Patient-specific fetal radiation dosimetry for pregnant patients undergoing abdominal and pelvic CT imaging

Background: Accurate estimation of fetal radiation dose is crucial for risk-benefit analysis of radiological imaging, while the radiation dosimetry studies based on individual pregnant patient are highly desired. Purpose: To use Monte Carlo calculations for estimation of fetal radiation dose from abdominal and pelvic computed tomography (CT) examinations for a population of patients with a range of variations in patients’ anatomy, abdominal circumference, gestational age (GA), fetal depth (FD), and fetal development. Methods: Forty-four patient-specific pregnant female models were constructed based on CT imaging data of pregnant patients, with gestational ages ranging from 8 to 35 weeks. The simulation of abdominal and pelvic helical CT examinations was performed on three validated commercial scanner systems to calculate organ-level fetal radiation dose. Results: The absorbed radiation dose to the fetus ranged between 0.97 and 2.24 mGy, with an average of 1.63 ± 0.33 mGy. The CTDIvol-normalized fetal dose ranged between 0.56 and 1.30, with an average of 0.94 ± 0.25. The normalized fetal organ dose showed significant correlations with gestational age, maternal abdominal circumference (MAC), and fetal depth. The use of ATCM technique increased the fetal radiation dose in some patients. Conclusion: A technique enabling the calculation of organ-level radiation dose to the fetus was developed from models of actual anatomy representing a range of gestational age, maternal size, and fetal position. The developed maternal and fetal models provide a basis for reliable and accurate radiation dose estimation to fetal organs.</p

Attention Allocation Aid for Visual Search

This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.Comment: To be presented at the ACM CHI conference in Denver, Colorado in May 201

Automatic Diagnosis for Prostate Cancer Using Run-Length Matrix Method

Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist as normal tissue or prostatic carcinoma (PCa). The sample was then subsequently digitized at 50X magnification. We divided the digitized image into sub-regions of 20 X 20 pixels and classified each sub-region as normal or PCa by a texture analysis method. In the texture analysis, we computed texture features for each of the sub-regions based on the Gray-level Run-length Matrix(GL-RLM). Those features include LGRE, HGRE and RPC from the run-length matrix, mean and standard deviation of the pixel intensity. We utilized a feature selection algorithm to select a set of effective features and used a multi-layer perceptron (MLP) classifier to distinguish normal from PCa. In total, the whole histological image was divided into 42 PCa and 6280 normal regions. Three-fold cross validation results show that the proposed method achieves an average classification accuracy of 89.5% with a sensitivity and specificity of 90.48% and 89.49%, respectively

Validating Pareto Optimal Operation Parameters of Polyp Detection Algorithms for CT Colonography

We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or the supine scan or both. In the two-fold CV, we randomly divided the 133 patients into two cohorts. Each cohort was used to obtain the Pareto front by a multi-objective genetic algorithm, where a set of optimized thresholds was applied on the test cohort to get test results. This process was repeated twice so that each cohort was used in the training and testing process once. We averaged the two training Pareto fronts as our final training Pareto front and averaged the test results from the two runs in the CV as our final test results. Our experiments demonstrated that the averaged testing results were close to the mean Pareto front determined from the training process. We conclude that the Pareto front-based algorithm appears to be generalizable to new test data

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: A pilot study of a potential cancer field effect

Purpose: In women with biopsy-proven breast cancer, histologically normal areas of the parenchyma have shown molecular similarity to the tumor, supporting a potential cancer field effect. The purpose of this work was to investigate relationships of human-engineered radiomic and deep learning features between regions across the breast in mammographic parenchymal patterns and specimen radiographs. Approach: This study included mammograms from 74 patients with at least 1 identified malignant tumor, of whom 32 also possessed intraoperative radiographs of mastectomy specimens. Mammograms were acquired with a Hologic system and specimen radiographs were acquired with a Fujifilm imaging system. All images were retrospectively collected under an Institutional Review Board-approved protocol. Regions of interest (ROI) of 128 × 128 pixels were selected from three regions: within the identified tumor, near to the tumor, and far from the tumor. Radiographic texture analysis was used to extract 45 radiomic features and transfer learning was used to extract 20 deep learning features in each region. Kendall’s Tau-b and Pearson correlation tests were performed to assess relationships between features in each region. Results: Statistically significant correlations in select subgroups of features with tumor, near to the tumor, and far from the tumor ROI regions were identified in both mammograms and specimen radiographs. Intensity-based features were found to show significant correlations with ROI regions across both modalities. Conclusions: Results support our hypothesis of a potential cancer field effect, accessible radiographically, across tumor and non-tumor regions, thus indicating the potential for computerized analysis of mammographic parenchymal patterns to predict breast cancer risk.</p

Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons

Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify longitudinal representativeness of the demographic characteristics of the primary MIDRC dataset compared to the United States general population (US Census) and COVID-19 positive case counts from the Centers for Disease Control and Prevention (CDC). Approach: The Jensen-Shannon distance (JSD), a measure of similarity of two distributions, was used to longitudinally measure the representativeness of the distribution of (1) all unique patients in the MIDRC data to the 2020 US Census and (2) all unique COVID-19 positive patients in the MIDRC data to the case counts reported by the CDC. The distributions were evaluated in the demographic categories of age at index, sex, race, ethnicity, and the combination of race and ethnicity. Results: Representativeness of the MIDRC data by ethnicity and the combination of race and ethnicity was impacted by the percentage of CDC case counts for which this was not reported. The distributions by sex and race have retained their level of representativeness over time. Conclusion: The representativeness of the open medical imaging datasets in the curated public data commons at MIDRC has evolved over time as the number of contributing institutions and overall number of subjects have grown. The use of metrics, such as the JSD support measurement of representativeness, is one step needed for fair and generalizable AI algorithm development.</p

MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence