McCarthy CJ, Yu AYC, Do S, Dawson SL, Uppot RN
Journal of the American College of Radiology : JACR
Interventional Radiology Training Using a Dynamic Medical Immersive Training Environment (DynaMITE). J Am Coll Radiol. 2018 05; 15(5):789-793.
Interventional Radiology Training Using a Dynamic Medical Immersive Training Environment (DynaMITE).
Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Geis JR, Pandharipande PV, Brink JA, Dreyer KJ
Current Applications and Future Impact of Machine Learning in Radiology. Radiology. 2018 Aug; 288(2):318-328.
Current Applications and Future Impact of Machine Learning in Radiology.
Schlett CL, Maurovich-Horvat P, Ferencik M, Alkadhi H, Stolzmann P, Scheffel H, Seifarth H, Nakano M, Do S, Vorpahl M, Kauczor HU, Bamberg F, Tearney GJ, Virmani R, Hoffmann U
Histogram analysis of lipid-core plaques in coronary computed tomographic angiography: ex vivo validation against histology. Invest Radiol. 2013 Sep; 48(9):646-53.
Histogram analysis of lipid-core plaques in coronary computed tomographic angiography: ex vivo validation against histology.
Scheffel H, Stolzmann P, Schlett CL, Engel LC, Major GP, Károlyi M, Do S, Maurovich-Horvat P, Hoffmann U
Coronary artery plaques: cardiac CT with model-based and adaptive-statistical iterative reconstruction technique. Eur J Radiol. 2012 Mar; 81(3):e363-9.
Coronary artery plaques: cardiac CT with model-based and adaptive-statistical iterative reconstruction technique.
Tajmir SH, Lee H, Shailam R, Gale HI, Nguyen JC, Westra SJ, Lim R, Yune S, Gee MS, Do S
Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol. 2019 Feb; 48(2):275-283.
Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability.
Singh S, Kalra MK, Do S, Thibault JB, Pien H, O'Connor OJ, Connor OO, Blake MA
Comparison of hybrid and pure iterative reconstruction techniques with conventional filtered back projection: dose reduction potential in the abdomen. J Comput Assist Tomogr. 2012 May-Jun; 36(3):347-53.
Comparison of hybrid and pure iterative reconstruction techniques with conventional filtered back projection: dose reduction potential in the abdomen.
Do S, Salvaggio K, Gupta S, Kalra M, Ali NU, Pien H
Computational and mathematical methods in medicine
Automated quantification of pneumothorax in CT. Comput Math Methods Med. 2012; 2012:736320.
Automated quantification of pneumothorax in CT.
Yune S, Lee H, Kim M, Tajmir SH, Gee MS, Do S
Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model. J Digit Imaging. 2019 08; 32(4):665-671.
Beyond Human Perception: Sexual Dimorphism in Hand and Wrist Radiographs Is Discernible by a Deep Learning Model.
He L, Orten B, Do S, Karl WC, Kambadakone A, Sahani DV, Pien H
A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Trans Med Imaging. 2010 May; 29(5):1182-91.
A spatio-temporal deconvolution method to improve perfusion CT quantification.
Radiographic Image Interpretation, Computer-Assisted
Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, Gonzalez RG, Lev MH, Do S
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng. 2019 03; 3(3):173-182.
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets.
Lee H, Huang C, Yune S, Tajmir SH, Kim M, Do S
Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction. Sci Rep. 2019 10 29; 9(1):15540.
Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction.
Sim Y, Chung MJ, Kotter E, Yune S, Kim M, Do S, Han K, Kim H, Yang S, Lee DJ, Choi BW
Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs. Radiology. 2020 01; 294(1):199-209.
Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.
Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning. Korean J Radiol. 2020 01; 21(1):33-41.
Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning.
Parakh A, Lee H, Lee JH, Eisner BH, Sahani DV, Do S
Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization. Radiol Artif Intell. 2019 Jul; 1(4):e180066.
Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization.
Kim D, Chung J, Choi J, Succi MD, Conklin J, Longo MGF, Ackman JB, Little BP, Petranovic M, Kalra MK, Lev MH, Do S
Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model. Nat Commun. 2022 04 06; 13(1):1867.
Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model.
A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI). Comput Biol Med. 2022 05; 144:105332.
A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI).
Chung J, Kim D, Choi J, Yune S, Song KD, Kim S, Chua M, Succi MD, Conklin J, Longo MGF, Ackman JB, Petranovic M, Lev MH, Do S
Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach. Sci Rep. 2022 12 07; 12(1):21164.
Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.
Chua M, Kim D, Choi J, Lee NG, Deshpande V, Schwab J, Lev MH, Gonzalez RG, Gee MS, Do S
Tackling prediction uncertainty in machine learning for healthcare. Nat Biomed Eng. 2023 06; 7(6):711-718.
Tackling prediction uncertainty in machine learning for healthcare.
Witowski J, Choi J, Jeon S, Kim D, Chung J, Conklin J, Longo MGF, Succi MD, Do S
MarkIt: A Collaborative Artificial Intelligence Annotation Platform Leveraging Blockchain For Medical Imaging Research. Blockchain Healthc Today. 2021; 4.
MarkIt: A Collaborative Artificial Intelligence Annotation Platform Leveraging Blockchain For Medical Imaging Research.
Yoon BC, Pomerantz SR, Mercaldo ND, Goyal S, L'Italien EM, Lev MH, Buch KA, Buchbinder BR, Chen JW, Conklin J, Gupta R, Hunter GJ, Kamalian SC, Kelly HR, Rapalino O, Rincon SP, Romero JM, He J, Schaefer PW, Do S, González RG
Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs. PLoS One. 2023; 18(3):e0281900.
Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs.
Author Correction: Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach. Sci Rep. 2023 Mar 15; 13(1):4296.
Author Correction: Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach.
Beyond the AJR: An International Competition Advances Artificial Intelligence Research. AJR Am J Roentgenol. 2024 01; 222(1):e2329644.
Beyond the AJR: An International Competition Advances Artificial Intelligence Research.
Artificial Intelligence Applied to Contrast-enhanced Mammography: Exploring Uncharted Territory. Radiology. 2023 06; 307(5):e231140.
Artificial Intelligence Applied to Contrast-enhanced Mammography: Exploring Uncharted Territory.
Lin LY, Zhou P, Shi M, Lu JE, Jeon S, Kim D, Liu JM, Wang M, Do S, Lee NG
A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy. Ophthalmol Sci. 2024 Jan-Feb; 4(1):100412.
A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy.
Oosterhoff JHF, Jeon S, Akhbari B, Shin D, Tobert DG, Do S, Ashkani-Esfahani S
OTA international : the open access journal of orthopaedic trauma
A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry. OTA Int. 2023 Dec; 6(5 Suppl):e283.
A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry.
Alkhadrawi AM, Lin LY, Langarica SA, Kim K, Ha SK, Lee NG, Do S
Investigative ophthalmology & visual science
Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease. Invest Ophthalmol Vis Sci. 2024 May 01; 65(5):6.
Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.
Artificial Intelligence for Breast Cancer Screening: Trade-offs between Sensitivity and Specificity. Radiol Artif Intell. 2024 05; 6(3):e240184.
Artificial Intelligence for Breast Cancer Screening: Trade-offs between Sensitivity and Specificity.
Kim YT, Kim H, So M, Kong J, Kim KT, Hong JH, Son Y, Sa JK, Do S, Han JH, Kim JB
Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity. Neuroimage. 2024 Aug 15; 297:120749.
Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.
Kalra MK, Woisetschläger M, Dahlström N, Singh S, Digumarthy S, Do S, Pien H, Quick P, Schmidt B, Sedlmair M, Shepard JA, Persson A
Sinogram-affirmed iterative reconstruction of low-dose chest CT: effect on image quality and radiation dose. AJR Am J Roentgenol. 2013 Aug; 201(2):W235-44.
Sinogram-affirmed iterative reconstruction of low-dose chest CT: effect on image quality and radiation dose.
Journal of the Korean Society of Radiology
[Explainable & Safe Artificial Intelligence in Radiology]. J Korean Soc Radiol. 2024 Sep; 85(5):834-847.
[Explainable & Safe Artificial Intelligence in Radiology].
Kim YT, Han BS, Kim JB, Sa JK, Hong JH, Son Y, Han JH, Do S, Chae JS, Bae JK
Journal of orthopaedic surgery and research
HKA-Net: clinically-adapted deep learning for automated measurement of hip-knee-ankle angle on lower limb radiography for knee osteoarthritis assessment. J Orthop Surg Res. 2024 Nov 20; 19(1):777.
HKA-Net: clinically-adapted deep learning for automated measurement of hip-knee-ankle angle on lower limb radiography for knee osteoarthritis assessment.
Puchner SB, Ferencik M, Karolyi M, Do S, Maurovich-Horvat P, Kauczor HU, Hoffmann U, Schlett CL
The international journal of cardiovascular imaging
The effect of iterative image reconstruction algorithms on the feasibility of automated plaque assessment in coronary CT angiography. Int J Cardiovasc Imaging. 2013 Dec; 29(8):1879-88.
The effect of iterative image reconstruction algorithms on the feasibility of automated plaque assessment in coronary CT angiography.
Schlett CL, Ferencik M, Celeng C, Maurovich-Horvat P, Scheffel H, Stolzmann P, Do S, Kauczor HU, Alkadhi H, Bamberg F, Hoffmann U
European heart journal. Cardiovascular Imaging
How to assess non-calcified plaque in CT angiography: delineation methods affect diagnostic accuracy of low-attenuation plaque by CT for lipid-core plaque in histology. Eur Heart J Cardiovasc Imaging. 2013 Nov; 14(11):1099-105.
How to assess non-calcified plaque in CT angiography: delineation methods affect diagnostic accuracy of low-attenuation plaque by CT for lipid-core plaque in histology.
Liew G, Ali N, Do S, Petranovic M, Cury R, Brady T, Hoffmann U, Ghoshhajra B
A novel analysis algorithm for potential quantitative assessment of myocardial computed tomography perfusion. Acad Radiol. 2013 Oct; 20(10):1301-5.
A novel analysis algorithm for potential quantitative assessment of myocardial computed tomography perfusion.
Ando M, Sunaguchi N, Wu Y, Do S, Sung Y, Louissaint A, Yuasa T, Ichihara S, Gupta R
Crystal analyser-based X-ray phase contrast imaging in the dark field: implementation and evaluation using excised tissue specimens. Eur Radiol. 2014 Feb; 24(2):423-33.
Crystal analyser-based X-ray phase contrast imaging in the dark field: implementation and evaluation using excised tissue specimens.
Lamb LR, Lehman CD, Do S, Kim K, Langarica S, Bahl M
Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT). AI Precis Oncol. 2024 Aug; 1(4):195-206.
Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT).
Alkhadrawi AM, Peña-Trujillo V, Gallo-Bernal S, Gee MS, Cobos CEJ, Kim K, Langarica S, Kim YT, Victoria T, Do S
Journal of imaging informatics in medicine
ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume. J Imaging Inform Med. 2026 Apr; 39(2):1202-1216.
ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume.
Pourjabbar S, Singh S, Singh AK, Johnston RP, Shenoy-Bhangle AS, Do S, Padole A, Blake MA, Persson A, Kalra MK
Preliminary results: prospective clinical study to assess image-based iterative reconstruction for abdominal computed tomography acquired at 2 radiation dose levels. J Comput Assist Tomogr. 2014 Jan-Feb; 38(1):117-22.
Preliminary results: prospective clinical study to assess image-based iterative reconstruction for abdominal computed tomography acquired at 2 radiation dose levels.
Synho Do; Sarabjeet Singh; Mannudeep K. Kalra; W. Clem Karl; Thomas J. Brady; Homer Pien
High-order noise analysis for low dose iterative image reconstruction methods: ASIR, IRIS, and MBAI
Proceeding of SPIE. High-order noise analysis for low dose iterative image reconstruction methods: ASIR, IRIS, and MBAI. 2011; 7961.
Synho Do; Zhuangli Liang; William Clem Karl; Thomas Brady; Homer Pien
A projection-driven pre-correction technique for iterative reconstruction of helical cone-beam cardiac CT images
A projection-driven pre-correction technique for iterative reconstruction of helical cone-beam cardiac CT images. 2008.
Synho Do; Mannudeep K. Kalra; Zhuangli Liang; W. Clem. Karl; Thomas J. Brady; Homer Pien
Noise properties of iterative reconstruction techniques in low-dose CT scans
Noise properties of iterative reconstruction techniques in low-dose CT scans. 2009.
Synho Do; W. Clem Karl; Mannudeep K. Kalra; Thomas J. Brady; Homer Pien
Clinical low dose CT image reconstruction using high-order total variation techniques
Clinical low dose CT image reconstruction using high-order total variation techniques. 2010.
Langarica S, Kim YT, Alkhadrawi A, Kim JB, Do S
MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping microbiome-disease associations. Brief Bioinform. 2025 Aug 31; 26(5).
MINERVA-microbiome network research and visualization atlas: a scalable knowledge graph for mapping microbiome-disease associations.
Khawaja RD, Singh S, Gilman M, Sharma A, Do S, Pourjabbar S, Padole A, Lira D, Brown K, Shepard JA, Kalra MK
Computed tomography (CT) of the chest at less than 1 mSv: an ongoing prospective clinical trial of chest CT at submillisievert radiation doses with iterative model image reconstruction and iDose4 technique. J Comput Assist Tomogr. 2014 Jul-Aug; 38(4):613-9.
Computed tomography (CT) of the chest at less than 1 mSv: an ongoing prospective clinical trial of chest CT at submillisievert radiation doses with iterative model image reconstruction and iDose4 technique.
Kong J, So M, Park H, Kim YT, Kim H, Pahk K, Kang SH, Do S, Lim DK, Lee CN, Kim JB
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Electroencephalography functional network for screening amyloid positivity in mild cognitive impairment: a cross-sectional study. Clin Neurophysiol. 2025 Dec; 180:2111374.
Electroencephalography functional network for screening amyloid positivity in mild cognitive impairment: a cross-sectional study.
Khawaja RD, Singh S, Lira D, Bippus R, Do S, Padole A, Pourjabbar S, Koehler T, Shepard JA, Kalra MK
Role of compressive sensing technique in dose reduction for chest computed tomography: a prospective blinded clinical study. J Comput Assist Tomogr. 2014 Sep-Oct; 38(5):760-7.
Role of compressive sensing technique in dose reduction for chest computed tomography: a prospective blinded clinical study.
Kim YT, Kim H, Bahl M, Lev MH, González RG, Gee MS, Do S
Defining operational safety in clinical artificial intelligence systems. NPJ Digit Med. 2026 Feb 20; 9(1).
Defining operational safety in clinical artificial intelligence systems.
Pourjabbar S, Singh S, Kulkarni N, Muse V, Digumarthy SR, Khawaja RD, Padole A, Do S, Kalra MK
Acta radiologica (Stockholm, Sweden : 1987)
Dose reduction for chest CT: comparison of two iterative reconstruction techniques. Acta Radiol. 2015 Jun; 56(6):688-95.
Dose reduction for chest CT: comparison of two iterative reconstruction techniques.
Correction: ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume. J Imaging Inform Med. 2025 Oct 20.
Correction: ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume.
Bahl M, Langarica S, Lamb LR, Kniss AS, Do S
AI to Reduce the Interval Cancer Rate of Screening Digital Breast Tomosynthesis. Radiology. 2025 07; 316(1):e241050.
AI to Reduce the Interval Cancer Rate of Screening Digital Breast Tomosynthesis.
Kim K, Bahl M, Alkhadrawi AM, Kim YT, Do S
Generative AI-Enhanced Microcalcification Detection in Full-Field Digital Mammography: Reducing False Positives with High Sensitivity. Res Sq. 2025 Nov 27.
Generative AI-Enhanced Microcalcification Detection in Full-Field Digital Mammography: Reducing False Positives with High Sensitivity.
Bahl M, Kim K, Kim H, Alkhadrawi A, Do S
Commercial Artificial Intelligence (AI) Tool for Screening Digital Breast Tomosynthesis: Factors Associated With AI-Based Breast Cancer Detection. AJR Am J Roentgenol. 2026 Apr 01; 1-13.
Commercial Artificial Intelligence (AI) Tool for Screening Digital Breast Tomosynthesis: Factors Associated With AI-Based Breast Cancer Detection.
Response to 'Methodological and statistical concerns in MINERVA microbiome-disease knowledge graph' by Salvatore Chirumbolo. Brief Bioinform. 2025 Nov 01; 26(6).
Response to 'Methodological and statistical concerns in MINERVA microbiome-disease knowledge graph' by Salvatore Chirumbolo.
Padole A, Singh S, Ackman JB, Wu C, Do S, Pourjabbar S, Khawaja RD, Otrakji A, Digumarthy S, Shepard JA, Kalra M
Submillisievert chest CT with filtered back projection and iterative reconstruction techniques. AJR Am J Roentgenol. 2014 Oct; 203(4):772-81.
Submillisievert chest CT with filtered back projection and iterative reconstruction techniques.
Do S, Karl WC, Singh S, Kalra M, Brady T, Shin E, Pien H
High fidelity system modeling for high quality image reconstruction in clinical CT. PLoS One. 2014; 9(11):e111625.
High fidelity system modeling for high quality image reconstruction in clinical CT.
Khawaja RDA, Singh S, Blake M, Harisinghani M, Choy G, Karosmangulu A, Padole A, Do S, Brown K, Thompson R, Morton T, Raihani N, Koehler T, Kalra MK
Ultra-low dose abdominal MDCT: using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study. Eur J Radiol. 2015 Jan; 84(1):2-10.
Ultra-low dose abdominal MDCT: using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study.
Abdominal imaging : computation and clinical applications : 5th International Workshop, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013 : proceedings. Abdominal Imaging (Workshop) (5th : 2013 : Nagoya-shi, Japan)
Computer-Aided Detection of Colorectal Lesions with Super-Resolution CT Colonography: Pilot Evaluation. Abdom Imaging (2013). 2013; 8198:73-80.
Computer-Aided Detection of Colorectal Lesions with Super-Resolution CT Colonography: Pilot Evaluation.
Padole A, Singh S, Lira D, Blake MA, Pourjabbar S, Khawaja RD, Choy G, Saini S, Do S, Kalra MK
Assessment of Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction for Reduced Dose Abdominal Computed Tomography. J Comput Assist Tomogr. 2015 Jul-Aug; 39(4):462-7.
Assessment of Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction for Reduced Dose Abdominal Computed Tomography.
Khawaja RD, Singh S, Blake M, Harisinghani M, Choy G, Karaosmanoglu A, Karosmanoglu A, Padole A, Pourjabbar S, Do S, Kalra MK
Ultralow-Dose Abdominal Computed Tomography: Comparison of 2 Iterative Reconstruction Techniques in a Prospective Clinical Study. J Comput Assist Tomogr. 2015 Jul-Aug; 39(4):489-98.
Ultralow-Dose Abdominal Computed Tomography: Comparison of 2 Iterative Reconstruction Techniques in a Prospective Clinical Study.
Puchner SB, Ferencik M, Maehara A, Stolzmann P, Ma S, Do S, Kauczor HU, Mintz GS, Hoffmann U, Schlett CL
Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound. AJR Am J Roentgenol. 2017 Apr; 208(4):777-784.
Iterative Image Reconstruction Improves the Accuracy of Automated Plaque Burden Assessment in Coronary CT Angiography: A Comparison With Intravascular Ultrasound.
Ruth L. Kirschstein National Research Service Award, T32
Lee H, Tajmir S, Lee J, Zissen M, Yeshiwas BA, Alkasab TK, Choy G, Do S
Fully Automated Deep Learning System for Bone Age Assessment. J Digit Imaging. 2017 Aug; 30(4):427-441.
Fully Automated Deep Learning System for Bone Age Assessment.
Jeong JW, Kim TS, Shin DC, Do S, Singh M, Marmarelis VZ
Soft tissue differentiation using multiband signatures of high resolution ultrasonic transmission tomography. IEEE Trans Med Imaging. 2005 Mar; 24(3):399-408.
Soft tissue differentiation using multiband signatures of high resolution ultrasonic transmission tomography.
Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography. IEEE Trans Med Imaging. 2006 Aug; 25(8):1068-78.
Segmentation methodology for automated classification and differentiation of soft tissues in multiband images of high-resolution ultrasonic transmission tomography.
Singh S, Kalra MK, Hsieh J, Licato PE, Do S, Pien HH, Blake MA
Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology. 2010 Nov; 257(2):373-83.
Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques.
Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, Do S
Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis. J Digit Imaging. 2017 Aug; 30(4):487-498.
Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis.
Jeong JW, Shin DC, Do SH, Blanco C, Klipfel NE, Holmes DR, Hovanessian-Larsen LJ, Marmarelis VZ
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Differentiation of cancerous lesions in excised human breast specimens using multiband attenuation profiles from ultrasonic transmission tomography. J Ultrasound Med. 2008 Mar; 27(3):435-51.
Differentiation of cancerous lesions in excised human breast specimens using multiband attenuation profiles from ultrasonic transmission tomography.
Lee H, Mansouri M, Tajmir S, Lev MH, Do S
A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection. J Digit Imaging. 2018 08; 31(4):393-402.
A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection.
Do S, Karl WC, Liang Z, Kalra M, Brady TJ, Pien HH
A decomposition-based CT reconstruction formulation for reducing blooming artifacts. Phys Med Biol. 2011 Nov 21; 56(22):7109-25.
A decomposition-based CT reconstruction formulation for reducing blooming artifacts.
Ghoshhajra BB, Engel LC, Major GP, Goehler A, Techasith T, Verdini D, Do S, Liu B, Li X, Sala M, Kim MS, Blankstein R, Prakash P, Sidhu MS, Corsini E, Banerji D, Wu D, Abbara S, Truong Q, Brady TJ, Hoffmann U, Kalra M
Evolution of coronary computed tomography radiation dose reduction at a tertiary referral center. Am J Med. 2012 Aug; 125(8):764-72.
Evolution of coronary computed tomography radiation dose reduction at a tertiary referral center.
Fuchs G, Chretien YR, Mario J, Do S, Eikermann M, Liu B, Yang K, Fintelmann FJ
Quantifying the effect of slice thickness, intravenous contrast and tube current on muscle segmentation: Implications for body composition analysis. Eur Radiol. 2018 Jun; 28(6):2455-2463.
Quantifying the effect of slice thickness, intravenous contrast and tube current on muscle segmentation: Implications for body composition analysis.
Prakash P, Kalra MK, Ackman JB, Digumarthy SR, Hsieh J, Do S, Shepard JA, Gilman MD
Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique. Radiology. 2010 Jul; 256(1):261-9.
Diffuse lung disease: CT of the chest with adaptive statistical iterative reconstruction technique.
Khawaja RDA, Singh S, Blake M, Harisinghani M, Choy G, Karaosmanoglu A, Padole A, Do S, Brown K, Thompson R, Morton T, Raihani N, Koehler T, Kalra MK
Corrigendum to "Ultra-low dose abdominal MDCT: Using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study" [Eur. J. Radiol. 84 (2015) 2-10]. Eur J Radiol. 2016 Jan; 85(1):310.
Corrigendum to "Ultra-low dose abdominal MDCT: Using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study" [Eur. J. Radiol. 84 (2015) 2-10].
Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. J Am Coll Radiol. 2018 03; 15(3 Pt B):504-508.
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.