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Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods? [article]

Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K. Kalra, Ge Wang
2018 arXiv   pre-print
Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K.  ... 
arXiv:1811.03691v1 fatcat:75zgv47hbzdajptylxzvhms7ee

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Fatemeh Homayounieh, Ruhani Doda Khera, Bernardo Canedo Bizzo, Shadi Ebrahimian, Andrew Primak, Bernhard Schmidt, Sanjay Saini, Mannudeep K. Kalra
2020 Abdominal Radiology  
To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi. The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal
more » ... lculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output. Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85-0.92). Higher-order radiomics (gray-level size zone matrix - GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89-0.92). Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.
doi:10.1007/s00261-020-02865-0 pmid:33242099 pmcid:PMC7690335 fatcat:edscnkyvyjhynoegd52noppqve

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography

Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
2021 Nature Communications  
AbstractCancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieves an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identifies patients with high
more » ... mortality risks (AUC of 0.768). We validate our model against ECG-gated cardiac CT based markers, including coronary artery calcification (CAC) score, CAD-RADS score, and MESA 10-year risk score from an independent dataset of 335 subjects. Our work shows that, in high-risk patients, deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation.
doi:10.1038/s41467-021-23235-4 pmid:34017001 fatcat:cwi6j4vpu5fvjf3qqajxtine6u

Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

Azadeh Tabari, Singh Ramandeep, Ruhani Doda Khera, Yiemeng Hoi, Erin Angel, Mannudeep K. Kalra, Rachna Madan
2019 European Journal of Radiology Open  
We assessed the effect of the forward projected model-based reconstruction technique (FIRST) on lesion detection of routine abdomen CT at <1 mSv. Thirty-seven adult patients gave written informed consent for acquisition of low-dose CT (LDCT) immediately after their clinically-indicated, standard of care dose (SDCT), routine abdomen CT on a 640-slice MDCT (Aquillion One, Canon Medical System). The LDCT series were reconstructed with FIRST (at STD (Standard) and STR (Strong) levels), and SDCT
more » ... es with filtered back projection (FBP). Two radiologists assessed lesions in LD-FBP and FIRST images followed by SDCT images. Then, SDCT and LDCT were compared for presence of artifacts in a randomized and blinded fashion. Patient demographics, size and radiation dose descriptors (CTDIvol, DLP) were recorded. Descriptive statistics and inter-observer variability were calculated for data analysis. Mean CTDIvol for SDCT and LDCT were 13 ± 4.7 mGy and 2.2 ± 0.8 mGy, respectively. There were 46 true positive lesions detected on SDCT. Radiologists detected 38/46 lesions on LD-FIRST-STD compared to 26/46 lesions on LD-FIRST-STR. The eight lesions (liver and kidney cysts, pancreatic lesions, sub-cm peritoneal lymph node) missed on LD-FIRST-STD were seen in patients with BMI > 25.8 kg/m2. Diagnostic confidence for lesion assessment was optimal in LD-FIRST-STD setting in most patients regardless of their size. The inter-observer agreement (kappa-value) for overall image quality were 0.98 and 0.84 for LD-FIRST-STD and STR levels, respectively. FIRST enabled optimal lesion detection in routine abdomen CT at less than 1 mSv radiation dose in patients with body mass less than ≤25.8 kg/m2.
doi:10.1016/j.ejro.2019.05.001 pmid:31304196 pmcid:PMC6603257 fatcat:77heiso6abcdtifijz5edkoy2m

Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography [article]

Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan
2020 arXiv   pre-print
The high risk population of cardiovascular disease (CVD) is simultaneously at high risk of lung cancer. Given the dominance of low dose computed tomography (LDCT) for lung cancer screening, the feasibility of extracting information on CVD from the same LDCT scan would add major value to patients at no additional radiation dose. However, with strong noise in LDCT images and without electrocardiogram (ECG) gating, CVD risk analysis from LDCT is highly challenging. Here we present an innovative
more » ... p learning model to address this challenge. Our deep model was trained with 30,286 LDCT volumes and achieved the state-of-the-art performance (area under the curve (AUC) of 0.869) on 2,085 National Lung Cancer Screening Trial (NLST) subjects, and effectively identified patients with high CVD mortality risks (AUC of 0.768). Our deep model was further calibrated against the clinical gold standard CVD risk scores from ECG-gated dedicated cardiac CT, including coronary artery calcification (CAC) score, CAD-RADS score and MESA 10-year CHD risk score from an independent dataset of 106 subjects. In this validation study, our model achieved AUC of 0.942, 0.809 and 0.817 for CAC, CAD-RADS and MESA scores, respectively. Our deep learning model has the potential to convert LDCT for lung cancer screening into dual-screening quantitative tool for CVD risk estimation.
arXiv:2008.06997v1 fatcat:ojelmuunbrfsjpdxuhfh2y4hq4

Deploying Clinical Process Improvement Strategies to Reduce Motion Artifacts and Expiratory Phase Scanning in Chest CT

Ruhani Doda Khera, Ramandeep Singh, Fatemeh Homayounieh, Evan Stone, Travis Redel, Cristy A. Savage, Katherine Stockton, Jo-Anne O. Shepard, Mannudeep K. Kalra, Subba R. Digumarthy
2019 Scientific Reports  
We hypothesized that clinical process improvement strategies can reduce frequency of motion artifacts and expiratory phase scanning in chest CT. We reviewed 826 chest CT to establish the baseline frequency. Per clinical process improvement guidelines, we brainstormed corrective measures and priority-pay-off matrix. The first intervention involved education of CT technologists, following which 795 chest CT were reviewed. For the second intervention, instructional videos on optimal breath-hold
more » ... e shown to 245 adult patients just before their chest CT. Presence of motion artifacts and expiratory phase scanning was assessed. We also reviewed 311 chest CT scans belonging to a control group of patients who did not see the instructional videos. Pareto and percentage run charts were created for baseline and post-intervention data. Baseline incidence of motion artifacts and expiratory phase scanning in chest CT was 35% (292/826). There was no change in the corresponding incidence following the first intervention (36%; 283/795). Respiratory motion and expiratory phase chest CT with the second intervention decreased (8%, 20/245 patients). Instructional videos for patients (and not education and training of CT technologists) reduce the frequency of motion artifacts and expiratory phase scanning in chest CT.
doi:10.1038/s41598-019-48423-7 pmid:31413297 pmcid:PMC6694170 fatcat:vu2atiugwvhgdolbwbqwvcsyoe

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome

Fatemeh Homayounieh, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Shadi Ebrahimian, Ruhani Doda Khera, Bernardo C. Bizzo, Varun Buch, Rosa Babaei, Hadi Karimi Mobin, Iman Mohseni, Matthias Mitschke, Mathis Zimmermann, Felix Durlak (+3 others)
2021 Journal of digital imaging  
Authors and Affiliations Fatemeh Homayounieh 1 · Marcio Aloisio Bezerra Cavalcanti Rockenbach 2 · Shadi Ebrahimian 1 · Ruhani Doda Khera 1 · Bernardo C.  ... 
doi:10.1007/s10278-021-00430-9 pmid:33634416 fatcat:uutg2jzwhjcjjexeeodqh3r4im

Breast Cancer Detection in Qatar: Evaluation of Mammography Image Quality Using A Standardized Assessment Tool

Anand K. Narayan, Massachusetts General Hospital, Boston, MA, USA, Huda Al-Naemi, Antar Aly, Mohammad Hassan Kharita, Ruhani Doda Khera, Mohamad Hajaj, Madan M. Rehani, Hamad Medical Corporation, Doha, Qatar, Hamad Medical Corporation, Doha, Qatar, Hamad Medical Corporation, Doha, Qatar, Massachusetts General Hospital, Boston, MA, USA (+2 others)
2020 European Journal of Breast Health  
Compared with other countries in the Middle East, Qatar has one of the highest breast cancer incidence and mortality rates. Poor quality mammography images may be associated with advanced stage breast cancer, however there is limited information about the quality of breast imaging in Qatar. Our purpose was to evaluate the clinical image quality of mammography examinations performed at a tertiary care center in Doha, Qatar using a standardized assessment tool. Bilateral mammograms from
more » ... e patients from a tertiary care cancer center in Doha, Qatar were obtained. Proportions of examinations deemed adequate for interpretation were estimated. Standardized clinical image quality assessment form was utilized to evaluate image quality components. For each image, image quality components were given grades on a 1-5 scale (5-excellent, 4-good, 3-average, 2-fair, 1-poor). Mean scores with 95% confidence intervals were estimated for each component. Consecutive sample of 132 patients was obtained representing 528 mammographic images. Overall, 99.2% of patients underwent examinations rated as acceptable for interpretation. Mean scores for each image quality component ranged from 4.045 to 5.000 (lowest score for inframammary fold). Image quality component scores were 93.0% excellent, 5.2% good, 1.1% average, 0.6% fair, and 0.1% poor. Overall image quality at a tertiary care center in Doha, Qatar was acceptable for interpretation with minimal areas identified for improvement.
doi:10.5152/ejbh.2020.5115 pmid:32285034 pmcid:PMC7138364 fatcat:wr5e4swrknhyblfas2ktbbjobu

Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks [article]

Rachel Lea Draelos, Lawrence Carin
2021 arXiv   pre-print
Radiology, 296(2), 2020. 4 [48] Xiang Li, James H Thrall, Subba R Digumarthy, Man- nudeep K Kalra, Pari V Pandharipande, Bowen Zhang, Chayanin Nitiwarangkul, Ramandeep Singh, Ruhani Doda Khera, and  ... 
arXiv:2011.08891v4 fatcat:yo6gawyxdraedbe3nmwnwzvxu4