Search research articles
Contact Us
Filters
Showing results (1-10 of 12) with videos related to
Page
of 2
Sort By:
Ultrasonic Imaging
|
May 4, 2005
Clinical evaluation of combined spatial compounding and adaptive imaging in breast tissue
Jeremy J Dahl, Mary S Soo, Gregg E Trahey
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|
November 16, 2005
Spatial and temporal aberrator stability for real-time adaptive imaging
Jeremy J Dahl, Mary S Soo, Gregg E Trahey
AJR. American Journal of Roentgenology
|
March 25, 2005
BI-RADS for sonography: positive and negative predictive values of sonographic features
Andrea S Hong, Eric L Rosen, Mary S Soo, et al.
Academic Radiology
|
March 31, 2012
Suspicious breast lesions detected at 3.0 T magnetic resonance imaging: clinical and histological outcomes
Karen S Johnson, Jay A Baker, Sheila S Lee, et al.
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|
October 14, 2006
Adaptive imaging on a diagnostic ultrasound scanner at quasi real-time rates
Jeremy J Dahl, Stephen A McAleavey, Gianmarco F Pinton, et al.
Radiology
|
October 18, 2014
Can breast cancer molecular subtype help to select patients for preoperative MR imaging?
Lars J Grimm, Karen S Johnson, P Kelly Marcom, et al.
Academic Radiology
|
July 9, 2015
Abbreviated screening protocol for breast MRI: a feasibility study
Lars J Grimm, Mary S Soo, Sora Yoon, et al.
The Breast Journal
|
March 16, 2017
Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype
Lars J Grimm, Jing Zhang, Jay A Baker, et al.
Academic Radiology
|
February 10, 2009
The influence of increased ambient lighting on mass detection in mammograms
Benjamin J Pollard, Ehsan Samei, Amarpreet S Chawla, et al.
Journal of Magnetic Resonance Imaging : JMRI
|
January 17, 2019
Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI
Ashirbani Saha, Lars J Grimm, Sujata V Ghate, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 12) with videos related to
Sort By:
Page
of 2
Ultrasonic Imaging
|
May 4, 2005
Clinical evaluation of combined spatial compounding and adaptive imaging in breast tissue
Jeremy J Dahl, Mary S Soo, Gregg E Trahey
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|
November 16, 2005
Spatial and temporal aberrator stability for real-time adaptive imaging
Jeremy J Dahl, Mary S Soo, Gregg E Trahey
AJR. American Journal of Roentgenology
|
March 25, 2005
BI-RADS for sonography: positive and negative predictive values of sonographic features
Andrea S Hong, Eric L Rosen, Mary S Soo, et al.
Academic Radiology
|
March 31, 2012
Suspicious breast lesions detected at 3.0 T magnetic resonance imaging: clinical and histological outcomes
Karen S Johnson, Jay A Baker, Sheila S Lee, et al.
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|
October 14, 2006
Adaptive imaging on a diagnostic ultrasound scanner at quasi real-time rates
Jeremy J Dahl, Stephen A McAleavey, Gianmarco F Pinton, et al.
Radiology
|
October 18, 2014
Can breast cancer molecular subtype help to select patients for preoperative MR imaging?
Lars J Grimm, Karen S Johnson, P Kelly Marcom, et al.
Academic Radiology
|
July 9, 2015
Abbreviated screening protocol for breast MRI: a feasibility study
Lars J Grimm, Mary S Soo, Sora Yoon, et al.
The Breast Journal
|
March 16, 2017
Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype
Lars J Grimm, Jing Zhang, Jay A Baker, et al.
Academic Radiology
|
February 10, 2009
The influence of increased ambient lighting on mass detection in mammograms
Benjamin J Pollard, Ehsan Samei, Amarpreet S Chawla, et al.
Journal of Magnetic Resonance Imaging : JMRI
|
January 17, 2019
Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI
Ashirbani Saha, Lars J Grimm, Sujata V Ghate, et al.
Page
of 2