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.^

A dosimetry and radiobiological model for intravascular brachytherapy treatment planning with radioisotope emitting stents

The aim of this study was to develop a practical, versatile and fast dosimetry and radiobiological model for calculation of the 3D dose distribution and radiobiological effectiveness of radioactive stents. The algorithm was written in Matlab 6.5 programming language and is based on the dose point kernel convolution. The dosimetry and radiobiological model was applied for evaluation of the 3D dose distribution of 32P, 90Y, 188Re and 177Lu stents. Of the four, 32P delivers the highest dose, while 90Y, 188Re and 177Lu require high levels of activity to deliver a significant therapeutic dose in the range of 15-30 Gy. Results of the radiobiological model demonstrated that the same physical dose delivered by different radioisotopes produces significantly different radiobiological effects. This type of theoretical dose calculation can be useful in the development of new stent designs, the planning of animal studies and clinical trials, and clinical decisions involving individualized treatment plans.

Microspheres for liver radiomicrospheres therapy and planning

Liver cancer accounts for nearly 10% of all cancers in the US. Intrahepatic Arterial Radiomicrosphere Therapy (RMT), also known as Selective Internal Radiation Treatment (SIRT), is one of the evolving treatment modalities. Successful patient clinical outcomes require suitable treatment planning followed by delivery of the microspheres for therapy. The production and in vitro evaluation of various polymers (PGCD, CHS and CHSg) microspheres for a RMT and RMT planning are described. Microparticles with a 30±10 µm size distribution were prepared by emulsion method. The in vitro half-life of the particles was determined in PBS buffer and porcine plasma and their potential application (treatment or treatment planning) established. Further, the fast degrading microspheres (≤ 48 hours in vitro half-life) were labeled with 68Ga and/or 99mTc as they are suitable for the imaging component of treatment planning, which is the primary emphasis of this dissertation. Labeling kinetics demonstrated that 68Ga-PGCD, 68Ga-CHSg and 68Ga-NOTA-CHSg can be labeled with more than 95% yield in 15 minutes; 99mTc-PGCD and 99mTc-CHSg can also be labeled with high yield within 15-30 minutes. In vitro stability after four hours was more than 90% in saline and PBS buffer for all of them. Experiments in reconstituted hemoglobin lysate were also performed. Two successful imaging (RMT planning) agents were found: 99mTc-CHSg and 68Ga-NOTA-CHSg. For the 99mTc-PGCD a successful perfusion image was obtained after 10 minutes, however the in vivo degradation was very fast (half-life), releasing the 99mTc from the lungs. Slow degrading CHS microparticles (> 21 days half-life) were modified with p-SCN-b-DOTA and labeled with 90 Y for production of 90Y-DOTA-CHS. Radiochemical purity was evaluated in vitro and in vivo showing more than 90% stability after 72 and 24 hours respectively. All agents were compared to their respective gold standards (99mTc-MAA for 68Ga-NOTA-CHSg and 99m Tc-CHSg; 90Y-SirTEX for 90Y-DOTA-CHS) showing superior in vivo stability. RMT and RMT planning agents (Therapy, PET and SPECT imaging) were designed and successfully evaluated in vitro and in vivo.

Microspheres for Liver Radiomicrospheres Therapy and Planning

Liver cancer accounts for nearly 10% of all cancers in the US. Intrahepatic Arterial Radiomicrosphere Therapy (RMT), also known as Selective Internal Radiation Treatment (SIRT), is one of the evolving treatment modalities. Successful patient clinical outcomes require suitable treatment planning followed by delivery of the microspheres for therapy. The production and in vitro evaluation of various polymers (PGCD, CHS and CHSg) microspheres for a RMT and RMT planning are described. Microparticles with a 30±10 µm size distribution were prepared by emulsion method. The in vitro half-life of the particles was determined in PBS buffer and porcine plasma and their potential application (treatment or treatment planning) established. Further, the fast degrading microspheres (≤ 48 hours in vitro half-life) were labeled with 68Ga and/or 99mTc as they are suitable for the imaging component of treatment planning, which is the primary emphasis of this dissertation. Labeling kinetics demonstrated that 68Ga-PGCD, 68Ga-CHSg and 68Ga-NOTA-CHSg can be labeled with more than 95% yield in 15 minutes; 99mTc-PGCD and 99mTc-CHSg can also be labeled with high yield within 15-30 minutes. In vitro stability after four hours was more than 90% in saline and PBS buffer for all of them. Experiments in reconstituted hemoglobin lysate were also performed. Two successful imaging (RMT planning) agents were found: 99mTc-CHSg and 68Ga-NOTA-CHSg. For the 99mTc-PGCD a successful perfusion image was obtained after 10 minutes, however the in vivo degradation was very fast (half-life), releasing the 99mTc from the lungs. Slow degrading CHS microparticles (> 21 days half-life) were modified with p-SCN-b-DOTA and labeled with 90Y for production of 90Y-DOTA-CHS. Radiochemical purity was evaluated in vitro and in vivo showing more than 90% stability after 72 and 24 hours respectively. All agents were compared to their respective gold standards (99mTc-MAA for 68Ga-NOTA-CHSg and 99mTc-CHSg; 90Y-SirTEX for 90Y-DOTA-CHS) showing superior in vivo stability. RMT and RMT planning agents (Therapy, PET and SPECT imaging) were designed and successfully evaluated in vitro and in vivo.

Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

Subject -centered and problem -based approaches to teaching clinical competence in physical therapy

Physical therapy students must apply the relevant information learned in their academic and clinical experience to problem solve in treating patients. I compared the clinical cognitive competence in patient care of second-year masters students enrolled in two different curricular programs: modified problem-based (M P-B; n = 27) and subject-centered (S-C; n = 41). Main features of S-C learning include lecture and demonstration as the major teaching strategies and no exposure to patients or problem solving learning until the sciences (knowledge) have been taught. Comparatively, main features of M P-B learning include case study in small student groups as the main teaching strategy, early and frequent exposure to patients, and knowledge and problem solving skills learned together for each specific case. Basic and clinical orthopedic knowledge was measured with a written test with open-ended items. Problem solving skills were measured with a written case study patient problem test yielding three subscores: assessment, problem identification, and treatment planning. ^ Results indicated that among the demographic and educational characteristics analyzed, there was a significant difference between groups on ethnicity, bachelor degree type, admission GPA, and current GPA, but there was no significant difference on gender, age, possession of a physical therapy assistant license, and GRE score. In addition, the M P-B group achieved a significantly higher adjusted mean score on the orthopedic knowledge test after controlling for GRE scores. The S-C group achieved a significantly higher adjusted mean total score and treatment management subscore on the case study test after controlling for orthopedic knowledge test scores. These findings did not support their respective research hypotheses. There was no significant difference between groups on the assessment and problem identification subscores of the case study test. The integrated M P-B approach promoted superior retention of basic and clinical science knowledge. The results on problem solving skills were mixed. The S-C approach facilitated superior treatment planning skills, but equivalent patient assessment and problem identification skills by emphasizing all equally and exposing the students to more patients with a wider variety of orthopedic physical therapy needs than in the M P-B approach. ^

Positron Emission Tomography (PET) Tumor Segmentation and Quantification: Development of New Algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.