (both p > 0.05) and depth of myometrial invasion (both p > 0.05). The area under the ROC curve (AUC) was higher for T2 values than for ADC values in predicting grade 3 EA (0.939 vs. 0.764, p = 0.048). When combined T2 and ADC values, the AUC for predicting grade 3 EA showed a significant increase to 0.947 (p = 0.03) compared with those of ADC values. The T2 and ADC values were negatively correlated with the tumor grades (r = -0.706 and r = -0.537, respectively).ConclusionQuantitative T2 values demonstrate potential suitability in discriminating between EMC and normal endometrium, EA and non-EA, grade 3 EA and grade 1/2 EA. Combining T2 and ADC values performs better in predicting the histological grades of EA in comparison with ADC values alone

Compressed Sensing with Signal Averaging for Improved Sensitivity and Motion Artifact Reduction in Fluorine-19 MRI

Fluorine-19 (19F) MRI of injected perfluorocarbon emulsions (PFCs) allows for the non-invasive quantification of inflammation and cell tracking, but suffers from a low signal-to-noise ratio and extended scan time. To address this limitation, we tested the hypothesis that a 19F MRI pulse sequence that combines a specific undersampling regime with signal averaging has increased sensitivity and robustness against motion artifacts compared to a non-averaged fully-sampled dataset, when both are reconstructed with compressed sensing. To this end, numerical simulations and phantom experiments were performed to characterize the point spread function (PSF) of undersampling patterns and the vulnerability to noise of acquisition-reconstruction strategies with paired numbers of x signal averages and acceleration factor x (NAx-AFx). At all investigated noise levels, the DSC of the acquisition-reconstruction strategies strongly depended on the regularization parameters and acceleration factor. In phantoms, motion robustness of an NA8-AF8 undersampling pattern versus NA1-AF1 was evaluated with simulated and real motions. Differences were assessed with Dice similarity coefficients (DSC), and were consistently higher for NA8-AF8 compared to NA1-AF1 strategy, for both simulated and real cyclic motions (P<0.001). Both acquisition-reconstruction strategies were validated in vivo in mice (n=2) injected with perfluoropolyether. These images displayed a sharper delineation of the liver with the NA8-AF8 strategy than with the NA1-AF1 strategy. In conclusion, we validated the hypothesis that in 19F MRI, the combination of undersampling and averaging improves both the sensitivity and the robustness against motion artifacts compared to a non-averaged fully-sampled dataset, when both are reconstructed with compressed sensing

Model-Based Super-Resolution Reconstruction of T2 Maps

Purpose High-resolution isotropic T-2 mapping of the human brain with multi-echo spin-echo (MESE) acquisitions is challenging. When using a 2D sequence, the resolution is limited by the slice thickness. If used as a 3D acquisition, specific absorption rate limits are easily exceeded due to the high power deposition of nonselective refocusing pulses. A method to reconstruct 1-mm(3) isotropic T-2 maps is proposed based on multiple 2D MESE acquisitions. Data were undersampled (10-fold) to compensate for the prolonged scan time stemming from the super-resolution acquisition. Theory and Methods The proposed method integrates a classical super-resolution with an iterative model-based approach to reconstruct quantitative maps from a set of undersampled low-resolution data. The method was tested on numerical and multipurpose phantoms, and in vivo data. T-2 values were assessed with a region-of-interest analysis using a single-slice spin-echo and a fully sampled MESE acquisition in a phantom, and a MESE acquisition in healthy volunteers. Results Numerical simulations showed that the best trade-off between acceleration and number of low-resolution datasets is 10-fold acceleration with 4 acquisitions (acquisition time = 18 min). The proposed approach showed improved resolution over low-resolution images for both phantom and brain. Region-of-interest analysis of the phantom compartments revealed that at shorter T-2, the proposed method was comparable with the fully sampled MESE. For the volunteer data, the T-2 values found in the brain structures were consistent across subjects (8.5-13.1 ms standard deviation). Conclusion The proposed method addresses the inherent limitations associated with high-resolution T-2 mapping and enables the reconstruction of 1 mm(3) isotropic relaxation maps with a 10 times faster acquisition

TrueCISS: Genuine bSSFP Signal Reconstruction from Undersampled Multiple-Acquisition SSFP Using Model-Based Iterative Non-Linear Inversion

Balanced steady-state free-precession (bSSFP) is prone to local field inhomogeneities, typically appearing as signal voids, i.e. banding-artifacts. A new method, termed true constructive interference in steady state (trueCISS), is proposed based on the acquisition of eight highly undersampled bSSFP k-spaces with different radio-frequency (RF) phase increments. A model-based non-linear inversion is used to fit the bSSFP signal model onto the undersampled data, effectively estimating parameter maps that allow synthesizing the genuine bSSFP signal over the whole image, thus without any noticeable banding artifacts