A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning

Three-Dimensional (3-D) imaging is vital in computer-assisted surgical planning including minimal invasive surgery, targeted drug delivery, and tumor resection. Selective Internal Radiation Therapy (SIRT) is a liver directed radiation therapy for the treatment of liver cancer. Accurate calculation of anatomical liver and tumor volumes are essential for the determination of the tumor to normal liver ratio and for the calculation of the dose of Y-90 microspheres that will result in high concentration of the radiation in the tumor region as compared to nearby healthy tissue. Present manual techniques for segmentation of the liver from Computed Tomography (CT) tend to be tedious and greatly dependent on the skill of the technician/doctor performing the task. This dissertation presents the development and implementation of a fully integrated algorithm for 3-D liver and tumor segmentation from tri-phase CT that yield highly accurate estimations of the respective volumes of the liver and tumor(s). The algorithm as designed requires minimal human intervention without compromising the accuracy of the segmentation results. Embedded within this algorithm is an effective method for extracting blood vessels that feed the tumor(s) in order to plan effectively the appropriate treatment. Segmentation of the liver led to an accuracy in excess of 95% in estimating liver volumes in 20 datasets in comparison to the manual gold standard volumes. In a similar comparison, tumor segmentation exhibited an accuracy of 86% in estimating tumor(s) volume(s). Qualitative results of the blood vessel segmentation algorithm demonstrated the effectiveness of the algorithm in extracting and rendering the vasculature structure of the liver. Results of the parallel computing process, using a single workstation, showed a 78% gain. Also, statistical analysis carried out to determine if the manual initialization has any impact on the accuracy showed user initialization independence in the results. The dissertation thus provides a complete 3-D solution towards liver cancer treatment planning with the opportunity to extract, visualize and quantify the needed statistics for liver cancer treatment. Since SIRT requires highly accurate calculation of the liver and tumor volumes, this new method provides an effective and computationally efficient process required of such challenging clinical requirements

Proliferative and Glycolytic Assessment of the Whole-Body Bone Marrow Compartment

Objective: Quantitative assessment of active bone marrow (BM) in vivo is yet to be well-defined. This study aims to compare total body BM volume estimations obtained from use of both18F-FLT PET/CT and 18F-FDG PET/CT in order to consolidate higher cellular proliferation rates with imaging the highly active red BM in pancreatic cancer. Methods: This phase I pilot study includes seven patients with pancreatic cancers who underwent both 18F-FLT and 18F-FDG imaging each acquired within a week’s duration. A CT-based classifier is used for segmenting bone into cortical and trabecular regions. The total BM volume is determined through statistical thresholding on PET activity found within the trabecular bone. Results: Results showed that 18F-FLT measures of red BM volume (RBV) were higher than those obtained from 18F-FDG (∆=89.21 ml). RBV obtained using 18F-FLT in males were found to have high correlation with measured weight (R2=0.61) and BMI (R2=0.70). The red BM fraction obtained from 18F-FLT was significantly different between males and females, with females showing much higher red bone matter within the trabecular bone (p<0.05). In contrast to 18F-FLT, 18F-FDG BM measurements showed that RBV was significantly different between males and females (p<0.05). Results also show that spinal activity SUV threshold for red BM segmentation is significantly different between 18F-FLT PET and 18F-FDG PET (p<0.05). Conclusion: By combining 18F-FLT-PET and 18F-FDG-PET, this study provides useful insights for in vivo BM estimation through its proliferative and glycolytic activitie

18F-FLT Positron Emission Tomography/Computed Tomography Imaging in Pancreatic Cancer: Determination of Tumor Proliferative Activity and Comparison with Glycolytic Activity as Measured by 18F-FDG Positron Emission Tomography/Computed Tomography Imaging

OBJECTIVE: This phase-I imaging study examined the imaging characteristic of 3’-deoxy-3’-((18)F)-fluorothymidine ((18)F-FLT) positron emission tomography (PET) in patients with pancreatic cancer and comparisons were made with ((18)F)-fluorodeoxyglucose ((18)F-FDG). The ultimate aim was to develop a molecular imaging tool that could better define the biologic characteristics of pancreas cancer, and to identify the patients who could potentially benefit from surgical resection who were deemed inoperable by conventional means of staging. METHODS: Six patients with newly diagnosed pancreatic cancer underwent a combined FLT and FDG computed tomography (CT) PET/CT imaging protocol. The FLT PET/CT scan was performed within 1 week of FDG PET/CT imaging. Tumor uptake of a tracer was determined and compared using various techniques; statistical thresholding (z score=2.5), and fixed standardized uptake value (SUV) thresholds of 1.4 and 2.5, and applying a threshold of 40% of maximum SUV (SUV(max)) and mean SUV (SUV(mean)). The correlation of functional tumor volumes (FTV) between (18)F-FDG and (18)F-FLT was assessed using linear regression analysis. RESULTS: It was found that there is a correlation in FTV due to metabolic and proliferation activity when using a threshold of SUV 2.5 for FDG and 1.4 for FLT (r=0.698, p=ns), but a better correlation was obtained when using SUV of 2.5 for both tracers (r=0.698, p=ns). The z score thresholding (z=2.5) method showed lower correlation between the FTVs (r=0.698, p=ns) of FDG and FLT PET. CONCLUSION: Different tumor segmentation techniques yielded varying degrees of correlation in FTV between FLT and FDG-PET images. FLT imaging may have a different meaning in determining tumor biology and prognosis

Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease

This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer’s disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that theMini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume ormean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD

A practical guideline for intracranial volume estimation in patients with Alzheimer’s disease

Background Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure. Methods Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups. Results Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study. Conclusions This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations

Effects of apodization smoothing and denoising on spectral fitting

Visual review of individual spectra in magnetic resonance spectroscopic imaging (MRSI) data benefits from the application of spectral smoothing; however, if this processing step is applied prior to spectral analysis this can impact the accuracy of the quantitation. This study aims to analyze the effect of spectral denoising and apodization smoothing on the quantitation of whole-brain MRSI data obtained at short TE. Short-TE MRSI data obtained at 3 T were analyzed with no spectral smoothing, following (i) Gaussian apodization with values of 1, 2, 4, 6, and 8 Hz, and (ii) denoising using principal component analysis (dnPCA) with 3 different values for the number of retained principal components. The mean lobar white matter estimates for four metabolites, signal-to-noise ratio (SNR), spectral linewidth, and confidence intervals were compared to data reconstructed using no smoothing. Additionally, a voxel-wise comparison for N-acetylaspartate quantitation with different smoothing schemes was performed. Significant pairwise differences were seen for all Gaussian smoothing methods as compared to no smoothing (p<0.001) in linewidth and metabolite estimates, whereas dnPCA methods showing no statistically significant differences in these measures. Confidence intervals decreased, and SNR increased with increasing levels of apodization smoothing or dnPCA denoising. Mild Gaussian apodization (≤2 Hz at 3 T) can be applied with minimal (1%) errors in quantitation; however, smoothing values greater than that can significantly affect metabolite quantification. In contrast, mild to moderate dnPCA based denoising provides quantitative results that are consistent with the analysis of unsmoothed data and this method is recommended for spectral denoising. •Analyze spectral denoising and apodization smoothing on the quantitation of MRSI•Gaussian apodization and denoising using principal component analysis (dnPCA)•Mild Gaussian apodization can be applied with minimal errors in quantitation.•dnPCA based denoising methods produce more consistent results

Dynamic lung tumor phantom coupled with chest motion

Motion artifacts have always been a non-desired effect in the field of Medical Imaging. Thus new technologies are being investigated to ameliorate the damaging effects of image blurring caused by motion. The development of these new technologies requires the use of phantoms as a means of precise, repeatable and controllable source of motion for testing initial algorithms and prototypes. The objective of this project was to design a dynamic lung tumor phantom coupled with chest motion. The phantom consists of a pair of linear actuators. The complete design, excluding the actuators was built in house out of acrylic materials with low attenuation factors, making it ideal for PET studies. The linear actuator is a stepper motor coupled to a lead screw which translates rotational motion into linear displacement at a rate of 0.0254 mm/step. The system is driven by a PIC microcontroller that allows the user to select different tumor motion parameters, and is capable of performing 3D motion. The phantom is capable of providing lung tumor and chest position with an accuracy of 1.3 m in the axis of motion, with a displacement of up to 52 mm and maximum velocity of 21.59 mm/second. The design has proven to be suitable for simulating lung tumor motion in PET studies, as well as testing motion tracking algorithms. However it can also be used in studies dealing with gated radiotherapy