MONTE CARLO MODELING BASED PATIENT DOSE OPTIMIZATION IN DIAGNOSTIC RADIOLOGY

Radiation doses are caused by the energy deposited in unit mass of matter from ionizing radiation. In the US, radiation doses from medical imaging increased six-fold in the past generation. Among medical exposures to patients, computed tomography (CT) composes about half of the collective doses, and interventional fluoroscopy composes 14%. Radiation exposure to patients undergoing diagnostic radiological procedures causes increased lifetime carcinogenic risks, especially for pediatric patients who are more radiosensitive than adults. The correlation between procedural x-ray techniques and the radiation doses to patients, as well as the resultant image quality, is not well understood, and therefore the focus of the performed studies. High radiation dose levels can occur as an outcome of complex procedures requiring additional imaging, or when a patient undergoes multiple radiological procedures. Accumulated occupational doses, caused by the scattered radiation from the patient to the staff during the procedures, are also of concern. There are many factors that affect the patient radiation doses, such as different combinations of technical parameter settings and patient characteristics. Due to the complexities and time-consuming nature of clinical dose/exposure measurements, the Monte Carlo technique is the only realistic tool to investigate patient doses and occupational exposure. Therefore, the objective of this Dissertation is to investigate the possible optimization methods of the irradiation technical factors in order to lower radiation doses to patients undergoing diagnostic radiological examinations using Monte Carlo algorithm-based software. Our general hypothesis is that incident x-ray photon energy used in a diagnostic radiological procedure can be optimized to reduce patient doses without sacrificing image quality, and therefore can lower radiation-induced lifetime carcinogenic risks for patients. Our results will be valuable for medical physicists to analyze dose distributions, and for the cardiology clinicians to maximize image guidance capabilities while minimizing potential carcinogenic and deterministic risks to pediatric patients. Firstly, the impact of irradiation parameters on patient doses during CT scans was investigated and possible optimization methods were discussed. Our results about cone beam CT scans showed that there were major differences in organ and effective dose as the x-ray tube rotates around the patient. This suggested that the use of x-ray tube current modulation could produce substantial reductions in organ and effective dose for body imaging with cone beam CT. For chest CT, our results showed that the existing x-ray tube current modulation schemes are expected to reduce patient effective doses in chest CT examinations by about 10%, with longitudinal modulation accounting for two thirds and angular modulation for the remaining one third. It was also shown that the choice of the scanned region affects organ doses in CT. Secondly, the radiation-induced cancer risks from body CT examinations for adult patients were estimated. For patients who differ from a standard sized adult, correction factors based on the patient weight and antero-posterior dimension are provided to adjust organ doses and the corresponding risks. Our results showed that at constant incident radiation intensity, for CT examinations that include the chest, risks in females are markedly higher than those for males, whereas for examinations that include the pelvis, risks in males were slightly higher than those in females. In abdominal CT scans, risks for males and female patients are very similar. A conclusion was reached that cancer risks in body CT can be estimated from the examination Dose Length Product by accounting for sex, age, as well as patient physical characteristics. Thirdly, a set of innovative Monte Carlo models were developed to investigate the role of x-ray photon energy in determining skin dose, energy imparted, and image quality in pediatric interventional radiology using the MCNP5 platform. Contrast, relative noise, and contrast-to-noise ratio (CNR) were obtained for diagnostic imaging with and without the utilization of grids. Our results indicated that using Monte Carlo methods, the optimized x-ray tube voltage for a relatively low patient dose under the desired image quality could be obtained for any specific patient undergoing a certain type of diagnostic examination. Lastly, we investigated the changes in the pattern of energy deposition in patient phantoms following the use of iodinated contrast media using Monte Carlo models built on MCNP5 platform. Relative energy imparted to the volume of interest with iodine contrast agent, as well as to the whole patient phantom, was calculated. Changes in patterns of energy deposition around the contrast-filled volume were also investigated. Our results suggested that adding iodine can result in values of localized absorbed dose increasing by more than an order of magnitude, but the total energy deposition is generally very modest. Furthermore, our results also showed that adding iodine primarily changes the pattern of energy deposition in the irradiated region, rather than increasing the corresponding patient doses. The goal of this project was to establish a better understanding of the roles of different technique factors in the patient doses from diagnostic radiological procedures. Based on these studies, the limitations of the current Monte Carlo software were analyzed and our own Monte Carlo model was proposed for simulations of patient doses during pediatric interventional radiology procedures. The ultimate goal of this study is to develop a comprehensive dosimetry database using Monte Carlo technique, with the output of patient doses, operator doses, and the corresponding radiation-induced carcinogenesis risks for pediatric interventional radiology procedures

Creep and Shrinkage of High Performance Concrete, and Prediction of Long-Term Camber of Prestressed Bridge Girders

The long-term camber of prestressed bridge girders is typically over-estimated by current Iowa Department of Transportation (IA DOT) methods at erection (typically 3 month after production of girders), especially for long-span bulb tee girders. This often leads to increased costs due to the haunch modifications in the field, and unnecessary delay of construction. Creep and shrinkage of concrete play an important role in the long-term camber of a prestressed bridge girder. The current models used to predict the creep and shrinkage yield large disparties with the actual behavior of concrete in prestressed girders cast using local materials in Iowa. In order to improve the accuracy of prediction of the camber of prestressed bridge girders, creep and shrinkage tests of concrete using local materials were performed. Seven mixes from three precast plants were investigated in this study, in which four of them were high performance concrete (HPC) that are currently used to cast prestressed bridge girders, and three of them were normal concrete (NC) that were utilized to produce prestressed bridge girders previously. Mineral admixtures including slag and fly ash are typically added into HPC. Half of the creep and shrinkage specimens were sealed with Sikagard 62 to minimize the evaporation of water, and the rest were unsealed. All creep and shrinkage specimens with 4 in. diameter and 8 in. height were monitored in an environmentally controlled chamber for one year. In addition, twenty-six prestressed bridge girders produced using HPC from three precast plants were monitored and the corresponding long-term camber was measured. It was observed that due to the early age of loading and the use of slag and fly ash HPC had higher average creep coefficient and average shrinkage strain than NC for both sealed and unsealed specimens during 1 year. It was also found that sealed specimens represent the creep and shrinkage behavior of a full scale prestressed bridge girder much better than unsealed specimens, in agreement with some of the previous literature. It was also observed that the sealed creep coefficient and sealed shrinkage strain measured from the four HPC mixes were similar, and it was acceptable to use the average sealed creep coefficient and average sealed shrinkage strain of the four HPC mixes tested to predict long-term camber of prestressed bridge girders produced in Iowa. Three simplified methods were applied to predict long-term camber of the prestressed bridge girders, including Tadros\u27s Method (2011), Naaman\u27s Method (2004) and an incremental method. Naamans\u27 Method and the incremental method yielded similar results, and both methods yielded 25% errors relative to measured camber of 26 prestressed bridge girders, but Tadros\u27s Method yielded up to 50% errors. The calculation of Naaman\u27s Method was simpler than for the incremental method. Therefore, Naaman\u27s Method was the recommended method to predict the long-term camber of prestressed bridge girders produced in Iowa

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for NPC. Despite recent methods that have achieved promising results on GTV segmentation, they are still limited by lacking carefully-annotated data and hard-to-access data from multiple hospitals in clinical practice. Although some unsupervised domain adaptation (UDA) has been proposed to alleviate this problem, unconditionally mapping the distribution distorts the underlying structural information, leading to inferior performance. To address this challenge, we devise a novel Sourece-Free Active Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV segmentation task. Specifically, we design a dual reference strategy to select domain-invariant and domain-specific representative samples from a specific target domain for annotation and model fine-tuning without relying on source-domain data. Our approach not only ensures data privacy but also reduces the workload for oncologists as it just requires annotating a few representative samples from the target domain and does not need to access the source data. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach. Experimental results show that our method outperforms the UDA methods and achieves comparable results to the fully supervised upper bound, even with few annotations, highlighting the significant medical utility of our approach. In addition, there is no public dataset about multi-center NPC segmentation, we will release code and dataset for future research

Capacity-based Spatial Modulation Constellation and Pre-scaling Design

Spatial Modulation (SM) can utilize the index of the transmit antenna (TA) to transmit additional information. In this paper, to improve the performance of SM, a non-uniform constellation (NUC) and pre-scaling coefficients optimization design scheme is proposed. The bit-interleaved coded modulation (BICM) capacity calculation formula of SM system is firstly derived. The constellation and pre-scaling coefficients are optimized by maximizing the BICM capacity without channel state information (CSI) feedback. Optimization results are given for the multiple-input-single-output (MISO) system with Rayleigh channel. Simulation result shows the proposed scheme provides a meaningful performance gain compared to conventional SM system without CSI feedback. The proposed optimization design scheme can be a promising technology for future 6G to achieve high-efficiency.Comment: 6 pages,conferenc