Parametric imaging of ultrasonic backscatter of fixed sheep brain

2020 ◽  
Vol 148 (4) ◽  
pp. 2774-2774
Author(s):  
Cecille Labuda ◽  
Will R. Newman ◽  
Brent K. Hoffmeister
Keyword(s):  
Parametric Imaging ◽  
Sheep Brain ◽  
Ultrasonic Backscatter

Cancellous bone characterization using ultrasonic backscatter parametric imaging

2018 ◽  
Vol 144 (3) ◽  
pp. 1822-1822 ◽  
Author(s):  
Dean Ta ◽  
Ying Li ◽  
Boyi Li ◽  
Rui Zheng ◽  
Lawrence H. Le
Keyword(s):  
Cancellous Bone ◽  
Parametric Imaging ◽  
Ultrasonic Backscatter

The Ability of Ultrasonic Backscatter Parametric Imaging to Characterize Bovine Trabecular Bone

2019 ◽  
Vol 41 (5) ◽  
pp. 271-289 ◽  
Author(s):  
Ying Li ◽  
Boyi Li ◽  
Yifang Li ◽  
Chengcheng Liu ◽  
Feng Xu ◽  
...  
Keyword(s):  
Bone Density ◽  
Trabecular Bone ◽  
Region Of Interest ◽  
Bone Microstructure ◽  
Parametric Imaging ◽  
Trabecular Bone Microstructure ◽  
Step Interval ◽  
Trabecular Bone Mass ◽  
Centroid Shift ◽  
Ultrasonic Backscatter

The ultrasonic backscatter technique holds the promise of characterizing bone density and microstructure. This paper conducts ultrasonic backscatter parametric imaging based on measurements of apparent integrated backscatter (AIB), spectral centroid shift (SCS), frequency slope of apparent backscatter (FSAB), and frequency intercept of apparent backscatter (FIAB) for representing trabecular bone mass and microstructure. We scanned 33 bovine trabecular bone samples using a 7.5 MHz focused transducer in a 20 mm × 20 mm region of interest (ROI) with a step interval of 0.05 mm. Images based on the ultrasonic backscatter parameters (i.e., AIB, SCS, FSAB, and FIAB) were constructed to compare with photographic images of the specimens as well as two-dimensional (2D) μ-CT images from approximately the same depth and location of the specimen. Similar structures and trabecular alignments can be observed among these images. Statistical analyses demonstrated that the means and standard deviations of the ultrasonic backscatter parameters exhibited significant correlations with bone density (|R| = 0.45-0.78, p < 0.01) and bone microstructure (|R| = 0.44-0.87, p < 0.001). Some bovine trabecular bone microstructure parameters were independently associated with the ultrasonic backscatter parameters (Δ R2 = 4.18%-44.45%, p < 0.05) after adjustment for bone apparent density (BAD). The results show that ultrasonic backscatter parametric imaging can provide a direct view of the trabecular microstructure and can reflect information about the density and microstructure of trabecular bone.

Parametric imaging of [11C]PIB studies using spectral analysis

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S590-S590 ◽  
Author(s):  
Rainer Hinz ◽  
Gunnar Blomquist ◽  
Paul Edison ◽  
David J Brooks
Keyword(s):  
Spectral Analysis ◽  
Parametric Imaging

Time - resolved flavin fluorescence of pyridoxine-5-P oxidase from sheep brain

1987 ◽  
pp. 217-224
Author(s):  
Antonie J.W. Visser ◽  
Francis Kwok ◽  
Jorge E. Churchich
Keyword(s):  
Time Resolved ◽  
Sheep Brain

scholarly journals Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamidreza Taleghamar ◽  
Hadi Moghadas-Dastjerdi ◽  
Gregory J. Czarnota ◽  
Ali Sadeghi-Naini
Keyword(s):  
Breast Cancer ◽  
Treatment Response ◽  
Quantitative Ultrasound ◽  
Parametric Imaging ◽  
Term Survival ◽  
Adaptive Boosting ◽  
Tumor Margin ◽  
Parametric Images ◽  
Response To Chemotherapy ◽  
Pre Treatment

AbstractThe efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

Grain size evaluation with time-frequency ultrasonic backscatter

2021 ◽  
Vol 117 ◽  
pp. 102369
Author(s):  
Yangguang Bu ◽  
Xiling Liu ◽  
Joseph A. Turner ◽  
Yongfeng Song ◽  
Xiongbing Li
Keyword(s):  
Grain Size ◽  
Time Frequency ◽  
Ultrasonic Backscatter ◽  
Size Evaluation
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