The Effects of Organ-based Tube Current Modulation on Radiation Dose and Image Quality in Computed Tomography Imaging

The purpose of this thesis was to quantify dose and noise performance of organ-dose-based tube current modulation (ODM) through experimental studies with an anthropomorphic phantom and simulations with a voxelized phantom library. Tube current modulation is a dose reduction technique that modulates radiation dose in angular and/or slice directions based on patient attenuation. ODM technique proposed by GE Healthcare further reduces tube current for anterior source positions, without increasing current for posterior positions. Axial CT scans at 120 kV were performed on head and chest phantoms (Rando Alderson Research Laboratories, Stanford, CA) on an ODM-equipped scanner (Optima CT660, GE Healthcare, Chalfont St Giles, England). Dosimeters quantified dose to breast, lung, heart, spine, eye lens and brain regions (mobile MOSFET Dosimetry System, Best Medical, Ottawa, Canada) for ODM, AutomA (z-axis modulation), and SmartmA (angular and z-axis modulation) settings. Noise standard deviation was calculated in brain and chest regions of reconstructed images. To study a variety of patient sizes, Monte Carlo dose simulations, validated with experimental data, were performed on voxelized head and chest phantoms. Experimental studies on anthropomorphic chest and head phantoms demonstrated reduction in dose at all dosimeter locations with respect to SmartmA, with dose changes of -31.3% (breast), -20.7% (lung), -24.4% (heart), -5.9% (spine), -18.9% (eye), and -10.1% (brain). Simulation studies using voxelized phantoms indicated average dose changes of -33.4% (breast), -20.2% (lung), -18.6% (spine), -20.0% (eye) and -7.2% (brain). ODM reduced dose to the brain and lung tissues, however these tissues would experience up to 15.2% and 13.1% dose increase respectively at noise standard deviation equal to SmartmA. ODM reduced dose to the eye lens in 22 of 28 phantoms (-1.2% to -12.4%), had no change in dose for one phantom, and increased dose for four phantoms (0.7% to 2.3% ) with respect to SmartmA at equal noise standard deviation. All phantoms demonstrated breast dose reduction (-2.1% to -27.6%) at equal noise standard deviation. Experimental and simulation studies over a range of patient sizes indicate that ODM has the potential to reduce dose to radiosensitive organs by 5 - 38% with a limited increase in image noise

Technical Note: Phantom study to evaluate the dose and image quality effects of a computed tomography Organ-based Tube Current Modulation Technique

Purpose This technical note quantifies the dose and image quality performance of a clinically available organ-dose-based tube current modulation (ODM) technique, using experimental and simulation phantom studies. The investigated ODM implementation reduces the tube current for the anterior source positions, without increasing current for posterior positions, although such an approach was also evaluated for comparison. Methods Axial CT scans at 120 kV were performed on head and chest phantoms on an ODM-equipped scanner (Optima CT660, GE Healthcare, Chalfont St. Giles, England). Dosimeters quantified dose to breast, lung, heart, spine, eye lens, and brain regions for ODM and 3D-modulation (SmartmA) settings. Monte Carlo simulations, validated with experimental data, were performed on 28 voxelized head phantoms and 10 chest phantoms to quantify organ dose and noise standard deviation. The dose and noise effects of increasing the posterior tube current were also investigated. Results ODM reduced the dose for all experimental dosimeters with respect to SmartmA, with average dose reductions across dosimeters of 31% (breast), 21% (lung), 24% (heart), 6% (spine), 19% (eye lens), and 11% (brain), with similar results for the simulation validation study. In the phantom library study, the average dose reduction across all phantoms was 34% (breast), 20% (lung), 8% (spine), 20% (eye lens), and 8% (brain). ODM increased the noise standard deviation in reconstructed images by 6%–20%, with generally greater noise increases in anterior regions. Increasing the posterior tube current provided similar dose reduction as ODM for breast and eye lens, increased dose to the spine, with noise effects ranging from 2% noise reduction to 16% noise increase. At noise equal to SmartmA, ODM increased the estimated effective dose by 4% and 8% for chest and head scans, respectively. Increasing the posterior tube current further increased the effective dose by 15% (chest) and 18% (head) relative to SmartmA. Conclusions ODM reduced dose in all experimental and simulation studies over a range of phantoms, while increasing noise. The results suggest a net dose/noise benefit for breast and eye lens for all studied phantoms, negligible lung dose effects for two phantoms, increased lung dose and/or noise for eight phantoms, and increased dose and/or noise for brain and spine for all studied phantoms compared to the reference protocol

The Effects of Gantry Tilt on Breast Dose and Image Noise in Cardiac CT

Purpose: This study investigated the effects of tilted-gantry acquisition on image noise and glandular breast dose in females during cardiac computed tomography (CT) scans. Reducing the dose to glandular breast tissue is important due to its high radiosensitivity and limited diagnostic significance in cardiac CT scans. Methods: Tilted-gantry acquisition was investigated through computer simulations and experimental measurements. Upon IRB approval, eight voxelized phantoms were constructed from previously acquired cardiac CT datasets. Monte Carlo simulations quantified the dose deposited in glandular breast tissue over a range of tilt angles. The effects of tilted-gantry acquisition on breast dose were measured on a clinical CT scanner (CT750HD, GE Healthcare) using an anthropomorphic phantom with MOSFET dosimeters in the breast regions. In both simulations and experiments, scans were performed at gantry tilt angles of 0°–30°, in 5° increments. The percent change in breast dose was calculated relative to the nontilted scan for all tilt angles. The percent change in noise standard deviation due to gantry tilt was calculated in all reconstructed simulated and experimental images. Results: Tilting the gantry reduced the breast dose in all simulated and experimental phantoms, with generally greater dose reduction at increased gantry tilts. For example, at 30° gantry tilt, the dosimeters located in the superior, middle, and inferior breast regions measured dose reductions of 74%, 61%, and 9%, respectively. The simulations estimated 0%–30% total breast dose reduction across the eight phantoms and range of tilt angles. However, tilted-gantry acquisition also increased the noise standard deviation in the simulated phantoms by 2%–50% due to increased pathlength through the iodine-filled heart. The experimental phantom, which did not contain iodine in the blood, demonstrated decreased breast dose and decreased noise at all gantry tilt angles. Conclusions: Tilting the gantry reduced the dose to the breast, while also increasing noise standard deviation. Overall, the noise increase outweighed the dose reduction for the eight voxelized phantoms, suggesting that tilted gantry acquisition may not be beneficial for reducing breast dose while maintaining image quality

Diagnosis and treatment planning using the 2017 classification of periodontal diseases among three dental schools

Objectives: The American Academy of Periodontology and the European Federation of Periodontology developed a new classification system for periodontal diseases in 2017. The next step in its widespread implementation involves training dental students to improve consistency in clinical decisions. This study conducted in 2020–2021 aimed to evaluate knowledge in periodontal diagnosis and treatment planning using the new classification, among first, second, third- and fourth-year dental students at Indiana University School of Dentistry (IUSD), University of Texas School of Dentistry at Houston (UTSD), and University of Louisville School of Dentistry (ULSD). Methods: A minimum of 20 dental students per class year from each of the three schools participated. Ten HIPPA de-identified case records and a questionnaire with a fixed list of answer options, comprising two demographic questions and two questions on diagnosis and treatment planning of each case, were presented to the participants. A group of three board-certified periodontists established the answers for all cases which were used to score the appropriateness of diagnosis and treatment planning among the participants. Results: A total of 263 students participated. Overall, 22.6% of IUSD responses, 25.2% of UTSD, and 27.6% of ULSD responses were correct for diagnosis (no statistically significant differences). For the treatment plan, 64.9% of IUSD responses, 66.2% of UTSD, and 68.9% of ULSD responses were correct (no statistically significant differences). Conclusion: Based on the findings from our study, we suggest that additional training be considered to improve the understanding of the 2017 classification of periodontal and peri-implant diseases among dental students

A time domain 2D OaA-based convolutional neural networks accelerator

Convolutional neural networks (CNNs) are widely implemented in modern facial recognition systems for image recognition applications. Runtime speed is a critical parameter for real-time systems. Traditional FPGA-based accelerations require either large on-chip memory or high bandwidth and high memory access time that slow down the network. The proposed work uses an algorithm and its subsequent hardware design for a quick CNN computation using an overlap-and-add-based technique in the time domain. In the algorithm, the input images are broken into tiles that can be processed independently without computing overhead in the frequency domain. This also allows for efficient concurrency of the convolution process, resulting in higher throughput and lower power consumption. At the same time, we maintain low on-chip memory requirements necessary for faster and cheaper processor designs. We implemented CNN VGG-16 and AlexNet models with our design on Xilinx Virtex-7 and Zynq boards. The performance analysis of our design provides 48% better throughput than the state-of-the-art AlexNet and uses 68.85% lesser multipliers and other resources than the state-of-the-art VGG-16