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Optimization of Computed Tomography Calibration Curve for Proton Therapy Treatment Planning
Title
Optimization of Computed Tomography Calibration Curve for Proton Therapy Treatment Planning.
Creator
Ghasemi Ghonchehnazi, Maryam, Shang, Charles, Leventouri, Theodora, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description
The accuracy of proton dose computation in the treatment planning system relies on the conversion from the Hounsfield units (HU) of each voxel in the patient CT scan to the proton stopping power ratio (SPR). The aim of this study is to investigate the potential improvement in determining proton SPR using single energy computed tomography (SECT) to reduce the uncertainty in predicting the proton range in patients. Factors which may cause CT number variations in the calibration curve have been...
Show moreThe accuracy of proton dose computation in the treatment planning system relies on the conversion from the Hounsfield units (HU) of each voxel in the patient CT scan to the proton stopping power ratio (SPR). The aim of this study is to investigate the potential improvement in determining proton SPR using single energy computed tomography (SECT) to reduce the uncertainty in predicting the proton range in patients. Factors which may cause CT number variations in the calibration curve have been examined. The HU-SPR calibration curve was determined based on HU of human body tissues using the stoichiometric method. The uncertainties in SPR were divided into two major categories: The inherent uncertainty, and the CT number uncertainty. The root mean square errors of the inherent uncertainties were estimated 0.02%, 0.61% and 0.26% for lung tissues, soft tissues (excluding Thyroid), and bone tissues, respectively. The total uncertainties due to the inherent uncertainty and CT imaging errors were estimated 1.50%. The average calibration curve of two sized phantoms (head and body) were used in the treatment planning system to mitigate beam hardening effect through the attenuating media. A higher accuracy of the SPR prediction using the stoichiometric method is suggested through comparison with the predicted SPRs that derived from the direct calibration approach.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013374
Subject Headings
Proton Therapy, Tomography, Calibration, Tomography, X-Ray Computed
Format
Document (PDF)
CONSISTENCY OF CT NUMBER AND ELECTRON DENSITY IN TREATMENT PLANNING SYSTEM VERSUS CT SCANNER, AND DOSIMETRIC CONSEQUENCES
Title
CONSISTENCY OF CT NUMBER AND ELECTRON DENSITY IN TREATMENT PLANNING SYSTEM VERSUS CT SCANNER, AND DOSIMETRIC CONSEQUENCES.
Creator
Hana, Evan Makdasy, Pella, Silvia, Leventouri, Theodora, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
The Computer Tomography (CT) scanned images are very important for the Treatment Planning System (TPS) to provide the electron density of the different types of tissues that the radiation penetrates in the path to the tumor to be treated. This electron density is converted to an attenuation coefficient, which varies with tissue for each structure and even varies by the tissue volume. The purpose of this research is to evaluate the CT numbers, and convert them into relative electron densities....
Show moreThe Computer Tomography (CT) scanned images are very important for the Treatment Planning System (TPS) to provide the electron density of the different types of tissues that the radiation penetrates in the path to the tumor to be treated. This electron density is converted to an attenuation coefficient, which varies with tissue for each structure and even varies by the tissue volume. The purpose of this research is to evaluate the CT numbers, and convert them into relative electron densities. Twenty-five patients’ data and CT numbers were evaluated in the CT scanner and in Eclipse and were converted into relative electron density and compared with each other. The differences between the relative electron density in the Eclipse was found to be from 0 up to 6% between tissue equivalent materials, the final result for all equivalent tissue materials was about 2%. For the patients’ data, the percentage difference of CT number versus electron density was found to be high for high relative electron density organs, namely the final average result for the spine was 8%, less for pelvis, and less for rib while for the other organs it was even less. The very lowest was 0.3% compared with 1% which is acceptable for clinical standards.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013315
Subject Headings
Tomography, X-Ray Computed, Electron density, Radiation dosimetry--Evaluation, Tomography Scanners, X-Ray Computed
Format
Document (PDF)
Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning
Title
Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
Creator
Mohammadi Khoroushadi, Mohammad Sadegh, Leventouri, Theodora, Zhuang, Hanqi, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient...
Show moreReducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013115
Subject Headings
Tomography--Image quality, Wavelets (Mathematics), Deep learning, Tomography, X-Ray Computed
Format
Document (PDF)
DETECTION AND CATEGORIZATION OF LUNG CANCER USING CONVOLUTIONAL NEURAL NETWORK
Title
DETECTION AND CATEGORIZATION OF LUNG CANCER USING CONVOLUTIONAL NEURAL NETWORK.
Creator
Mostafanazhad, Shahabeddin Aslmarand, Muhammad, Wazir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description
Medical professionals use CT images to get information about the size, shape, and location of any lung nodules. This information will help radiologist and oncologist to identify the type of cancer and create a treatment plan. However, most of the time, the diagnosis regarding the types of lung cancer is error-prone and time-consuming. One way to address these problems is by using convolutional neural networks. In this Thesis, we developed a convolutional neural network that can detect...
Show moreMedical professionals use CT images to get information about the size, shape, and location of any lung nodules. This information will help radiologist and oncologist to identify the type of cancer and create a treatment plan. However, most of the time, the diagnosis regarding the types of lung cancer is error-prone and time-consuming. One way to address these problems is by using convolutional neural networks. In this Thesis, we developed a convolutional neural network that can detect abnormalities in lung CT scans and further categorize the abnormalities to benign, malignant adenocarcinoma and malignant squamous cell carcinoma. Our network is based on DenseNet, which utilizes dense connections between layers (dense blocks), so that all layers are connected. Because of all layers being connected, different layers can reuse features from previous layers which speeds up the process and make this network computationally efficient. To retrain this network we used CT images for 314 patients (over 1500 CT images) consistent of 42 Lung Adenocarcinoma and 78 Squamous Cell Carcinoma, 118 Non cancer and 76 benign were acquired from the National Lung Screening Trial (NLST). These images were divided to two categories of Training and Validation with 70% being training dataset and 30% as validation dataset. We trained our network on Training dataset and then checked the accuracy of our model using the validation dataset. Our model was able to categorize lung cancer with an accuracy of 88%. Afterwards we calculated the the confusion matrix, Precision (Sensitivity), Recall (Positivity) and F1 score of our model for each category. Our model is able to classify Normal CT images with Normal Accuracy of 89% Precision of 94% and F1 score of 93%. For benign nodules Accuracy was 92% precision of 97% and F1 score 86%, while for Adenocarcinoma and squamous cell cancer the Accuracy was 98% and 93%, Precision 85% and 84% and F1 score 92% and 86.9%. The relatively high accuracy of our model shows that convolutional neural networks can be a valuable tool for the classification of lung cancer, especially in a small city or underdeveloped rural hospital settings and can play a role in achieving healthcare equality.
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013965
Subject Headings
Lungs--Cancer, Neural networks (Computer science), Tomography, X-Ray Computed
Format
Document (PDF)