Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: mitigating key radioprotection issues

Purpose: Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. Methods: With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). Results: The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11–40% relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027–0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). Conclusions: Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses

Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig.

Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues

Purpose: Optimization of CT scan practices can help achieve and maintain optimal radiation protection. The aim was to assess centering, scan length, and positioning of patients undergoing chest CT for suspected or known COVID-19 pneumonia and to investigate their effect on associated radiation doses. Methods: With respective approvals from institutional review boards, we compiled CT imaging and radiation dose data from four hospitals belonging to four countries (Brazil, Iran, Italy, and USA) on 400 adult patients who underwent chest CT for suspected or known COVID-19 pneumonia between April 2020 and August 2020. We recorded patient demographics and volume CT dose index (CTDIvol) and dose length product (DLP). From thin-section CT images of each patient, we estimated the scan length and recorded the first and last vertebral bodies at the scan start and end locations. Patient mis-centering and arm position were recorded. Data were analyzed with analysis of variance (ANOVA). Results: The extent and frequency of patient mis-centering did not differ across the four CT facilities (>0.09). The frequency of patients scanned with arms by their side (11�40 relative to those with arms up) had greater mis-centering and higher CTDIvol and DLP at 2/4 facilities (p = 0.027�0.05). Despite lack of variations in effective diameters (p = 0.14), there were significantly variations in scan lengths, CTDIvol and DLP across the four facilities (p < 0.001). Conclusions: Mis-centering, over-scanning, and arms by the side are frequent issues with use of chest CT in COVID-19 pneumonia and are associated with higher radiation doses. © 202

Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT

(1) Background: Optimal anatomic coverage is important for radiation-dose optimization. We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT as those with optimum, under-scanned, or over-scanned scan length. (2) Methods: To test our hypothesis, we performed a study with 428 consecutive chest CT examinations (mean age 70 ± 14 years; male:female 190:238) performed at one of the four hospitals. CT examinations from two hospitals were used to train the DL classification algorithms to identify lung apices and bases. The developed algorithms were then tested on the data from the remaining two hospitals. For each CT, we recorded the scan lengths above and below the lung apices and bases. Model performance was assessed with receiver operating characteristics (ROC) analysis. (3) Results: The two DL models for lung apex and bases had high sensitivity, specificity, accuracy, and areas under the curve (AUC) for identifying under-scanning (100%, 99%, 99%, and 0.999 (95% CI 0.996–1.000)) and over-scanning (99%, 99%, 99%, and 0.998 (95%CI 0.992–1.000)). (4) Conclusions: Our DL models can accurately identify markers for missing anatomic coverage and over-scanning in chest CTs

Auto-Detection of Motion Artifacts on CT Pulmonary Angiograms with a Physician-Trained AI Algorithm

Purpose: Motion-impaired CT images can result in limited or suboptimal diagnostic interpretation (with missed or miscalled lesions) and patient recall. We trained and tested an artificial intelligence (AI) model for identifying substantial motion artifacts on CT pulmonary angiography (CTPA) that have a negative impact on diagnostic interpretation. Methods: With IRB approval and HIPAA compliance, we queried our multicenter radiology report database (mPower, Nuance) for CTPA reports between July 2015 and March 2022 for the following terms: “motion artifacts”, “respiratory motion”, “technically inadequate”, and “suboptimal” or “limited exam”. All CTPA reports were from two quaternary (Site A, n = 335; B, n = 259) and a community (C, n = 199) healthcare sites. A thoracic radiologist reviewed CT images of all positive hits for motion artifacts (present or absent) and their severity (no diagnostic effect or major diagnostic impairment). Coronal multiplanar images from 793 CTPA exams were de-identified and exported offline into an AI model building prototype (Cognex Vision Pro, Cognex Corporation) to train an AI model to perform two-class classification (“motion” or “no motion”) with data from the three sites (70% training dataset, n = 554; 30% validation dataset, n = 239). Separately, data from Site A and Site C were used for training and validating; testing was performed on the Site B CTPA exams. A five-fold repeated cross-validation was performed to evaluate the model performance with accuracy and receiver operating characteristics analysis (ROC). Results: Among the CTPA images from 793 patients (mean age 63 ± 17 years; 391 males, 402 females), 372 had no motion artifacts, and 421 had substantial motion artifacts. The statistics for the average performance of the AI model after five-fold repeated cross-validation for the two-class classification included 94% sensitivity, 91% specificity, 93% accuracy, and 0.93 area under the ROC curve (AUC: 95% CI 0.89–0.97). Conclusion: The AI model used in this study can successfully identify CTPA exams with diagnostic interpretation limiting motion artifacts in multicenter training and test datasets. Clinical relevance: The AI model used in the study can help alert technologists about the presence of substantial motion artifacts on CTPA, where a repeat image acquisition can help salvage diagnostic information