Model-based reconstruction of accelerated quantitative magnetic resonance imaging (MRI)

Quantitative MRI refers to the determination of quantitative parameters (T1,T2,diffusion, perfusion etc.) in magnetic resonance imaging (MRI). The ’parameter maps’ are estimated from a set of acquired MR images using a parameter model, i.e. a set of mathematical equations that describes the MR images as a function of the parameter(s). A precise and accurate highresolution estimation of the parameters is needed in order to detect small changes and/or to visualize small structures. Particularly in clinical diagnostics, the method provides important information about tissue structures and respective pathologic alterations. Unfortunately, it also requires comparatively long measurement times which preclude widespread practical applications. To overcome such limitations, approaches like Parallel Imaging (PI) and Compressed Sensing (CS) along with the model-based reconstruction concept has been proposed. These methods allow for the estimation of quantitative maps from only a fraction of the usually required data. The present work deals with the model-based reconstruction methods that are applicable for the most widely available Cartesian (rectilinear) acquisition scheme. The initial implementation was based on accelerating the T*2 mapping using Maximum Likelihood estimation and Parallel Imaging (PI). The method was tested on a Multiecho Gradient Echo (MEGE) T*2 mapping experiment in a phantom and a human brain with retrospective undersampling. Since T*2 is very sensitive to phase perturbations as a result of magnetic field inhomogeneity further work was done to address this. The importance of coherent phase information in improving the accuracy of the accelerated T*2 mapping fitting was investigated. Using alternating minimization, the method extends the MLE approach based on complex exponential model fitting which avoids loss of phase information in recovering T*2 relaxation times. The implementation of this method was tested on prospective(real time) undersampling in addition to retrospective. Compared with fully sampled reference scans, the use of phase information reduced the error of the accelerated T*2 maps by up to 20% as compared to baseline magnitude-only method. The total scan time for the four times accelerated 3D T*2 mapping was 7 minutes which is clinically acceptable. The second main part of this thesis focuses on the development of a model-based super-resolution framework for the T2 mapping. 2D multi-echo spin-echo (MESE) acquisitions suffer from low spatial resolution in the slice dimension. To overcome this limitation while keeping acceptable scan times, we combined a classical super-resolution method with an iterative model-based reconstruction to reconstruct T2 maps from highly undersampled MESE data. Based on an optimal protocol determined from simulations, we were able to reconstruct 1mm3 isotropic T2 maps of both phantom and healthy volunteer data. Comparison of T2 values obtained with the proposed method with fully sampled reference MESE results showed good agreement. 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 method. These approaches may help to take the next step away from qualitative towards a fully quantitative MR imaging modality, facilitating precision medicine and personalized treatment

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

Sampling order optimization preserves contrast and improves clinical diagnostic utility of accelerated prospective 3D brain MRI: a radiological assessment study on healthy volunteers

The code and data herein distributed reproduce the results published in the paper: Arnold JulianVinoj Benjamin, Wajiha Bano, Grant Mair, Mike Davies, and Ian. Marshall,“Sampling order optimization preserves contrast and improves clinical diagnostic utility of accelerated prospective 3D brain MRI: a radiological assessment study on healthy volunteers”. In: Proc Intl Soc Mag Reson Med 2018, #3189. The data comprises of 3D brain datasets from eight healthy volunteers. Each dataset contains raw data from a fully sampled scan and three accelerated scans that are described in the aforementioned paper.Benjamin, Arnold; Bano, Wajiha; Mair, Grant; Davies, Michael; Marshall, Ian. (2019). Sampling order optimization preserves contrast and improves clinical diagnostic utility of accelerated prospective 3D brain MRI: a radiological assessment study on healthy volunteers, [dataset]. Institute for Digital Communications. University of Edinburgh. https://doi.org/10.7488/ds/2486

Improved Accuracy of Accelerated 3D T2* Mapping with Coherent Parallel Maximum Likelihood Estimation

The purpose of this study is to investigate the importance of coherent phase information in improving the accuracy of the accelerated T2* mapping as compared to the magnitude-only fitting. Our approach extends the acceleration attained in Parallel Imaging (PI) to Maximum Likelihood Estimation (MLE) approach based on complex exponential model fitting which avoids phase information loss in recovering T2* relaxation times. The method was tested on a Multiecho Gradient Echo (MEGE) T2* mapping experiment in a numerical phantom and a healthy human brain data with retrospective and prospective (real time) undersampling. Compared with fully sampled reference scans, the use of phase information reduces the error of the accelerated T2* maps up to 20% as compared to baseline magnitude-only method. Coherent fitting results in better denoising and additionally provides valuable information about magnetic field inhomogeneity. The total scan time is clinically acceptable.Bano, Wajiha; Golbabaee, Mohammad; Benjamin, Arnold; Marshall, Ian; Davies, Mike. (2018). Improved Accuracy of Accelerated 3D T2* Mapping with Coherent Parallel Maximum Likelihood Estimation, 2016-2018 [dataset]. University of Edinburgh. Institute for Digital Communications. https://doi.org/10.7488/ds/2428

GC-MS analysis, antimicrobial, antioxidant, antilipoxygenase and cytotoxic activities of Jacaranda mimosifolia methanol leaf extracts and fractions.

Jacaranda mimosifolia trees are grown in frost-free regions globally. The aim of this study was to evaluate the methanol crude extract and various fractions of increasing polarity of J. mimosifolia leaves for bioactive metabolites, as well as antimicrobial, antioxidant and anticancer activities. The anti-inflammatory potential of the various fractions of J. mimosifolia leaf extract was studied via the lipoxygenase (LOX) inhibitory assay. Methanol crude extract (ME), derived fractions extracted with chloroform (CF) and ethyl acetate (EAF), and residual aqueous extract (AE) of dried J. mimosifolia leaves were assayed for polyphenolic compounds, their antioxidant, antimicrobial and lipoxygenase (LOX) inhibitory activities, and anticancer properties. Polyphenolic compounds were determined via HPLC while phytochemicals (total phenolics, flavonoids, tannins and ortho-diphenol contents), antioxidant activities (DPPH, hydrogen peroxideperoxide, hydroxyl and superoxide radical anions) and LOX were measured via spectrophotometry. Methanol extracts and various fractions were evaluated for antibacterial activities against Bacillus subtilis, Klebsiella pneumonia, Pseudomonas aeruginosa and Staphylococcus aureus. Antifungal potential of the fractions was tested against three species: Aspergillus flavus, Aspergillus fumigatus and Fusarium oxysporum. The highest values for total phenolic content (TPC), total flavonoid content (TFC), flavonols, tannins and ortho-diphenols were in the ME, followed by CF > EAF > AE. ME also had the highest antioxidant activity with EC50 values 48±1.3, 45±2.4, 42±1.3 and 46±1.3 μg/mL based on the DPPH, hydrogen peroxide, hydroxyl radical and superoxide radical assays, respectively. TPC and TFC showed a significant, strong and positive correlation with the values for each of these antioxidant activities. ME exhibited anti-inflammatory potential based on its LOX inhibitory activity (IC50 = 1.3 μg/mL). ME also had the maximum antibacterial and antifungal potential, followed by EAF > CF > AE. Furthermore, ME showed the strongest cytotoxic effect (EC50 = 10.7 and 17.3 μg/mL) against human hormone-dependent prostate carcinoma (LnCaP) and human lung carcinoma (LU-1) cell lines, respectively. Bioactive compounds present in leaf methanol extracts of J. mimosifolia were identified using gas chromatography-mass spectrometry (GC-MS). Fifteen compounds were identified including phenolic and alcoholic compounds, as well as fatty acids. Our results suggest that J. mimosifolia leaves are a good source of natural products with antioxidant, anti-inflammatory and anti-cancer properties for potential therapeutic, nutraceutical and functional food applications