scholarly journals New parameters of ultrafast dynamic contrast‐enhanced breast MRI using compressed sensing

2019 ◽  
Vol 51 (1) ◽  
pp. 164-174 ◽  
Author(s):  
Maya Honda ◽  
Masako Kataoka ◽  
Natsuko Onishi ◽  
Mami Iima ◽  
Akane Ohashi ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Breast Mri ◽  
Dynamic Contrast Enhanced ◽  
Ultrafast Dynamic ◽  
Contrast Enhanced

scholarly journals Impact of the Number of Iterations in Compressed Sensing Reconstruction on Ultrafast Dynamic Contrast-enhanced Breast MR Imaging

2019 ◽  
Vol 18 (3) ◽  
pp. 200-207 ◽  
Author(s):  
Hajime Sagawa ◽  
Masako Kataoka ◽  
Shotaro Kanao ◽  
Natsuko Onishi ◽  
Marcel Dominik Nickel ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Compressed Sensing ◽  
Dynamic Contrast Enhanced ◽  
Ultrafast Dynamic ◽  
Contrast Enhanced ◽  
Breast Mr ◽  
Number Of Iterations

Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study

2018 ◽  
Author(s):  
Maryellen L. Giger ◽  
Karen Drukker ◽  
Rachel Anderson ◽  
Fred Pineda ◽  
Alexandra Edwards ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Pilot Study ◽  
Breast Mri ◽  
Dynamic Contrast Enhanced ◽  
Ultrafast Dynamic ◽  
Contrast Enhanced

scholarly journals Differentiation between subcentimeter carcinomas and benign lesions using kinetic parameters derived from ultrafast dynamic contrast-enhanced breast MRI

2019 ◽  
Vol 30 (2) ◽  
pp. 756-766 ◽  
Author(s):  
Natsuko Onishi ◽  
Meredith Sadinski ◽  
Peter Gibbs ◽  
Katherine M. Gallagher ◽  
Mary C. Hughes ◽  
...  
Keyword(s):  
Kinetic Parameters ◽  
Breast Mri ◽  
Benign Lesions ◽  
Dynamic Contrast Enhanced ◽  
Ultrafast Dynamic ◽  
Contrast Enhanced

scholarly journals Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Natsuko Onishi ◽  
Meredith Sadinski ◽  
Mary C. Hughes ◽  
Eun Sook Ko ◽  
Peter Gibbs ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Mri ◽  
Dynamic Contrast Enhanced ◽  
Ultrafast Dynamic ◽  
Contrast Enhanced ◽  
Imaging Markers

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Variable spatiotemporal resolution three-dimensional dixon sequence for rapid dynamic contrast-enhanced breast MRI

2014 ◽  
Vol 40 (6) ◽  
pp. spcone-spcone
Author(s):  
Manojkumar Saranathan ◽  
Dan W. Rettmann ◽  
Brian A. Hargreaves ◽  
Jafi A. Lipson ◽  
Bruce L. Daniel
Keyword(s):  
Three Dimensional ◽  
Breast Mri ◽  
Spatiotemporal Resolution ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced
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