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Showing 1 to 7 of 7 entries
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SU-E-I-110: Minimized Pediatric Dose in Direct Radiography (DR).

Medical physics

So J, Nickoloff E, Dutta A, Jambawalikar S.
PMID: 28517622
Med Phys. 2012 Jun;39(6):3650. doi: 10.1118/1.4734827.

PURPOSE: Pediatric x-rays techniques are not standardized. They depend upon: patient size, anatomical localized and equipment manufacturers. Most pediatric techniques use the default factory settings. This project's goal is to find the best compromise between dose to pediatric patients...

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So J, Nickoloff E, Dutta A, et al. SU-E-I-110: Minimized Pediatric Dose in Direct Radiography (DR). Med Phys. 2012;39(6):3650doi: 10.1118/1.4734827.
So, J., Nickoloff, E., Dutta, A., & Jambawalikar, S. (2012). SU-E-I-110: Minimized Pediatric Dose in Direct Radiography (DR). Medical physics, 39(6), 3650. https://doi.org/10.1118/1.4734827
So, J, et al. "SU-E-I-110: Minimized Pediatric Dose in Direct Radiography (DR)." Medical physics vol. 39,6 (2012): 3650. doi: https://doi.org/10.1118/1.4734827
So J, Nickoloff E, Dutta A, Jambawalikar S. SU-E-I-110: Minimized Pediatric Dose in Direct Radiography (DR). Med Phys. 2012 Jun;39(6):3650. doi: 10.1118/1.4734827. PMID: 28517622.
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Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data.

AJR. American journal of roentgenology

Ha R, Mutasa S, Sant EPV, Karcich J, Chin C, Liu MZ, Jambawalikar S.
PMID: 30860901
AJR Am J Roentgenol. 2019 Mar 12;1-6. doi: 10.2214/AJR.18.20250. Epub 2019 Mar 12.

OBJECTIVE: The purpose of this study was to test the hypothesis that convolutional neural networks can be used to predict which patients with pure atypical ductal hyperplasia (ADH) may be safely monitored rather than undergo surgery.MATERIALS AND METHODS: A...

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Ha R, Mutasa S, Sant EPV, et al. Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data. AJR Am J Roentgenol. 2019;1-6doi: 10.2214/AJR.18.20250.
Ha, R., Mutasa, S., Sant, E. P. V., Karcich, J., Chin, C., Liu, M. Z., & Jambawalikar, S. (2019). Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data. AJR. American journal of roentgenology, 1-6. https://doi.org/10.2214/AJR.18.20250
Ha, Richard, et al. "Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data." AJR. American journal of roentgenology vol. (2019): 1-6. doi: https://doi.org/10.2214/AJR.18.20250
Ha R, Mutasa S, Sant EPV, Karcich J, Chin C, Liu MZ, Jambawalikar S. Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data. AJR Am J Roentgenol. 2019 Mar 12;1-6. doi: 10.2214/AJR.18.20250. Epub 2019 Mar 12. PMID: 30860901; PMCID: PMC8111785.
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Weakly Supervised Deep Learning Approach to Breast MRI Assessment.

Academic radiology

Liu MZ, Swintelski C, Sun S, Siddique M, Desperito E, Jambawalikar S, Ha R.
PMID: 34108114
Acad Radiol. 2021 Jun 06; doi: 10.1016/j.acra.2021.03.032. Epub 2021 Jun 06.

RATIONALE AND OBJECTIVES: To evaluate a weakly supervised deep learning approach to breast Magnetic Resonance Imaging (MRI) assessment without pixel level segmentation in order to improve the specificity of breast MRI lesion classification.MATERIALS AND METHODS: In this IRB approved...

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Liu MZ, Swintelski C, Sun S, et al. Weakly Supervised Deep Learning Approach to Breast MRI Assessment. Acad Radiol. 2021;doi: 10.1016/j.acra.2021.03.032.
Liu, M. Z., Swintelski, C., Sun, S., Siddique, M., Desperito, E., Jambawalikar, S., & Ha, R. (2021). Weakly Supervised Deep Learning Approach to Breast MRI Assessment. Academic radiology, . https://doi.org/10.1016/j.acra.2021.03.032
Liu, Michael Z, et al. "Weakly Supervised Deep Learning Approach to Breast MRI Assessment." Academic radiology vol. (2021). doi: https://doi.org/10.1016/j.acra.2021.03.032
Liu MZ, Swintelski C, Sun S, Siddique M, Desperito E, Jambawalikar S, Ha R. Weakly Supervised Deep Learning Approach to Breast MRI Assessment. Acad Radiol. 2021 Jun 06; doi: 10.1016/j.acra.2021.03.032. Epub 2021 Jun 06. PMID: 34108114.
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SU-E-I-71: Susceptibility Weighted Imaging (SWI) Software for Post-Processing of SWI Data.

Medical physics

Jambawalikar S, Krishnamoorthy S, So J, Dutta A, Li H, Button T, Nickoloff E.
PMID: 28517658
Med Phys. 2012 Jun;39(6):3641. doi: 10.1118/1.4734788.

PURPOSE: To develop open source software for post processing of susceptibility weighted (SWI) MR images using magnitude and phase data.METHODS: SWI data was acquired using Philips MRI 3T scanner with the following parameter: 3D T1 FFE axial with TR=40ms,...

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Jambawalikar S, Krishnamoorthy S, So J, et al. SU-E-I-71: Susceptibility Weighted Imaging (SWI) Software for Post-Processing of SWI Data. Med Phys. 2012;39(6):3641doi: 10.1118/1.4734788.
Jambawalikar, S., Krishnamoorthy, S., So, J., Dutta, A., Li, H., Button, T., & Nickoloff, E. (2012). SU-E-I-71: Susceptibility Weighted Imaging (SWI) Software for Post-Processing of SWI Data. Medical physics, 39(6), 3641. https://doi.org/10.1118/1.4734788
Jambawalikar, S, et al. "SU-E-I-71: Susceptibility Weighted Imaging (SWI) Software for Post-Processing of SWI Data." Medical physics vol. 39,6 (2012): 3641. doi: https://doi.org/10.1118/1.4734788
Jambawalikar S, Krishnamoorthy S, So J, Dutta A, Li H, Button T, Nickoloff E. SU-E-I-71: Susceptibility Weighted Imaging (SWI) Software for Post-Processing of SWI Data. Med Phys. 2012 Jun;39(6):3641. doi: 10.1118/1.4734788. PMID: 28517658.
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Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

Radiology. Artificial intelligence

Stember JN, Celik H, Gutman D, Swinburne N, Young R, Eskreis-Winkler S, Holodny A, Jambawalikar S, Wood BJ, Chang PD, Krupinski E, Bagci U.
PMID: 33842890
Radiol Artif Intell. 2020 Nov 11;3(1):e200047. doi: 10.1148/ryai.2020200047. eCollection 2021 Jan.

PURPOSE: To generate and assess an algorithm combining eye tracking and speech recognition to extract brain lesion location labels automatically for deep learning (DL).MATERIALS AND METHODS: In this retrospective study, 700 two-dimensional brain tumor MRI scans from the Brain...

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Stember JN, Celik H, Gutman D, et al. Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle. Radiol Artif Intell. 2020;3(1):e200047doi: 10.1148/ryai.2020200047.
Stember, J. N., Celik, H., Gutman, D., Swinburne, N., Young, R., Eskreis-Winkler, S., Holodny, A., Jambawalikar, S., Wood, B. J., Chang, P. D., Krupinski, E., & Bagci, U. (2021). Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle. Radiology. Artificial intelligence, 3(1), e200047. https://doi.org/10.1148/ryai.2020200047
Stember, Joseph N, et al. "Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle." Radiology. Artificial intelligence vol. 3,1 (2021): e200047. doi: https://doi.org/10.1148/ryai.2020200047
Stember JN, Celik H, Gutman D, Swinburne N, Young R, Eskreis-Winkler S, Holodny A, Jambawalikar S, Wood BJ, Chang PD, Krupinski E, Bagci U. Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle. Radiol Artif Intell. 2020 Nov 11;3(1):e200047. doi: 10.1148/ryai.2020200047. eCollection 2021 Jan. PMID: 33842890; PMCID: PMC7845782.
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Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study.

Acta radiologica (Stockholm, Sweden : 1987)

Wong TT, Quarterman P, Lynch TS, Rasiej MJ, Jaramillo D, Jambawalikar SR.
PMID: 33926266
Acta Radiol. 2021 Apr 29;2841851211011563. doi: 10.1177/02841851211011563. Epub 2021 Apr 29.

BACKGROUND: Ultrashort echo time (UTE) T2* is sensitive to molecular changes within the deep calcified layer of cartilage. Feasibility of its use in the hip needs to be established to determine suitability for clinical use.PURPOSE: To establish feasibility of...

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Wong TT, Quarterman P, Lynch TS, et al. Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study. Acta Radiol. 2021;2841851211011563doi: 10.1177/02841851211011563.
Wong, T. T., Quarterman, P., Lynch, T. S., Rasiej, M. J., Jaramillo, D., & Jambawalikar, S. R. (2021). Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study. Acta radiologica (Stockholm, Sweden : 1987), 2841851211011563. https://doi.org/10.1177/02841851211011563
Wong, Tony T, et al. "Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study." Acta radiologica (Stockholm, Sweden : 1987) vol. (2021): 2841851211011563. doi: https://doi.org/10.1177/02841851211011563
Wong TT, Quarterman P, Lynch TS, Rasiej MJ, Jaramillo D, Jambawalikar SR. Feasibility of ultrashort echo time (UTE) T2* cartilage mapping in the hip: a pilot study. Acta Radiol. 2021 Apr 29;2841851211011563. doi: 10.1177/02841851211011563. Epub 2021 Apr 29. PMID: 33926266.
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The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset.

Radiology. Artificial intelligence

Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS.
PMID: 34235438
Radiol Artif Intell. 2021 Feb 10;3(3):e200078. doi: 10.1148/ryai.2021200078. eCollection 2021 May.

PURPOSE: To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.MATERIALS AND METHODS: A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients...

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Desai AD, Caliva F, Iriondo C, et al. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell. 2021;3(3):e200078doi: 10.1148/ryai.2021200078.
Desai, A. D., Caliva, F., Iriondo, C., Mortazi, A., Jambawalikar, S., Bagci, U., Perslev, M., Igel, C., Dam, E. B., Gaj, S., Yang, M., Li, X., Deniz, C. M., Juras, V., Regatte, R., Gold, G. E., Hargreaves, B. A., Pedoia, V., Chaudhari, A. S. (2021). The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiology. Artificial intelligence, 3(3), e200078. https://doi.org/10.1148/ryai.2021200078
Desai, Arjun D, et al. "The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset." Radiology. Artificial intelligence vol. 3,3 (2021): e200078. doi: https://doi.org/10.1148/ryai.2021200078
Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell. 2021 Feb 10;3(3):e200078. doi: 10.1148/ryai.2021200078. eCollection 2021 May. PMID: 34235438; PMCID: PMC8231759.
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Showing 1 to 7 of 7 entries