Impacting Parameter Analysis for Intensity Modulated Radiation Treatment

poster abstractIntroduction: Intensity-modulated radiation therapy (IMRT) accurately delivers radiation doses with high degree of conformity by modulating the intensity of the radiation beam in multiple small segments. Usually small fields have large variation in dose. For some TPS, there are no restrictions on plan parameters. Guideline for plan optimization is needed that allows the IMRT QA to pass satisfactorily. IMRT plan parameters are analyzed to correlate the success and failure of an IMRT QA plan. Materials and Methods: Based on IMRT QA results, 15 IMRT treatment plans, divided into 3 groups, are studies. Plans in group 1 passed IMRT QA with high gamma index passing rates and plan in group 2 passed with marginal passing rates. Plans in group 3 failed the IMRT QA. Statistical analysis has been performed on plan parameters, including beam number, segment number for each beam, MU in total or for each segment, the width variations of the leaf/jaw positions for each segment, the segment area sizes, and dose delivery for different segments of each beam, to discover the relationships between IMRT quality and these parameters. Results: The statistical results showed there is no correlation between plan quality and MU or beam/segment numbers. However, there are noticeable correlations between the IMRT quality and the segment sizes and widths. For each plan group, the IMRT quality decreased with the decreasing field sizes and segment widths. The histograms of these factors showed that failed IMRT plans have peak distributions with small field sizes (< 30cm2) and narrow widths (<20mm). Conclusion: Initial results showed that the passing rates of IMRT treatment plans have strong correlation with the segment field sizes and the opening widths of the leaf/jaw positions. Large number of segments with small fields produces unacceptable IMRT QA and should be avoided during IMRT planning

Systematic Study of Data Science and Analytics Programs

Rapid advances in information technologies have led to the generation of massive data sets, especially in life science and biomedical informatics. These data sets are valuable assets and in great needs to be analyzed. However, there is a shortage of workforce for big data analysis. Education innovations are required to empower students with the skills and technologies for large dataset analysis. Over the last few years, there is a high demand for new programs in data science and analytics (DSA). We has performed a systematic study of the existing DSA programs in the US by checking the detailed information about the degree programs, the program competencies, the curriculum designs, the expected learning outcomes, program sizes, professional careers, and other related information. There are more than 70 DSA programs offered in the US. This study provides guidance on DSA related program development and curriculum design. It also provides the potential trainees in DSA with the current market needs and the required knowledge for their future career

Healthcare Data Analytics for Parkinson’s Disease Patients: A Study of Hospital Cost and Utilization in the United States

Parkinson's Disease (PD), a prevalent problem, especially for the aged populations, is a progressive but non-fatal nervous system disorder. PD patients have special motor as well as non-motor symptoms over time. There are several limitations in the study of PD such as unavailability of data, proper diagnosis and treatment methods. These limitations significantly reduce the quality of PD patient life quality, either directly or indirectly. PD also imposes great financial burdens to PD patients and their family. This project aims to analyze the most common reasons for PD patient hospitalization, review complications that occur during inpatient stays, and measure the costs associated with PD patient characteristics. Using the HCUP NIS data, comprehensive data analysis has been performed. The results are customized visualized using Tableau and other software systems. The preliminary findings sheds light into how to improve the life quality of PD patients

Dual-Gated Volumetric Modulated Arc Therapy

BACKGROUND: Gated Volumetric Modulated Arc Therapy (VMAT) is an emerging radiation therapy modality for treatment of tumors affected by respiratory motion. However, gating significantly prolongs the treatment time, as delivery is only activated during a single respiratory phase. To enhance the efficiency of gated VMAT delivery, a novel dual-gated VMAT (DG-VMAT) technique, in which delivery is executed at both exhale and inhale phases in a given arc rotation, is developed and experimentally evaluated. METHODS: Arc delivery at two phases is realized by sequentially interleaving control points consisting of MUs, MLC sequences, and angles of VMAT plans generated at the exhale and inhale phases. Dual-gated delivery is initiated when a respiration gating signal enters the exhale window; when the exhale delivery concludes, the beam turns off and the gantry rolls back to the starting position for the inhale window. The process is then repeated until both inhale and exhale arcs are fully delivered. DG-VMAT plan delivery accuracy was assessed using a pinpoint chamber and diode array phantom undergoing programmed motion. RESULTS: DG-VMAT delivery was experimentally implemented through custom XML scripting in Varian's TrueBeam™ STx Developer Mode. Relative to single gated delivery at exhale, the treatment time was improved by 95.5% for a sinusoidal breathing pattern. The pinpoint chamber dose measurement agreed with the calculated dose within 0.7%. For the DG-VMAT delivery, 97.5% of the diode array measurements passed the 3%/3 mm gamma criterion. CONCLUSIONS: The feasibility of DG-VMAT delivery scheme has been experimentally demonstrated for the first time. By leveraging the stability and natural pauses that occur at end-inspiration and end-exhalation, DG-VMAT provides a practical method for enhancing gated delivery efficiency by up to a factor of two

Model Checking Temporal Logic Formulas Using Sticker Automata

As an important complex problem, the temporal logic model checking problem is still far from being fully resolved under the circumstance of DNA computing, especially Computation Tree Logic (CTL), Interval Temporal Logic (ITL), and Projection Temporal Logic (PTL), because there is still a lack of approaches for DNA model checking. To address this challenge, a model checking method is proposed for checking the basic formulas in the above three temporal logic types with DNA molecules. First, one-type single-stranded DNA molecules are employed to encode the Finite State Automaton (FSA) model of the given basic formula so that a sticker automaton is obtained. On the other hand, other single-stranded DNA molecules are employed to encode the given system model so that the input strings of the sticker automaton are obtained. Next, a series of biochemical reactions are conducted between the above two types of single-stranded DNA molecules. It can then be decided whether the system satisfies the formula or not. As a result, we have developed a DNA-based approach for checking all the basic formulas of CTL, ITL, and PTL. The simulated results demonstrate the effectiveness of the new method

CGPE: A user-friendly gene and pathway explore webserver for public cancer transcriptional data

Digitized for IUPUI ScholarWorks inclusion in 2021.High throughput technology has been widely used by researchers to understand diseases at the molecular level. Database and servers for downloading and analyzing these publicly data is available as well. But there is still lacking tools for facilitating researchers to study the function of genes in pathways views by integrated public omics data

A line-profile based double partial fusion method for acquiring planning CT of oversized patients in radiation treatment

True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation &amp; Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning

A Coherent Healthcare System with RDBMS, NoSQL and GIS Databases

With new database system development and new data types emerging, many applications are no longer using a monolithic, simple client/server structure, but using more than one types of database systems to store heterogeneous data. In this project, we exploit the benefits of combing Relational Database Management System (RDBMS) and NoSQL systems in the development of better Electronic Health Records (EHRs) and Clinical Decision Support Systems (CDSS). Specifically, MySQL, MongoDB, and GIS databases are integrated to improve EHR systems and to provide better clinical decision supports. The ACID (atomicity, consistency, isolation, durability) properties of the RDBMS ensure data integrity, database security, efficient SQL queries, easy data access, and effective transaction processing. MongoDB provides the system with clear internal data structure, easy scaling-out, fine-Tuning, and convenient mapping of application objects to the database objects. The GIS database allows vivid visualization of the geographic locations of patients, physician offices, and medical facilities. The integrations of these database systems in healthcare help application systems to comply with the EHR HIPAA requirements without compromising on scalability and performance

A Unified Health Information System Framework for Connecting Data, People, Devices, and Systems

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare

An effective method to reduce the interplay effects between respiratory motion and a uniform scanning proton beam irradiation for liver tumors: A case study

PURPOSE: For scanning particle beam therapy, interference between scanning patterns and interfield organ motion may result in suboptimal dose within target volume. In this study, we developed a simple offline correction technique for uniform scanning proton beam (USPB) delivery to compensate for the interplay between scanning patterns and respiratory motion and demonstrate the effectiveness of our technique in treating liver cancer. METHODS: The computed tomography (CT) and respiration data of two patients who had received stereotactic body radiotherapy for hepatocellular carcinoma were used. In the simulation, the relative beam weight delivered to each respiratory phase is calculated for each beam layer after treatment of each fraction. Respiratory phases with beam weights higher than 50% of the largest weight are considered "skipped phases" for the next fraction. For the following fraction, the beam trigger is regulated to prevent beam layers from starting irradiation in skipped phases by extending the interval between each layer. To calculate dose-volume histogram (DVH), the dose of the target volume at end-exhale (50% phase) was calculated as the sum of each energy layer, with consideration of displacement due to respiratory motion and relative beam weight delivered per respiratory phase. RESULTS: For a single fraction, D1% , D99% , and V100% were 114%, 88%, and 32%, respectively, when 8 Gy/min of dose rate was simulated. Although these parameters were improved with multiple fractions, dosimetric inhomogeneity without motion management remained even at 30 fractions, with V100% 86.9% at 30 fractions. In contrast, the V100% values with adaptation were 96% and 98% at 20 and 30 fractions, respectively. We developed an offline correction technique for USPB therapy to compensate for the interplay effects between respiratory organ motion and USPB beam delivery. CONCLUSIONS: For liver tumor, this adaptive therapy technique showed significant improvement in dose uniformity even with fewer treatment fractions than normal USPB therapy