A Finite Element Model of the Breast for Predicting Mechanical Deformations during Biopsy Procedures

Currently, High Field (1.5T) Superconducting MR image-guided needle breast procedures allow the physician only to calculate approximately the location and extent of a cancerous tumor in the compressed patient breast before inserting the needle. It can then become relatively uncertain that the tissue specimen removed during the biopsy actually belongs to the lesion of interest. A new method for guiding clinical breast biopsy is presented, based on a deformable finite element model of the breast. The geometry of the model is constructed from MR data, and its mechanical properties are modeled using a non-linear material model. This method allows imaging the breast without compression before the procedure, then compressing the breast and using the finite element model to predict the tumor’s position during the procedure

Simultaneous Estimation and Segmentation of T1 Map for Breast Parenchyma Measurement

Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T1 map estimation (E-Step) and the step of T1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T1 map can be noisy due to the complexity of T1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement

Methods for Predicting Mechanical Deformations in the Breast During Clinical Breast Biopsy

A new method for clinical breast biopsy is presented, based on a deformable finite element model of the breast. The geometry of the model is constructed from MR data, and its mechanical properties are based on a nonlinear material model. This method allows imaging the breast without compression before the procedure, then compressing the breast and using the finite element model to predict the tumor\u27s position

Methods for Modeling and Predicting Mechanical Deformations of the Breast During Interventional Procedures

When doing high field (1.5T) magnetic resonance breast imaging, the use of compression plate during imaging after a contrast-agent injection may critically change the enhancement characteristics of the tumor, making the tracking of its boundaries very difficult. A new method for clinical breast biopsy is presented based on a deformable finite element model of the breast. The geometry of the model is constructed from MR data, and its mechanical properties are based on a non-linear material model. This method allows imaging the breast without compression before the procedure, then compressing the breast and using the finite element model to predict the tumor’s position. The axial breast contours and the segmented slices are ported to a custom-written MR-image contour analysis program, which generates a finite element model (FEM) input file readable by a commercial FEM software. A deformable silicon gel phantom was built to study the movements of an inclusion inside a deformable environment. The hyperelastic properties of the phantom materials were evaluated on an Instron Model 1331 mechanical testing machine. The phantom was placed in a custom-built pressure device, where a pressure plate caused a 14% (9.8mm) compression. The phantom was imaged in a 1.5T magnet (axial and coronal), in the undeformed and deformed states. An FEM of the phantom was built using the custom-written software from the MR data, and another FEM of the phantom was built using a commercial pre-processor from the phantom’s directly measured dimensions. The displacements of the inclusion center and its boundaries were calculated, both from the experimental and FEM results. The calculated displacements from both models are within 0.5mm of each other, and agree within 1.0mm with the experimental results. This difference is within the imaging error

Conditional activation of Neu in the mammary epithelium of transgenic mice results in reversible pulmonary metastasis

AbstractTo determine the impact of tumor progression on the reversibility of Neu-induced tumorigenesis, we have used the tetracycline regulatory system to conditionally express activated Neu in the mammary epithelium of transgenic mice. When induced with doxycycline, bitransgenic MMTV-rtTA/TetO-NeuNT mice develop multiple invasive mammary carcinomas, essentially all of which regress to a clinically undetectable state following transgene deinduction. This demonstrates that Neu-initiated tumorigenesis is reversible. Strikingly, extensive lung metastases arising from Neu-induced mammary tumors also rapidly and fully regress following the abrogation of Neu expression. However, despite the near universal dependence of both primary tumors and metastases on Neu transgene expression, most animals bearing fully regressed Neu-induced tumors ultimately develop recurrent tumors that have progressed to a Neu-independent state

STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis: MR-based breast tumor diagnosis

The authors propose a spatiotemporal enhancement pattern (STEP) for comprehensive characterization of breast tumors in contrast-enhanced MR images. By viewing serial contrast-enhanced MR images as a single spatiotemporal image, they formulate the STEP as a combination of (1) dynamic enhancement and architectural features of a tumor, and (2) the spatial variations of pixelwise temporal enhancements. Although the latter has been widely used by radiologists for diagnostic purposes, it has rarely been employed for computer-aided diagnosis. This article presents two major contributions. First, the STEP features are introduced to capture temporal enhancement and its spatial variations. This is essentially carried out through the Fourier transformation and pharmacokinetic modeling of various temporal enhancement features, followed by the calculation of moment invariants and Gabor texture features. Second, for effectively extracting the STEP features from tumors, we develop a graph-cut based segmentation algorithm that aims at refining coarse manual segmentations of tumors. The STEP features are assessed through their diagnostic performance for differentiating between benign and malignant tumors using a linear classifier (along with a simple ranking-based feature selection) in a leave-one-out cross-validation setting. The experimental results for the proposed features exhibit superior performance, when compared to the existing approaches, with the area under the ROC curve approaching 0.97

Coronary computed tomography angiography compared with single photon emission computed tomography myocardial perfusion imaging as a guide to optimal medical therapy in patients presenting with stable angina: The RESCUE trial

Background The RESCUE (Randomized Evaluation of Patients with Stable Angina Comparing Utilization of Noninvasive Examinations) trial was a randomized, controlled, multicenter, comparative efficacy outcomes trial designed to assess whether initial testing with coronary computed tomographic angiography (CCTA) is noninferior to single photon emission computed tomography (SPECT) myocardial perfusion imaging in directing patients with stable angina to optimal medical therapy alone or optimal medical therapy with revascularization. Methods and Results The end point was first major adverse cardiovascular event (MACE) (cardiac death or myocardial infarction), or revascularization. Noninferiority margin for CCTA was set a priori as a hazard ratio (HR) of 1.3 (95% CI=0, 1.605). One thousand fifty participants from 44 sites were randomized to CCTA (n=518) or SPECT (n=532). Mean follow-up time was 16.2 (SD 7.9) months. There were no cardiac-related deaths. In patients with a negative CCTA there was 1 acute myocardial infarction; in patients with a negative SPECT examination there were 2 acute myocardial infarctions; and for positive CCTA and SPECT, 1 acute myocardial infarction each. Participants in the CCTA arm had a similar rate of MACE or revascularization compared with those in the SPECT myocardial perfusion imaging arm, (HR, 1.03; 95% CI=0.61-1.75)

Original contribution Reduced susceptibility effects in perfusion fMRI with single-shot spin- echo EPI acquisitions at 1.5 Tesla

Abstract Arterial spin labeling (ASL) perfusion contrast is not based on susceptibility effects and can therefore be used to study brain function in regions of high static inhomogeneity. As a proof of concept, single-shot spin-echo echo-planar imaging (EPI) acquisition was carried out with a multislice continuous ASL (CASL) method at 1.5T. A bilateral finger tapping paradigm was used in the presence of an exogenously induced susceptibility artifact over left motor cortex. The spin-echo CASL technique was compared with a regular gradient-echo EPI sequence with the same slice thickness, as well as other imaging methods using thin slices and spin-echo acquisitions. The results demonstrate improved functional sensitivity and efficiency of the spin-echo CASL approach as compared with gradient-echo EPI techniques, and a trend of improved sensitivity as compared with spin-echo EPI approach in the brain regions affected by the susceptibility artifact. ASL images, either with or without subtraction of the control, provide a robust alternative to blood oxygenation level dependant (BOLD) methods for activation imaging in regions of high static field inhomogeneity

Future vision for the quality assurance of oncology clinical trials

The National Cancer Institute clinical cooperative groups have been instrumental over the past 50 years in developing clinical trials and evidence-based process improvements for clinical oncology patient care. The cooperative groups are undergoing a transformation process as we further integrate molecular biology into personalized patient care and move to incorporate international partners in clinical trials. To support this vision, data acquisition and data management informatics tools must become both nimble and robust to support transformational research at an enterprise level. Information, including imaging, pathology, molecular biology, radiation oncology, surgery, systemic therapy, and patient outcome data needs to be integrated into the clinical trial charter using adaptive clinical trial mechanisms for design of the trial. This information needs to be made available to investigators using digital processes for real-time data analysis. Future clinical trials will need to be designed and completed in a timely manner facilitated by nimble informatics processes for data management. This paper discusses both past experience and future vision for clinical trials as we move to develop data management and quality assurance processes to meet the needs of the modern trial