MSBI Thesis & Abstracts 2014-2015

Samuel Huang
Advisor: Dr. Xiaojuan Li, PhD
Thesis Title: Automatic Segmentation and Kinematic Quantification of ACL Injured Patients Knees
Objectives: To develop an automated intra-patient atlas based bone segmentation technique that is reliable for the quantification of in-vivo MRI knee kinematics.

Materials and Methods: 30 patients who had unilateral anterior cruciate ligament (ACL) tear and under went ACL single-bundle reconstruction received loaded-MRI scans. The same procedure was done for 6 healthy control patients for reproducibility. Femur and tibia were segmented automatically, and using in-house MATLAB software, three-dimensional segmentations were used to obtain kinematic data (Internal Tibial Rotation [ITR] and Tibial Position [TP]). Automatic segmentation and kinematic data was compared to results from previously used semi-automatic segmentation methods.

Results: Automatic segmentation was successful in 98.9% of 175 cases tested, with high similarity between masks of automatic and semi-automatic segmentation (85.1%). Automatic segmentation algorithm showed excellent reproducibility results between scan and rescan segmentations with an average absolute difference of: TP: 0.46 +/- 0.41 mm, ITR: 1.60 +/- 1.58 deg. These values were superior to those obtained semi-automatically. Automatic and semi-automatic kinematic results showed high correlation for both TP and ITR, with R=0.935 and R=0.900 respectively. [add summary of longitudinal changes of TP and ITR in patients]

Conclusions: The automatic segmentation technique developed in this paper proves to be very useful in reducing segmentation time and labor as well as in reducing user-generated bias. The proposed technique demonstrates a highly consistent and robust method that is useful for longitudinal knee kinematics quantification for in-vivo MRI.

Nathaniel Jenkins
Thesis Title: NaF-18 PETMR Kinetic Analysis of Lower Back Pain

Gina Kirkish
Advisor: Dr. Roland Henry, PhD
Thesis Title: Cortical Recruitment of Motor Imagery in Timed Up and Go Task
Parkinson's disease is a highly prevalent, neurodegenerative movement disorder. Gait disturbances and postural instability, symptoms that produce the highest incidence of morbidity in individuals with PD, respond poorly to dopaminergic treatment. Further, the neural correlates underlying these disabilities are currently uncharacterized. It is essential to understand the non-dopaminergic pathways that break down in patients with PD, in order to develop a neuroprotective treatment for PD.

The TUG task is a clinical marker for PD. It integrates several motor components including gait speed, initiation of movement, and turning. In this study, an fMRI paradigm was developed in order to quantitatively evaluate neural correlates related to PD motor dysfunction, which could be directly compared to performance on the TUG task. fMRI brain activation patterns of the patient imagining themselves walking, imagining themselves turning in 360-degree circles and resting were compared in 11 patients with PD and in 11 age-matched controls. Motor performance on three physical tests - the TUG task, the ten-meter walk test and the timed 360-degree turn were also recorded. These motor metric results are correlated with fMRI activation strengths. This study concluded that a neural correlate of the TUG task exists in blood oxygen level dependent, BOLD signal change in the premotor and primary motor area when imagining-turning compared to imagining-walking.

When comparing patients with PD and controls, there were no detectable differences between the outcomes on the physical tasks, the TUG, 10-meter walk, or a 360-degree turn. Cortical activation in the SMA was localized in all subjects during motor imagery tasks. There were no significant differences between patients with PD and healthy controls relative to SMA ROI activation. However, individuals with PD had a significant increase in lateral occipital lobe activation when imagining-turning compared to imagining-walking. Assessing the relationship between postural instability and the corresponding functional brain areas has the potential to increase our understanding of the breakdown of motor control in patients with PD.

Tiffany Kwak
Advisor: Dr. Michael Ohliger, PhD
Thesis Title: Evaluation of D-Amino Acids as a Probe for Molecular Imaging of Bacterial Infections
Purpose: The goal of this study was to investigate a panel of D-amino acids and select potential probe candidates for imaging bacterial infections in vivo.

Methods: Uptake of radiolabeled D-amino acids was tested in E. coli. Selection of candidates was based on the following criteria: (1) high uptake in E. coli and (2) ease of 18 F labeled analog synthesis. Selected D-amino acid candidates were then tested for uptake and specificity in E. coli at several time points and with coadministration of non-radioactive D-amino acid blocking dose. 18 F D-Phenylalanine was synthesized to test uptake in E. coli over time. 18 F-FDG was tested for uptake and specificity in E. coli at several time points and with coadministration of non-radioactive Cytochalasin B blocking dose.

Results: D-Methionine showed the highest uptake in E. coli. D-Methionine uptake increased over time in E. coli and showed specific uptake. D-Phenylalanine uptake increased over time in E. coli and showed specific uptake. 18 F D-Phenylalanine showed higher uptake in E. coli and showed more bacteria specific uptake than 18 F-FDG.

Conclusion: D-Methionine and D-Phenylalanine were selected as potential probe candidates for imaging bacterial infections in vivo.

Tae Lee
Advisor: Dr. Thomas Lang, PhD
Thesis Title: Does the trabecular bone score reflect the structure of trabecular bone?
Introduction:  Trabecular bone score (TBS) was proposed as a method to indirectly assess the vertebral microarchitecture from Dual X-ray Absorptiometry (DXA) images, which are have a projectional geometry that does not reflect 3D structure. In this study, we evaluated the extent to which TBS is affected by the variable angulation of the vertebra in the body and the presence of aortic calcifications that overly the vertebra.

Methods:  All programming was done on Matlab. The CT data was provided by Dr Sundeep Khosla’s group at the Mayo Clinic from their study of age-related bone loss and fracture. Partial vertebral masks, calibration data, and coordinate transforms, were provided by Professor Engelke’s group. Calcified aorta was removed from each slice manually. We computed the TBS of a pure trabecular bone region in a standardized (“bone fixed”) vertebral coordinate system as a gold standard. This was compared via linear regression to the TBS of the entire vertebra in the bone fixed coordinate system and then to the entire vertebra in the native scanner coordinate system. Both comparisons were done with and without the aortic calcium removed.

Results:  There was a modest but significant correlation observed between the TBS of pure trabecular bone region and the TBS of the image set in bone-fixed coordinate with the calcified aorta removed. The correlation degraded slightly when calcified aorta was added back in, and degraded heavily when the images were rotated back to their original scanner coordinates. There was a correlation observed between rotation angle and the TBSs between the original and rotated image sets.

Conclusion:  There was a significant reduction of correlation between TBS of pure trabecular bone region and the TBS calculated from native images in the scanner coordinate system with aortic calcification. As the angle of rotation increased, greater deviation was noticed between the TBS of the original and the rotated image. We conclude that the poor correlation between the pure trabecular bone and the native TBS raises significant questions about the clinical utility of this technique.

Andrew Leynes
Advisor: Dr. Peder Larson, PhD
Thesis Title: Tissue Segmentation and Classification for PET/MR MR-based Attenuation Correction Using Zero-Echo Time (ZTE) MRI
To reduce errors in the reconstructed PET image, the photon attenuation of all tissues needs to be accounted for. Current sequence-based methods to generate an MR-derived attenuation map are unable to account for all tissue classes. Recent work has demonstrated that there is phase contrast in ultrashort echo time (UTE) or zero echo time (ZTE) images that would allow classification of all necessary tissue classes (bone, air, fat, and water) from only a single ZTE scan. The aim of this thesis is to demonstrate the feasibility of generating a pseudo-CT attenuation map based on bone, air, fat, and water classifications from a single ZTE acquisition. A 3D image of the pelvis was acquired using a ZTE pulse sequence on a 3T GE Signa PET/MRI system. Semi-automated algorithms were used to segment bone, air, and soft tissue from the ZTE magnitude image. Air was segmented using an intensity limited region growing algorithm and global thresholding. Bone was segmented by enhancing bone, and then using global thresholding. Soft tissue was defined as regions where bone and air were absent. A continuous-value fat/water map was then generated with fuzzy c-means clustering using the ZTE phase image and the soft tissue mask. Appropriate HU values were assigned to the segmented tissue maps, and combined to produce the pseudo-CT attenuation map. Qualitative comparisons with CT, and Dixon pseudo-CT images presented similar tissue classification results. Preliminary results demonstrate that bone, air, fat, and water can be classified using a single ZTE acquisition.

Elizabeth Li
Advisor: Dr. Nola Hylton, PhD
Thesis Title: ADC as an indicator of breast cancer response to Veliparib treatment
Quantitative MRI can accelerate drug development by providing non-invasive methods to determine treatment response. The primary aim of this study is to assess the change in normalized apparent diffusion coefficient values (ΔADC N ), derived from diffusion-weighted MRI (DWI), as an alternative method to standard dynamic contrast-enhanced (DCE) MRI for assessing response of primary breast tumors to neoadjuvant chemotherapy. Secondary aims are to: assess the influence of image quality scoring on the predictive performance of ΔADC N ; test correlations between ΔADCN and change in functional tumor volume (ΔFTV) at early (ΔFTV 2 ) and late (ΔFTV4 ) time points; and assess ΔADC N of responders versus non-responders.

Methods: 134 patients with primary breast cancers ≥2.5 cm in diameter and high MammaPrint scores were included. 62 and 72 patients received standard and experimental drug regimens respectively. ΔADCN was determined from DW images acquired at baseline and three weeks into chemotherapy. FTV (70% DCE-MRI enhancement at 2.5 minutes post-contrast) was used as an indication of tumor response throughout treatment. Pathologic complete response (pCR) was determined by histopathology following surgery. Whole tumor regions of interest (ROIs) and quality scoring was performed on 126 cases, of which 102 had passing quality scores.

Results: The area under the receiver operating characteristic (ROC) curve (AUC) for ΔADCN was 0.653 (95% confidence interval (CI) [0.538, 0.768], p=0.00605). The estimated AUC for ΔFTV2 was not significantly higher than ΔADCN (mean difference: -0.011±0.086, p=0.896). Using a ΔFTV4 cutoff of -97.8% as a surrogate endpoint, the AUC estimates were not significantly greater than 0.5. Image quality did not impact the predictive ability or distribution of ΔADCN , which increased by 0.836% (95% CI [-0.48, 0.026], p=0.34) with quality scoring. ΔADC N was not very correlated with ΔFTV2 or ΔFTV4. ΔADC N increased by 9.74% (95% CI [2.24, 17.51], p=0.012) with response in the full cohort.

Summary: These findings suggest that--may be similar to ΔFTV2 in predictive performance. While changes in ADC and FTV both reflect changes in tissue properties, they are indicative of independent biological processes. DWI is a promising non-contrast technique that can provide additional information to better predict treatment response.

Elizabeth Phillips
Advisor: Dr. Duan Xu, PhD
Thesis Title: ROI Analysis of Brain Spectra from Patients with Traumatic Brain Injury

Hecong Qin
Advisor: Dr. John Kurhanewicz, PhD
Thesis Title: In Vivo Hyperpolarized Carbon-13 Diffusion Weighted MRI Measures Lactate Metabolism and Transport in Prostate Cancer
Prostate cancer is a heterogeneous group of tumors ranging from clinically insignificant to lethally malignant. The clinical management of prostate cancer is challenging due to the lack of accurate assessment of cancer aggressiveness. Hyperpolarized magnetic resonance imaging (MRI) has enabled real-time measurement of metabolism, and has shown great promise for cancer diagnosis, staging and assessing treatment response in both pre- clinical and clinical studies. Aggressive prostate cancer overproduces lactate and overexpresses MCT4, the transporter primarily responsible for lactate efflux, resulting in acidification of the extracellular space and conferring a poor prognosis. In this pilot study, hyperpolarized diffusion weighted MRI was used to elucidate the intra- and extracellular distribution of metabolites, which can infer lactate efflux, and tumor microstructural environment. Transgenic adenocarcinoma mouse prostate (TRAMP) models of different stages were injected with hyperpolarized pyruvate; then pyruvate and lactate were excited with a single- band spectral-spatial RF pulse, followed by a single-shot, double spin-echo flyback echo planar imaging (EPI) readout. Four b-values were acquired per metabolite, ranging from 25-1000 s * mm -2 . Data were corrected for RF utilization and fit voxel-wise to a monoexponential decay to generate apparent diffusion coefficient (ADC) maps for each metabolite. We found that the in vivo lactate ADC is close to the ex vivo extracellular ADC, rather than the intracellular ADC. We also found lactate ADC in late-stage tumors (0.65 ± 0.11 mm * s -1 , n=4) is higher than early-stage (0.46 mm 2 * s-1 , n=1), indicating there is increased lactate efflux in aggressive cancer. In conclusion, we demonstrated that hyperpolarized diffusion weighted MRI can provide insight into metabolite compartmentalization and lactate efflux in the prostate tumor, and potentially assess cancer aggressiveness and therapeutic changes in a rapid, non-invasive manner.

Omar Rutledge
Advisor: Dr. Srikantan Nagarajan, PhD
Thesis Title: Characterization of combat-induced post-traumatic stress disorder in OIF/OEF veterans using MEG-based imaging
Background: Post-traumatic stress disorder (PTSD) is a mental health disorder characterized by symptoms such as insomnia, irritability, issues with memory, difficulty concentrating, and poor decision-making abilities. With symptoms that closely resemble those of other anxiety disorders, it is very difficult to accurately diagnose. More research is needed to identify structural and functional imaging biomarkers to aid in diagnosis.

Methods: Ten right-handed male subjects (5 combat-exposed veterans, 5 healthy civilian controls) underwent magnetoencephalographic recording for this study. MEG data were acquired with a 275-channel whole-head CTF Omega 2000 system. Resting-state and tasked-based (Stroop Color-Naming Task) data were acquired. Voxel-based time-frequency analysis was subsequently performed using NUTMEG and SPM8.

Results: Significant differences were found between the two groups at rest (in delta, theta, gamma, and high-gamma neural oscillatory frequency bands) and during the Stroop Color-Naming task (in alpha, beta, and gamma, and high-gamma frequency bands).

Conclusions: Despite the small sample size, we were able to replicate some aspects of previous MEG research in veterans with PTSD. Not only does this result substantiate the use of MEG for population studies, but it also shows that PTSD is a mental disorder that is physical in nature and can be characterized through passively observing electromagnetic neuronal activity.

Roksana Sadeghi
Advisor: Dr. Norbet Schuff , PhD
Thesis Title: Statistical Shape Analysis in MRI
Medical imaging including magnetic resonance imaging (MRI) has become a major source of information for making clinical decisions, specifically based on static and dynamic shape variations of organs and moving structures respectively. Most conventional algorithms for shape extraction from images rely on deterministic modeling that offers no quantification for confidence of the extracted shapes. In this thesis a probabilistic approach for shape extraction is tested that provides a degree of confidence in extracted shapes. The degree of confidence create the important information in clinical decisions. This novel method, termed probabilistic Bayesian shape analysis introduced by Le.T and Schuff.N in [1], utilizes Curvelet transformation and Hidden Markov model simultaneously to detect probabilistic distributions of shape contours. In addition, this thesis, aims to effectively summarize probabilistic shape features in terms of information theoretic measures, such as the entropy (E) and statistical complexity (SC). The approach was initially demonstrated on well-defined hand gestures. To show the clinical potential of probabilistic shape feature extraction, the novel method was tested on tongue shape variations during pronunciation of vowels. Probabilistic shape analysis of tongue movement during vowel pronunciations mapped on MRI was performed on 5 subjects [1 woman, 4 men, age range 25-58], who speak English. MRI consisted of a Fast Low Angle Shot sequence [2]. All subjects were asked to sequentially pronounce the vowels [u:], [i:], [a:], [ae:], [e:], [o:], each for approximately 7 seconds, while having the fast MRI scan of the tongue. A probabilistic contour of tongue shape was extracted from each MRI frame (total = 210) and its features were summarized in terms of E and SC. Variations in E and SC as a function of vowel were then tested using univariate linear mixed effects regression. In addition, a multivariate linear mixed effects regression based on Monte Carlo sampling was used to test variations in E and SC simultaneously. First, the analysis showed that the novel method significantly captures variations in tongue shapes compared to repeated MRI variations. Second, the analysis demonstrated that entropy of the tongue extracted consistent shapes for the vowels [u:], [ae:], and [o:] across subjects, and for SC consistent features were found for [u:], [ae:], [a:] and [o:]. Moreover, using E and SC together capture consistent features for the vowels [a:], [e:], [i:] and [u:] across subjects. In conclusion, this thesis illustrated that the probabilistic shape model captures tongue shapes associated with vowel pronunciation. The new method is of potential interest to clinical studies of tongue disorders and function, including speech therapy, assessment of tongue surgery and assessment of functionality of the tongue. Beyond the tongue, the novel probabilistic approach has potentially wide clinical applications in almost any medical field that uses imaging. Examples of applications for the importance of contour extraction and shape detection include the delineation of almost any organ, such as the shape of kidneys and liver, the changing shapes of tendons in joints during movement, the tracking of change in structure of the beating heart and the shape of a tumor, specifically in the brain.

Solomon Tang
Advisor: Dr. John Kurhanewicz, PhD 
Thesis Title: Metabolic characterization of slowly cycling melanoma cells
The low efficacy of existing methods of treatment for late-stage melanoma has been attributed to the development of drug resistant cells. Following conventional treatments, enrichment of slowly cycling melanoma cells has been reported. Recently, a unique biological marker histone 3 K4 demethylase JARID1B was used to characterize slow-cycling cells within a rapidly proliferating population of melanoma cells. High JARID1B expression is almost exclusive to slow-cycling melanoma cells. This phenotype of cells has been shown to be temporarily distinct, dynamic, and necessary for continuous tumor growth. This study aims to seek metabolic changes between JARID1Bhigh subpopulations and bulk melanoma tumor cells. Targeting this subpopulation of drug resistant cells may improve the efficacy of existing treatments. This project aims to identify and quantify metabolic differences by analyzing H NMR spectral data of cell extracts and culture media of slow-cycling JARID1Bhigh melanoma cells in comparison to highly proliferating bulk melanoma cells. We have demonstrated great improvement in acquiring consistent data for H NMR analysis over previous attempts. Our results show enhanced glucose and glutamine consumption, increased lactate/alanine ratios, and elevated concentrations of myo-inositol and choline derivatives in slow-cycling J/EGFPhigh subpopulations compared to highly proliferative tumor cells. These preliminary findings suggest heightened metabolic versatility in slow-cycling melanoma cells in accordance with recent revisions of cancer metabolism and may help understand the dynamic stem-ness of these seemingly quiescent cells.

Jay Yu
Advisor: Dr. Steven Hetts, MD 
Thesis Title: Development of filter device to limit systemic toxicity from doxorubicin chemotherapy: DNA ChemoFilter
Objective: ChemoFilter is a new class of image-guided temporarily deployable, endovascular catheter based medical device that selectively filters chemotherapeutic agents from the bloodstream to limit systemic toxicities. We report a novel method to filter doxorubicin in blood using genomic DNA as resin.

Materials and Methods: DNA binding experiments were performed in vitro with doxorubicin in PBS solution and porcine serum. Genomic DNA was used for binding experiments, with the DNA either free in solutions or with the DNA encapsulated in packets made with nylon or polyester mesh. Optimum concentration of genomic DNA to filter 50 mg doxorubicin in solution was determined. We then compared the kinetics of filtering doxorubicin by genomic DNA as compared to our previously published ion exchange resin based ChemoFilter.

Results: Doxorubicin concentration was reduced by over 90% within 1 minute with free DNA in PBS and porcine serum. With packaged DNA, doxorubicin concentration was reduced by 50% within 5 minutes in PBS, and 90% within 1 minute in porcine Serum. Doxorubicin filtering kinetics between DNA and the ion exchange resin Dowex were compared in each binding experiments and the DNA displayed more rapid kinetics in all situations.

Conclusion: We describe a new version of ChemoFilter that uses DNA as the binding agent for DNA binding drugs. DNA ChemoFilter allows selective filtration of DNA binding chemotherapeutic agents and demonstrates more rapid kinetics than previously described ion exchange version of ChemoFilter.

Joseph Zhang
Advisor: Dr. Pratik Mukherjee, PhD 
Thesis Title: Extending Glioma invasion of brain regions using tractography
Glioblastoma (GBM) is a progressive and malignant form of glioma with very poor prognosis. Treatment options for GBM include surgery, chemotherapy, and radiotherapy, with radiotherapy being the focus of this study. GBM is especially hard to treat with radiotherapy due to the fact that the disease often extends microscopically beyond the visible tumor on MRI, leading to high recurrence rates.

The widely accepted method of radiotherapy uses a 2cm isotropic expansion from the MRI indicated tumor region in order to capture the microscopic glial cells that protrude beyond the apparent tumor margin. This method, however, is nonspecific as the radiation volume often radiates grey matter regions that not only have a lower probability of tumor growth, but is also responsible for important cognitive functions associated with motor and sensory neuronal cells.

Studies have shown that GBM grows preferentially along the white matter tracts of the brain, which plays an integral role in our method of predicting tumor growth. Using patient data from 3 individuals with GBM, we utilized a diffusion tensor imaging (DTI) technique, tractography, to predict tumor growth along white matter tracts. DTI is very quantitative in capturing the anisotropic nature of the diffusion of water molecules in white matter tracts, which will allow us to model the tumor growth using tractography.

Ours results were compared against the clinical planned radiation treatment for each of the 3 patients, which showed significantly greater coverage of white matter tracts, areas of high probable tumor recurrence.

Yukai Zhou
Advisor: Dr. Dieter Meyerhoff, PhD 
Thesis Title: Microstructural integrity of brain white matter in different substance using populations: Diffusion Tensor Imaging analysis of 4 Tesla MRI data
​Many individuals with alcohol use disorders (AUD) are chronic cigarette smokers, and the specific contributions of both chronic drinking and smoking to brain injury need to be better understood. A previous analysis of 1.5T diffusion tensor imaging data by Tract-Based Spatial Statistics (TBSS) found significant microstructural injury of brain white matter in 11 smoking alcoholics at 1 week of abstinence. The objectives of this study are to reproduce the 1.5T study with corresponding 4T MRI data from a larger sample of alcoholics (ALC) in recovery and to test the effects of cigarette smoking on microstructural integrity of brain white matter. Corpus callosum, cingulum bundle, fornix, and medial forebrain bundle were chosen as a priori regions of interest (ROIs). After TBSS analyses, family-wise error (FWE)-corrected t-statistics showed significant fractional anisotropy (FA) deficits in smoking ALC (sALC) at 1 week of abstinence compared with nonsmoking healthy controls within corpus callosum (body, genu, and splenium), fornix, and medial forebrain bundle, largely replicating the findings at 1.5T. Exploratory uncorrected t-statistics found significant FA deficits in sALC at 1 month of abstinence compared with smoking controls within cingulum bundle, body of corpus callosum, fornix, and medial forebrain bundle; no regional FA difference remained significant after FWE correction. Exploratory uncorrected t-statistics found significant FA decreases and increases in smoking versus non-smoking alcoholics, as well as regional FA decreases in smoking versus non-smoking healthy controls; none of these smoking effects, however, remained significant after FWE correction. The analyses suggest that white matter microstructural integrity in alcoholics is compromised at 1 week of abstinence and may have recovered after 1 month of abstinence, and chronic cigarette smoking may reduce microstructural integrity in both alcoholics and healthy controls.