Browsing by Subject "MRI"

Purpose: A deep learning framework based on a conditional Generative Adversarial Network (cGAN) was developed to synthesize high-contrast Magnetization Prepared Rapid Gradient Echo (MP-RAGE) images from common spin-echo MR imaging sequences. This framework utilizes combinations of inputs from T1-weighted (T1-w), T2-weighted (T2-w), and Proton Density-weighted (PD-w) images. The primary objective was to augment the diversity of clinical data by capitalizing on the inherent advantages of MP-RAGE imaging, such as superior contrast, while mitigating its susceptibility to metal artifacts.Methods and Materials: A cGAN image synthesis model, incorporating a U-Net-based generator and a Patch GAN discriminator, was developed. The training was conducted across four distinct configurations, employing combinations of T1-w, T2-w, and PD-w images as inputs to synthesize MP-RAGE images, with and without Proton Density (PD) information, designated as PD-0 and PD-1, respectively. For training, data from 51 patients, comprising 8,160 slices, were used, following a training-to-validation ratio of 90:10. For prediction, data from 14 patients, comprising 2,240 slices, were utilized. The efficacy of the synthesized MP-RAGE images was evaluated using a suite of quantitative metrics, including Mean Absolute Error (MAE), Normalized Cross-Correlation (NCC), Percentage Mutual Information (PMI), and Structural Similarity Index (SSIM). Additionally, a Freesurfer brain segmentation task was performed on both synthesized and ground truth brain images, with the fidelity of synthesized images being indirectly assessed by the calculated Dice coefficient. Results: It was observed that the cGAN-synthesized MP-RAGE images exhibited comparable contrast to the ground truth in the axial view. A decrease in input channel numbers resulted in diminished contrast between certain anatomical structures in the synthetic MP-RAGE images, albeit within an acceptable range. The MAE approached (0.02±0.01), the PMI for two Three-in-One-out synthesis approached(0.76±0.07), the NCC was about (0.91±0.05), and the SSIM was about (0.9±0.1). The Freesurfer segmentation results showed desirable Dice coefficients (mostly above 0.8) for different kinds of inputs, except the One-in-One-out T1-w synthesis. Conclusion: The cGAN framework developed in this study has proven to be a robust and versatile tool for synthesizing high-contrast MP-RAGE images, even in scenarios with single-channel input images. The Freesurfer segmentation results demonstrated that the synthesized MP-RAGE images are highly similar to the ground truth in segmentation tasks, underscoring the potential clinical and research value of the proposed image synthesis model.

Controlled breathing techniques are widely used to help people manage pain, and there is growing interest in using these approaches during painful outpatient medical procedures. The outpatient MRI-guided breast biopsy is one setting where patients may particularly benefit from breathing interventions for pain. To date, however, no studies have examined interventions for pain reduction in this setting. This randomized controlled pilot study assessed the feasibility, acceptability, and efficacy of a novel audio-recorded controlled breathing intervention for reducing breast and body pain in women undergoing MRI-guided breast biopsy. Fifty-eight women undergoing MRI-guided breast biopsy were randomized to a 1) controlled breathing intervention or 2) usual care condition. Assessments of pain, anxiety, distraction from pain, relaxation, blood pressure, heart rate, pain catastrophizing, and self-efficacy for managing pain and anxiety were administered. Participants were assessed at baseline, during biopsy, immediately post-biopsy, and 24 hours post-biopsy. Results demonstrated that the intervention was feasible and acceptable. However, when compared to usual care, controlled breathing did not significantly reduce pain, increase distraction from pain or relaxation during biopsy, decrease physiological reactivity, reduce pain catastrophizing, or increase self-efficacy for pain and anxiety from pre- to post-biopsy. These findings could be used to revise the controlled breathing intervention.

Recent work has shown that hyperpolarized magnetic resonance spectroscopy (HP-MRS) can trace in vivo metabolism of biomolecules and is therefore extremely promising for diagnostic imaging. The most severe challenge this technique faces is the short signal lifetime for hyperpolarization, which is dictated by the spin-lattice (T1) relaxation. In this thesis we show with theory, simulation and experiment that the long-lived nuclear spin states in chemically equivalent or near equivalent spin systems offer a solution to this problem. Spin polarization that has lifetime much longer than T1 (up to 70-fold) has been demonstrated with pulse sequence techniques that are compatible with clinical imaging settings. Multiple classes of molecules have been demonstrated to sustain such long-lived hyperpolarization.

Brain Positron emission tomography (PET) has been widely employed for the clinic diagnosis of Alzheimer's disease (AD). Studies have shown that PET imaging is helpful in differentiating healthy elderly individuals, mild cognitive impairment (MCI) individuals, and AD individuals (Nordberg, Rinne, Kadir, & Långström, 2010). However, PET image quality and quantitative accuracy is degraded from partial volume effects (PVEs), which are due to the poor spatial resolution of PET. As a result, the compensation of PVEs in PET may be of great significance in the improvement of early diagnosis of AD. There are many different approaches available to address PVEs including region-based methods and voxel-based methods. In this study, a voxel-based PVE compensation technique using high-resolution anatomical images was investigated. The high-resolution anatomical images could be computed tomography (CT) or magnetic resonance imaging (MRI) images. Such methods have been proposed and investigated in many studies (Vunckx et al., 2012). However, relatively little research has been done on comparing the effects of different MRI images on voxel-based PVE correction methods. In this study, we compare the effect of 6 different MRI image protocols on PVE compensation in PET images. The MRI protocols compared in this study are T1-, T2-, proton-density (PD)-weighted and 3 different inversion recovery MRI protocols.

Results: OSEM and MAP/ICD images with isotropic prior are blurry and/or noisy. Compared with the OSEM and MAP/ICD images obtained by using an isotropic prior, the PET image reconstructed using anatomical information show better contrast and less noise. Visually, the PET image reconstructed with the ZeroCSF prior gave the PET image that visually appears to match best with the PET phantom. PET images reconstructed with T2, PD and ZeroWM image are similar to one another in image quality, but relative to the PET phantom and the ZeroCSF PET image, these images have poor contrast between CSF pockets and surrounding GM tissue, and they have less contrast between GM and WM. PET image reconstructed with T1 image had a better GM and CSF contrast, some of the CSF pockets in GM were reconstructed, but the WM region was very noisy. PET images reconstructed with ZeroGM image had noticeably worse performance on the GM reconstruction. Analysis suggest that these effects are caused by differences in tissue contrast with different MRI protocols

Keywords: PET, MRI, partial volume effect, image reconstruction, SPECT, Alzheimer's disease.

Purpose: To investigate the inter-patient and inter-sequence variation in liver tumor contrast in MRI and the feasibility of improving the liver tumor contrast by using an in-house developed multi-source adaptive fusion method for use in MRI-based treatment planning.

Methods and Materials: MR-images from 29 patients were retrospectively reviewed in this study. The imaging sequences acquired by a 1.5T GE and 3T Siemens MR scanner consisted of T1-w, T1-w, Post C, T2-w, T2/T1-w, and DWI. Using an in-house developed MSAF algorithm, we created fused images for a smaller subset of 12 patients using T1-w, T2-w, T2/T1-w, and DWI as inputs. Two fusion-images were obtained for each patient by implementing either an input-driven or output-driven fusion optimization method. Once a fusion-image was obtained an analysis was performed on each original image, and the fusion-image for each patient to calculate the tumor-to-tissue contrast-to-noise ratio(CNR) by contouring the tumor and a liver background-region(BG) in a homogeneous region of the liver using this in-house algorithm. CNR was calculated by (Itum-IBG)/SDBG, where Itum and IBG are the mean values of the tumor and the BG respectively, and SDBG is the standard deviation of the BG. To assess variation in tumor to tissue CNR for each image type an inter-patient coefficient-of-variation(CV) was calculated across all patients, as well as an inter-sequence CV. CV was calculated using the following: CV = σ/µ, where σ and µ are the standard deviation, and mean CNR for a single image sequence, respectively. These values were calculated for the original sequence types and fusion-images and compared.

Results: Our results from the 29 patients showed large inter-patient and inter-sequence variability, ranging from 86.90% to 67.03%, and 134.67% to 1.22% respectively. The T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT CV was 85.25%, 84.11%, 67.03%, 81.78%, 86.90%, and 74.30% respectively. Tumor CNR ranged from 0.95 to 4.47 with mean (± SD) CNR for T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT of 1.90 (±1.60), 2.12 (±1.42), 3.59 (±2.94), 1.95 (±1.70), 4.47 (±3.32), and 0.95 (±0.81) respectively. In the smaller subset of 12 patients, our results show a reduction in the inter-patient CV when using the in-house algorithm to obtain a tumor enhanced – fusion image. The inter-patient CV for T1-w, T2-w, T2/T1-w, DWI, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 94.16%, 112.73%, 105.69%, 124.23%, and 67.94% respectively. Tumor-CNR was significantly enhanced for each patient when using the in-house algorithm to obtain a tumor-enhanced image. The mean (± SD) CNR for T1-w, T2-w, T2/T1-w, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 2.11 (±1.99), 3.89 (±4.38), 3.71 (±3.92), 5.73 (±7.12), and 17.01 (±11.55) respectively.

Conclusion: The in-house multi-source adaptive fusion algorithm has the potential to increase the liver tumor contrast, as well as, improve the consistency for use in MRI based radiation therapy treatment planning.

Cone-beam computed tomography (CBCT) is one of the most commonly used image modalities in radiation therapy. It provides valuable information for target localization and outcome prediction throughout treatment courses. However, CBCT images suffer from various artifacts caused by scattering, beam hardening, undersampling, system hardware instability, and motions of the patient, which severely degrade the CBCT image quality. In addition, CBCT images have extremely poor soft-tissue contrast, making it almost impossible to accurately localize tumors in the soft tissue, such as liver tumors.

This dissertation presents the improvements of CBCT image quality for better outcome prediction and target localization by developing the deep learning and finite element based image enhancement model.

A deep learning based CBCT image enhancement model was developed to improve the radiomic feature accuracy. The model was trained based on 4D CBCT of ten patients and tested on three patients with different tumor sizes. The results show that 4D CBCT image quality can substantially affect the accuracy of the radiomic features, and the degree of impact is feature-dependent. The deep learning model was able to enhance the anatomical details and edge information in the 4D-CBCT as well as removing other image artifacts. This enhancement of image quality resulted in reduced errors for most radiomic

features. The average reduction of radiomics errors for 3 patients are 20.0%, 31.4%, 36.7%, 50.0%, 33.6% and 11.3% for histogram, GLCM, GLRLM, GLSZM, NGTDM and Wavelet features. And the error reduction was more significant for patients with larger tumors. To further improve the results, a patient-specific based training model has been developed. The model was trained based on the augmentation dataset of a single patient and tested on the different 4D CBCT of the same patient. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested.

In addition, a patient-specific deep learning model is proposed to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. A key innovation is using patient-specific CBCT-MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT-MRI images were augmented using translations and rotations to generate enough patient-specific training data. A U-Net-based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset.

Synthetic MRIs were quantitatively evaluated against ground-truth MRI. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal-to-noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images on the three patients tested. To further improve the robustness of synthetic MRI generation, we developed an organ specific biomechanical model. This model registers the pretreatment MRI images to onboard CBCT images based on the organ contours, and combines the MRI organ with CBCT body to the generate hybrid MRI/CBCT. 48 registration cases were performed, which includes 18 Monte Carlo simulated cases and 30 real patient cases. We identified tumor landmarks of hybrid MRI/CBCT, onboard CBCT and planning CT, and calculated errors of landmark locations of two CBCT images. The errors were calculated based on the landmark differences of two CBCT images and ground truth planning CT. The results show that the tumor landmark localization accuracy around tumor is improved by 54.2 ± 22.2 %.

PurposeCurrent testing requirement for individual coil elements are only once per year. Over time the coils degrade due to near continuous use and mishandling, causing elements to become faulty or die. Faulty coil elements may not immediately present themselves in clinical images, but do degrade the quality of the image. When enough elements have failed or degraded this can reduce diagnostic ability. This project serves to create an automated quality control process that tracks the performance of individual coil elements across a fleet of MRI scanners on a daily basis using raw clinical MRI data. Methods Utilizing third-party software we are able to collect the raw individual coil data from all localizer scans on the scanners and transfer this data to a network drive. Another computer then accesses this drive to process each of the localizer scans. The images are reconstructed from the raw k-space data, segmented, and the signal to noise ratio (SNR) is calculated. This SNR calculation is corrected for differences in acquisition parameters such that it can be trended across patients and over time. Results MR data was acquired using a variety of acceleration techniques for example parallel imaging and partial Fourier acquisition. Our code was designed to be robust enough to be

able to correctly reconstruct, segment, and process 85% of scans collected across four different MRI scanners. Segmentation code was able to automatically and accurately segment scans across a wide range of anatomy without any physicist or technician input. Corrections applied to SNR placed over 90% of functioning coil scans within the 100-300 range for SNR. Setting a threshold at a corrected SNR value of 50 allowed for the detection of three faulty coils in the span of three weeks before clinical indications began to show.Conclusion This work demonstrates that we can detect dead elements from clinical imaging data before otherwise apparent to physicians. Catching these failures early allows for replacement parts to be ordered and prevents patients from needing to be rescheduled. Many flagged scans were identified that were low SNR due to patient positioning rather than faulty coil elements. These cases were easily checked by physics to ensure that the coils were working properly.

Accurate delineation of tumor target and organs at risk (OARs) is critically important in modern radiation therapy for optimal treatment planning and precise treatment delivery. Conventionally, radiotherapy treatment planning uses 2D X-ray or 3D CT images to assist the delineation of tumor volumes and OARs, often under free-breathing condition. In modern radiation therapy, four-dimensional (4D) imaging is the state-of-art technique for imaging respiratory motion of thoracic and abdominal cancers. However, its application for abdominal cancers has been limited due to the low soft-tissue contrast of CT. Recently, a number of 4D magnetic resonance imaging (4D-MRI) techniques have been developed to overcome the limitations of 4D-CT in abdominal cancer applications. In contrast to 4D-CT, 4D-MRI imposes no ionizing radiation dose to the patient and offers superior soft-tissue contrast.

However, current 4D-MRI techniques also have limitations that prevent them from being widely adapted in routine clinical practices. For example, current techniques are all developed based on one particular MR sequence with a particular imaging contrast, which may not be optimal or consistent across patients. Furthermore, current 4D-MRI techniques are often affected by patients’ breathing variations, leading to significant motion artifacts in 4D-MRI images and adversely affects its image quality and thus clinical applicability.

The overall goal of this dissertation is to develop a novel 5D-MRI technique to overcome current limitations of 4D-MRI. The 5D-MRI technique is developed by synergizing three main technical components: multi-source MRI fusion (MSMF), 4D-MRI, and deformable image registration (DIR). 5D-MRI is an extension of 4D-MRI, embedding all the characterizations of 4D-MRI with an additional dimension of “image contrast”. The MSMF method consists of five key components: input MR images, image pre-processing, fusion algorithm, fusion adaptation, and output fused MR images. A linear-weighting fusion algorithm was implemented for MSMF in this study to demonstrate the proof of concept. Fusion options (weighting parameters and image features) are pre-determined with given input MR images and saved in a database for fast fusion adaption. 5D-MRI images were generated by applying the 4D displacement vector fields (4D-DVF) determined from the original 4D-MRI via DIR onto each of the fused MR images.

The 5D-MRI technique was tested on a digital human phantom (XCAT) and a liver tumor patient. 5D-MRI images were qualitatively evaluated for image contrast versatility, and quantitatively evaluated for image quality and motion accuracy. For the latter, motion trajectories of the liver tumor in the superior-inferior (SI) direction were determined from each of the 4D-MRI image sets of 5D-MRI and compared with those tracked on the single-slice sagittal cine MR images. Mean difference in displacement (D) and correlation coefficient (CC) were determined for each comparison.

For both the XCAT phantom and the liver tumor patient, the MSMF method produced more than a large number of fused MR images with versatile image contrasts and improved image quality, each presenting a unique set of anatomical and image features. Among twenty-four liver cancer patients, the average tumor CNR was significantly improved from a range of -1.80 to 8.05 in the four single contrast source MR images, to 22.51 in the fused MR images. The standard deviation of tumor CNR among the 24 patients was 0.87, which suggested high level of consistency. 5D-MRI images clearly demonstrated the respiratory motion during breathing in each 4D-MRI set. Liver tumor motion trajectories measured from 5D-MRI images closely matched with that from the reference cine MR images: D ranged from 0.402 to 0.561 mm (mean=0.463 mm) and CC ranged from 0.980 to 0.987 (mean=0.986).

In this dissertation, beyond the developmental work for the 5D-MRI technique, a novel organ segmentation method, which is an extension of the multi-source MRI fusion technique, and an image mutual-information (MI) based 4D-MRI improvement technique are also presented.

In summary, a novel 5D-MRI technique was developed and its feasibility on digital human phantom and liver tumor patients is demonstrated. Two exploratory projects extending and improving the 5D-MRI technique are also presented. The developed 5D-MRI technique is capable of producing a large number of synthetic 4D-MRI images with versatile image contrasts and improved image quality, holding great promises in enhancing MRI applications for radiation therapy.

Chronic respiratory diseases are one of the leading causes of death in the US and a driving factor in their mortality rate is the presence of comorbid cardiovascular diseases such as pulmonary hypertension (PH). As an increasing number of patients exhibit concomitant cardiac and pulmonary disease it becomes progressively more difficult to determine disease etiology and thus the optimal treatment course. The current standard diagnostic methods are insensitive to the underlying cause of gas exchange impairment, are unable to differentiate between phenotypes, and have limited utility in assessing disease progression or therapy response. The primary diagnostic tools for assessing pulmonary function are collectively referred to as pulmonary function tests (PFTs). While these tests are simple and non-invasive, they are also a global measurement that is effort-dependent and has poor reproducibility. Furthermore, PFTs cannot separate the contribution of concomitant disease on their measurements. The diagnosis of PH and subsequent determination of World Health Organization (WHO) classification requires invasive right heart catheterization (RHC) to meet strict hemodynamic cutoffs. However, the RHC interpretation can be challenging in patients with complex disease because the effect of comorbidities on RHC measurements is unknown. Therefore, new non-invasive diagnostic tools must be developed that can assess gas exchange impairment and pulmonary hemodynamics in tandem for patients to receive optimal treatment.

Hyperpolarized 129Xe MR imaging (MRI) and spectroscopy (MRS) have emerged as a powerful tool for assessing the pulmonary environment due xenon exhibiting distinct chemical shifts as it diffuses from the airspaces, through the alveolar membrane, and interacts with red blood cells (RBCs). This unique property allows the 129Xe signal to be decomposed in order to separately measure or image the three gas exchange compartments (gas, barrier, and RBC). 129Xe gas exchange imaging is beginning to show exquisite sensitivity to a range of obstructive and restrictive diseases. Still, despite this sensitivity to disease burden, 129Xe imaging techniques are unable to probe pulmonary hemodynamics. Thus, it does not provide sensitivity to PH, one of the possible causes of dyspnea. Previous work has demonstrated that in 129Xe MRS the characteristics of the spectral peaks can detect diffusion impairments present in interstitial lung disease (ILD). Yet current 129Xe MRS techniques only investigate static measurements of an inherently dynamic process. It is possible to extend 129Xe MRS and collect spectra as a time-series in dynamic spectroscopy to assess the cardiogenic changes in spectral parameters that may be associated with the hemodynamic changes in PH.

The objective of this work is to establish methods using 129Xe MRI/MRS to differentiate between diverse cardiopulmonary diseases. To this end, we develop the technique of 129Xe dynamic spectroscopy and assesses its utility in differentiating pre-capillary and post-capillary PH. We also investigate quality assurance metrics and tools including the repeatability of spectroscopic measurements and a thermally polarized xenon phantom to help facilitate the transition of 129Xe MRI/MRS into a clinical tool.

The foundation of 129Xe dynamic spectroscopy is the decomposition of each static spectrum into its three separate components. This is achieved by fitting each spectrum in the time-series to a mathematical model that describes the shape of each peak. In Chapter 3, to characterize the spectral parameters more accurately, we analyzed 6 different mathematical models for 129Xe dissolved-phase MR spectroscopy. We demonstrate that the optimized spectroscopic fitting model is a barrier Voigt model where the RBC peak has a Lorentzian lineshape and the barrier peak is a Voigt profile. This model was used in dynamic spectroscopy to extract the area, chemical shift, linewidth, and phase of each peak.

In principle, the dynamic variations in the spectral parameters of each 129Xe resonance detected during the cardiac cycle can contain vital information on pulmonary hemodynamics. Thus, in Chapter 4 we developed techniques to quantify and assess the temporal changes in the spectroscopic parameters during inhale, breath-hold, and exhalation. We observed a distinct cardiogenic oscillation in the amplitude and chemical shift of the RBC peak. This oscillation was quantified by its peak-to-peak height. Furthermore, we identified static and spectral parameters that are statistically different between healthy volunteers and subjects with idiopathic pulmonary fibrosis (IPF). This study demonstrated that that 129Xe dynamic spectroscopy is sensitive to disease.

The initial characterization of a diverse array of diseases is essential to understand the relationship between 129Xe spectroscopy and the cardiopulmonary environment. Thus, Chapter 5 characterizes 129Xe MRI/MRS in healthy volunteers and subjects with chronic obstructive pulmonary disease (COPD), IPF, left heart failure (LHF), and pulmonary arterial hypertension (PAH). The chosen cohorts provide two forms of chronic lung disease (IPF, COPD) and two forms of PH (LHF, PAH) that have different impedance locations with respect to the pulmonary capillary bed. LHF is a form of post-capillary PH because the impedance to blood flow is downstream of the pulmonary capillary bed as left ventricular dysfunction leads to a sustained increase in left atrial pressure. On the other hand, PAH is a form of pre-capillary PH caused by occlusions upstream of the capillary bed. We found that while gas exchange imaging is essential in the discrimination of obstructive and interstitial disease, only the height of oscillations in the RBC amplitude was able to differentiate between the different types of PH.

To test the utility of 129Xe MRI/MRS in differentiating PH status, we designed a diagnostic algorithm in Chapter 6 to distinguish between pre-capillary PH, post-capillary PH, no PH, and interstitial lung disease (ILD). Algorithm performance was tested in a single-blind reader study in which three expert readers used 129Xe MRI/MRS to determine the PH status of 32 test subjects. The algorithm performed well on straightforward cases of PH. For subjects with concomitant disease, the combination of MRI/MRS provided additional insight to the complex pathophysiology that cannot be quantified by hemodynamic measurements alone. This demonstrated that 129Xe dynamic MRS and gas exchange MRI can be used in tandem to uniquely provide non-invasive assessment of both hemodynamics and gas-exchange impairment to aid in the differentiation and detection of PH.

For 129Xe MRI/MRS to be adopted into a clinical setting it is essential to understand the underlying measurement variability. Chapter 7 presents an assessment of the repeatability of the dynamic spectroscopy sequence and quantification methods by acquiring two dynamic spectroscopy acquisitions during a single MR study. We also use these paired scans to develop quantitative criteria to assess the scan quality for inclusion in dynamic analysis. Additionally, as 129Xe MRI/MRS is more broadly implemented it is imperative to have standards for day-to-day validation and for comparing performance at different 129Xe imaging centers. Therefore, Chapter 8 present our development of a thermally polarized xenon phantom assembly and associated imaging protocol to enable rapid quality‐assurance (QA) imaging.

The work in this thesis develops a robust 129Xe dynamic spectroscopy protocol for evaluating the temporal dynamics in the RBC resonance. In particular, the height of RBC amplitude oscillations is found to be sensitive to PH and can be used to differentiate between pre- and post-capillary forms. 129Xe dynamic spectroscopy and 129Xe gas exchange MRI can differentiate between diverse cardiopulmonary diseases and together provide a complete evaluation of pulmonary hemodynamics and gas exchange impairments. This research lays the groundwork for the use of 129Xe MRI/MRS in clinical practice to diagnose and monitor PH and transforms 129Xe MRI/MRS into a more comprehensive tool for investigating the pathogenesis of unexplained dyspnea.

Purpose: To investigate the feasibility of the Log-Demons deformable image registration (DIR) method to correct eddy current and Echo Planar Imaging (EPI) distortions while preserving diffusion tensor information.

Methods: A phantom MR scan was conducted using a diffusion phantom scan (Diffusion Phantom Model 128, High Precision Devices, Inc) on a clinical 3T scanner. The scan includes a standard T1-weighted scan and a 20‐direction diffusion tensor imaging (DTI) scan, which consists of one data set with b=0s/mm2 and twenty diffusion-weighted data sets with b=1,000s/mm2. A Log-Demons DIR algorithm was applied to the DTI images for eddy current and EPI distortion correction based on the b=0s/mm2 and T1 weighted data sets and compared the eddy current and EPI distortion corrections along the phase encoding direction by affine and demons DIR algorithms. The Log-Demons framework is optimized based on both similarity and regularization. The registered images were analyzed using Cross-correlation (CC) and mutual information (MI) to assess the performances of distortion corrections by the DIR methods. Quantitative deviations from the original data after correction were also evaluated using the mean, and root mean square error (RMSE) for thirteen regions of interest in the Apparent Diffusion Coefficient (ADC) and Fractional Anisotropy (FA) maps.

The Log-Demons DIR algorithm was then applied to the MASSIVE dataset, which provides diffusion-weighted volumes divided into four sets with both positive (+) and negative(-) diffusion gradient directions and both AP and PA phase encoding directions. The registered images were analyzed using the mutual information (MI) and the absolute mean difference of two images with opposing gradient directions to assess the performances of distortion corrections by the DIR methods. Images with opposing gradient directions were compared when comparing eddy current distortions and images with opposing phase encoding directions were compared for EPI distortions.

Results: In the phantom study, the MI and CC were improved by 2.15%,0.89%, and 39.39% compared to no correction, and affine, and demons algorithm respectively when correction for eddy current distortions. MI and CC were improved by 8.89%, 9.33%, and 9.20% compared to no correction, and affine, and demons algorithm respectively when correction for EPI distortions. Analysis of the tensor metrics using percent difference and the RMS of the ADC and FA found that the Log-Demons algorithm outperforms the other algorithms in terms of preserving diffusion information.

In the MASSIVE study, the Log-demons DIR method outperformed the demons algorithm in terms of MI but underperformed compared to the affine registration for both eddy current and EPI distortions corrections. The absolute mean difference was decreased by 2.94%, 0.44%, and 1.53% compared to no correction, and affine, and demons algorithm respectively when correcting for eddy current distortions, and decreased by 0.39%, 8.03%, and 13.19% compared to no correction, and affine, and demons algorithm respectively when correcting for EPI distortions.

Conclusion: This work indicates that the Log-Demons DIR algorithm is feasible to reduce eddy current and EPI distortions while preserving quantitative diffusion information. Although demonstrated with a DTI phantom study and brain study, this method could be extended for areas in which diffusion-weighted imaging is beneficial.

Magnetic Resonance Imaging (MRI) image quality is strongly dependent on the homogeneity of the main magnetic field, B0. Inhomogeneities in this magnetic field lead to image artifacts such as: blurring, signal loss, and gross distortions of the imaged anatomy of the brain, degrading the images effectiveness to provide diagnostic information. A new radio-frequency (RF) head coil design with integrated direct-current (DC) shim coils to provide localized B0 shimming of the brain and simultaneously provide parallel excitation of reception is presented in this thesis. This design optimizes both the RF and DC shim coils proximity to the subject thereby maximizing both the signal-to-noise ratio and the shimming efficiency. This coil architecture is termed iPRES (integrated parallel receive, excitation and shimming).

An existing 32 channel receive-only head coil array was modified into an iPRES coil architecture. The coils of the array were modified using RF components to enable the simultaneous flow of both RF and DC currents on the same structure. The RF and DC currents provide concurrent signal reception and localized B0 shimming to the brain, respectively. In this thesis, the techniques, measurements and quality-metrics used to facilitate the iPRES coil array modification will be discussed.

The localized B0 shimming performance is evaluated in the frontal region of the brain which suffers from large susceptibility artifacts at the air/tissue boundary of the brain and the sinus. Axial B0 maps and echo-planar images (EPI) are acquired in vivo with optimized DC shim currents demonstrating a reduction in B0 inhomogeneities in the frontal lobe resulting in improved image EPI image quality. The coils quality factor and signal-to-noise ratio did not suffer as a result of the coil modification. The shimming performance and RF quality metrics are compared to standard whole-body spherical harmonic shimming and are discussed at length in the following chapters.

Finally, initial phantom results from the next-generation iPRES coil array will be presented. This architecture again uses an existing RF head coil array to simultaneously drive RF currents for reception and DC currents for local shimming. However, the shimming is further enhanced by providing additional RF-isolated shim coils which increases the shimming degrees of freedom. This design is useful when fast-changing, asymmetric B0 inhomogeneities are present in the imaged anatomy.

Purpose: Although brain Single Photon Emission Computed Tomography (SPECT) exam is a low cost, widely used functional and molecular brain imaging application, it also has poor spatial resolution (64 x 64 or 128 x 128, pixel sizes about 3 - 6 mm) and a noisy signal. As a result, the SPECT brain images may not be quantitatively accurate for radiotracer uptake, mainly gray matter (GM) and white matter (WM). Many studies have considered improving SPECT quantification by incorporating Magnetic Resonance Imaging (MRI) images into SPECT images. MRI has much higher spatial resolution (192x192 or 256 x 256, pixel sizes 1 to 1.5 mm), which is useful in correcting partial-volume degradation of SPECT quantification. MRI also provides broader image contrast options with many different types of MRI sequences, typically in T1 weighted (T1WI) and T2 weighted (T2WI) sequences. In most previous studies into the use of MRI or CT images to generate the anatomical priors for SPECT/ Positron Emission Tomography (PET) image reconstruction, only a single MRI sequence has been considered. Few studies have investigated the effects of different MRI sequence on the anatomical prior and the resulting SPECT/PET based on the different MRI sequences. In the present study, we evaluate the SPECT brain images at the midbrain level, with the anatomical priors based on the MRI T1WI gradient echo (GE) images and T2WI fast spin echo (FSE) images.

Materials and methods: Source brain images were downloaded from BrainWeb for SPECT image simulation. These included fuzzy gray matter and white matter models for digital radiotracer phantom creation, MRI T1WI and T2WI images for use in SPECT image-reconstruction anatomical priors. The images were selected at the midbrain level and converted to 32 bit to be used in SPECT-MAP. In SPECT-MAP, a ground truth radiotracer phantom was generated for Tc99m-ECD brain perfusion studies. Based on the phantom, SPECT projection data were simulated. These simulations modeled noise and spatial resolution. SPECT images were then reconstructed by maximum a posteriori (MAP). The prior probability distributions were generated from either gradient-echo T1 or fast-spin-echo T2 MRI images. The MAP objective function was optimized using an iterative coordinate descent (ICD) algorithm. SPECT images were also reconstructed by ordered subsets expectation maximization (OSEM). Reconstructed images were compared to the true phantom radiotracer distribution by visual inspection profiles, root mean square error (RMSE), and gray matter to white matter contrast to deviation ratio (CDR).

Results: After 17 iterations, the RMSE for method T1 MAP, T2 MAP and OSEM was 1266.4, 1752.6 and 3231.9. The CDR for method T1 MAP, T2 MAP and OSEM was 10.1, 7.8 and 2.8, which in the digital radiotracer phantom was 25.1. Relative to the T2-based prior, utilizing the T1-based prior for SPECT image reconstruction improved RMSE and CDR by 18% and 29% respectively. Relative to the best iterations for OSEM, the T1-based prior improved RMSE by 43% and CDR by a factor of 2.6. Visually, the SPECT image reconstructed with the T1-based prior was closest to the true phantom distribution, notably capturing certain structures that were not well reconstructed using the T2 image. Both MAP images were superior to OSEM visually and by RMSE and CDR.

Conclusion: The quality of SPECT images reconstructed utilizing MRI images depends substantially on the MRI sequence utilized. For this study, gradient-echo T1 MRI provided more accurate SPECT image reconstruction than fast-spin-echo T2 MRI. Both MRI sequences resulted in better RMSE and CDR than OSEM without use of MRI. The CSF signal distorted MRI boundaries relative to radiotracer boundaries, particularly for MRI T2 sequences. A T2 FLAIR-like images improved boundary alignment and SPECT reconstructed image as compared to T2 MRI images when they were used in anatomical priors for SPECT image reconstruction.

Mouse models of cancer are an invaluable tool for studying the mechanism of the disease and the effect of new therapies. Recent years have seen an explosive growth in the development of such models and consequently there is an increased need for better imaging techniques to study them. The goal of this work was to develop a technique that satisfied the requirements for preclinical cancer imaging: high spatial resolution, good soft tissue differentiation, excellent motion immunity, fast and non-invasive imaging to enable high-throughput, longitudinal studies.

T₂-weighted and diffusion-weighted magnetic resonance imaging (MRI) has been shown to be effective for tumor characterization clinically. But translation of these techniques to the mouse is challenging. The higher spatial resolution and faster physiologic motion make conventional approaches very susceptible to phase artifacts. Additionally, at higher magnetic fields required for these studies, T*₂ and T₂ are significantly shorter and T₁ is longer, making in vivo imaging even harder.

A rigorous cancer imaging protocol was developed by optimizing and integrating various components of the system, including MR hardware, animal handling, and pulse sequence design to achieve reliable, repeatable and rapid imaging. The technique presented here relies heavily on the non-Cartesian sampling strategy of PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) MRI. The novel data acquisition and reconstruction overcomes the adverse effects of physiological motion, allows for rapid setup and acquisition and provides excellent tissue contrast. The sequence was optimized to enable T₂-weighted and diffusion-weighted imaging in tumor-bearing mice with in-plane resolution of 117μm and slice thickness of 1mm. Multi-slice datasets covering the entire thorax and abdomen were acquired in ∼30 minutes.

The imaging protocol developed here was applied to a high-throughput, longitudinal study in a mouse model of liver metastases. The liver is a common site of distal metastases in colon and rectal cancer, and if detected early has an improved prognosis. Unfortunately, severe respiratory motion make it hard to image. The relative merits of the proposed PROPELLER technique were analyzed with respect to the accepted gold-standard for abdominal cancer imaging, computed tomography (CT).

The non-Cartesian MR microscopy technique proposed here is a valuable tool in the “Cancer analysis toolkit”. It allows for high-throughput, longitudinal experiments in free-breathing mice generating both structural and functional information with minimal artifacts and excellent spatial resolution. This work should find broad applications in various mouse models of cancer for studying the pathology of the disease, its progression as well as its response to treatment.