scholarly journals Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
F. Steinbruecker ◽  
A. Meyer-Baese ◽  
T. Schlossbauer ◽  
D. Cremers
Keyword(s):  
Breast Cancer ◽  
Motion Compensation ◽  
Rigid Motion ◽  
Breast Mri ◽  
Lesion Detection ◽  
Nonrigid Motion ◽  
Small Lesion ◽  
Spatiotemporal Features ◽  
Optical Flow Method ◽  
Compensation Technique

Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

Automated breast cancer lesion detection on breast MRI using artificial intelligence.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14612-e14612 ◽  
Author(s):  
Nataly Tapia Negrete ◽  
Ruquaiyah Takhtawala ◽  
Madeleine Shaver ◽  
Turkay Kart ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Breast Tumor ◽  
Breast Mri ◽  
Lesion Detection ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Data Set ◽  
Post Contrast

e14612 Background: Over 40,000 women in the US will die from breast cancer. Early detection of cancer is crucial and is a potential avenue to improve survival. The objective of this research study is to develop a convoluted neural network (CNN), a subset of artificial intelligence, in order to enhance computerized detection of breast lesions on MRIs. Methods: This is an institutional review board approved retrospective study with post contrast MRI data from 238 patients. Breast tumor segmentation was automated with a hybrid 3D/2D CNN designed adapted from U-net, a popular neural network architecture in biomedical image analysis. T1 post-contrast MRI volumes were used to train the network. The data set was separated into training (80%) and validation (20%) sets. Re-sampling and normalization using z-scores were applied to each volume before training. Contracting and expanding arms of the model consist of successive convolutions followed by batch normalization and ReLU operations. Ground truth was established through manual segmentation and previously conducted readings of the images used to train our network. Results: A 5-fold cross validation was performed for analysis. The Dice similarity coefficient was used to assess segmentation accuracy. The hybrid 3D/2D U-Net architecture yielded a Dice score of 0.753 and a Pearson correlation of 0.548 for the breast tumor segmentation. Conclusions: These results demonstrated the feasibility for artificial intelligence applications in accurately identifying the presence of lesions on breast MRI images.

Application and evaluation of a motion compensation technique to breast MRI

2009 ◽  
Author(s):  
Frank Steinbrücker ◽  
Anke Meyer-Bäse ◽  
Axel Wismüller ◽  
Thomas Schlossbauer
Keyword(s):  
Motion Compensation ◽  
Breast Mri ◽  
Compensation Technique

Evaluation of a known breast cancer using an abbreviated breast MRI protocol: Correlation of imaging characteristics and pathology with lesion detection and conspicuity

2016 ◽  
Vol 85 (4) ◽  
pp. 815-823 ◽  
Author(s):  
Laura Heacock ◽  
Amy N. Melsaether ◽  
Samantha L. Heller ◽  
Yiming Gao ◽  
Kristine M. Pysarenko ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Mri ◽  
Lesion Detection ◽  
Imaging Characteristics ◽  
Mri Protocol ◽  
Abbreviated Breast Mri

First experience with MRI-guided vacuum aspirated breast biopsy

2020 ◽  
pp. 25-31
Author(s):  
M. L. Mazo ◽  
O. E. Jacobs ◽  
O. S. Puchkova ◽  
M. V. Feldsherov ◽  
E. V. Kondratyev
Keyword(s):  
Breast Cancer ◽  
Breast Augmentation ◽  
Breast Biopsy ◽  
Breast Mri ◽  
Breast Tumors ◽  
High Tech ◽  
X Ray ◽  
Vacuum Aspiration ◽  
Mri Guided ◽  
Aesthetic Breast

The rate of detection of breast cancer by MRI, while other methods of radiological diagnosis are not sufficiently informative, ranges from 5.2 to 26.3 per cent. Suspicious breast tumors of category BI-RADS 4, 5 show morphological image-guided biopsy verification, in particular MRI with contrast. Purpose. To show the possibilities and features of carrying out MRI-guided vacuum breast biopsy, including after aesthetic breast augmentation. Material and methods. A comprehensive X-ray, ultrasound and MRI examination of 54 women aged between 28 and 70 years with different breast tumors was conducted. Of these, five were detected only by breast MRI with contrast, and were morphologically verified by MRI-guided vacuum aspiration biopsy. Results. 14 of the 54 patients with breast mass were diagnosed with breast cancer and 26 were diagnosed with benign diseases. The effectiveness of comprehensive examination and low-invasive high-tech MRI-guided procedures in early refined screening for breast cancer, including after aesthetic breast augmentation, has been demonstrated. MRI-guided vacuum-assisted breast biopsy is a fast, safe and accurate diagnostic method of morphological verification of suspicious breast tumors that do not have X-ray and ultrasound.

Susceptibility artifact at breast MRI due to retained fragment of localization hook‐wire hiding breast cancer

2021 ◽  
Author(s):  
Almir Galvão Vieira Bitencourt ◽  
Vinicius Cardona Felipe ◽  
Mauricio Doi ◽  
Luciana Graziano
Keyword(s):  
Breast Cancer ◽  
Breast Mri ◽  
Susceptibility Artifact ◽  
Hook Wire

scholarly journals Diffusion tensor imaging on 3-T MRI breast: diagnostic performance in comparison to diffusion-weighted imaging

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Dalia Abdelhady ◽  
Amany Abdelbary ◽  
Ahmed H. Afifi ◽  
Alaa-eldin Abdelhamid ◽  
Hebatallah H. M. Hassan
Keyword(s):  
Breast Cancer ◽  
Diffusion Tensor Imaging ◽  
Sensitivity And Specificity ◽  
Diffusion Weighted Imaging ◽  
Diffusion Tensor ◽  
Breast Mri ◽  
Breast Lesions ◽  
Dce Mri ◽  
Additional Tool ◽  
Diffusion Weighted

Abstract Background Breast cancer is the most prevalent cancer among females. Dynamic contrast-enhanced MRI (DCE-MRI) breast is highly sensitive (90%) in the detection of breast cancer. Despite its high sensitivity in detecting breast cancer, its specificity (72%) is moderate. Owing to 3-T breast MRI which has the advantage of a higher signal to noise ratio and shorter scanning time rather than the 1.5-T MRI, the adding of new techniques as diffusion tensor imaging (DTI) to breast MRI became more feasible. Diffusion-weighted imaging (DWI) which tracks the diffusion of the tissue water molecule as well as providing data about the integrity of the cell membrane has been used as a valuable additional tool of DCE-MRI to increase its specificity. Based on DWI, more details about the microstructure could be detected using diffusion tensor imaging. The DTI applies diffusion in many directions so apparent diffusion coefficient (ADC) will vary according to the measured direction raising its sensitivity to microstructure elements and cellular density. This study aimed to investigate the diagnostic accuracy of DTI in the assessment of breast lesions in comparison to DWI. Results By analyzing the data of the 50 cases (31 malignant cases and 19 benign cases), the sensitivity and specificity of DWI in differentiation between benign and malignant lesions were about 90% and 63% respectively with PPV 90% and NPV 62%, while the DTI showed lower sensitivity and specificity about 81% and 51.7%, respectively, with PPV 78.9% and NPV 54.8% (P-value ≤ 0.05). Conclusion While the DWI is still the most established diffusion parameter, DTI may be helpful in the further characterization of tumor microstructure and differentiation between benign and malignant breast lesions.

scholarly journals Fat-enlarged axillary lymph nodes are associated with node-positive breast cancer in obese patients

2021 ◽  
Author(s):  
Roberta M. diFlorio-Alexander ◽  
Qingyuan Song ◽  
Dennis Dwan ◽  
Judith A. Austin-Strohbehn ◽  
Kristen E. Muller ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Nodes ◽  
Invasive Breast Cancer ◽  
Breast Mri ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Obese Women ◽  
Axillary Metastases ◽  
Node Positive ◽  
Positive Breast Cancer

Abstract Purpose Obesity associated fat infiltration of organ systems is accompanied by organ dysfunction and poor cancer outcomes. Obese women demonstrate variable degrees of fat infiltration of axillary lymph nodes (LNs), and they are at increased risk for node-positive breast cancer. However, the relationship between enlarged axillary nodes and axillary metastases has not been investigated. The purpose of this study is to evaluate the association between axillary metastases and fat-enlarged axillary nodes visualized on mammograms and breast MRI in obese women with a diagnosis of invasive breast cancer. Methods This retrospective case–control study included 431 patients with histologically confirmed invasive breast cancer. The primary analysis of this study included 306 patients with pre-treatment and pre-operative breast MRI and body mass index (BMI) > 30 (201 node-positive cases and 105 randomly selected node-negative controls) diagnosed with invasive breast cancer between April 1, 2011, and March 1, 2020. The largest visible LN was measured in the axilla contralateral to the known breast cancer on breast MRI. Multivariate logistic regression models were used to assess the association between node-positive status and LN size adjusting for age, BMI, tumor size, tumor grade, tumor subtype, and lymphovascular invasion. Results A strong likelihood of node-positive breast cancer was observed among obese women with fat-expanded lymph nodes (adjusted OR for the 4th vs. 1st quartile for contralateral LN size on MRI: 9.70; 95% CI 4.26, 23.50; p < 0.001). The receiver operating characteristic curve for size of fat-enlarged nodes in the contralateral axilla identified on breast MRI had an area under the curve of 0.72 for predicting axillary metastasis, and this increased to 0.77 when combined with patient and tumor characteristics. Conclusion Fat expansion of axillary lymph nodes was associated with a high likelihood of axillary metastases in obese women with invasive breast cancer independent of BMI and tumor characteristics.

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