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

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

Measurement of enzymatic rates in humans

This thesis describes the development of an analysis method for data acquired using magnetic resonance (MR) techniques for the measurement of enzymatic rates in humans. We will evaluate aspects and compare the practicality of magnetization transfer experiments available to us for the study of enzyme kinetics. As an example we measure the creatine kinase reaction in human skeletal muscle. We discuss problems which are inherent in the MR techniques as well as in the system we chose to study. While theoretical analyses of magnetization transfer (MT) techniques have been determined to be valid (Forsen & Hoffman 1964, and our own unpublished results), only the saturation transfer technique has been validated in an in vivo system (Friedrich et al. 1993). We demonstrate that MT methods do not always yield accurate kinetic measurements. This failure, in the case of the saturation transfer method, is due to the introduction of unavoidable incomplete saturation. Here we derive, from theory, a modified approach for the analysis of this saturation transfer data. We demonstrate how this new method works on non-ideal saturation transfer data from human skeletal muscle with incomplete saturation of the $\gamma$P-ATP resonance. The problem of incomplete saturation is further complicated by inhomogeneous B\sb1 fields. A saturation pulse used with a surface coil to observe a large sample volume yields a saturation which is not constant throughout the sample volume. While we can correct for errors arising from an incomplete saturation produced within homogeneous B\sb1 fields, it is not trivial nor may it be possible to provide a full correction for errors arising from apparent incomplete saturation produced because of an inhomogeneous B\sb1 field. While the correction for incomplete saturation derived in chapter 2 is valid for homogeneous B\sb1 fields, it can be used as an approximate correction for saturation transfer data acquired with inhomogeneous B\sb1 fields

Opioid Agonist Therapy During Hospitalization Within the Veterans Health Administration: a Pragmatic Retrospective Cohort Analysis

Background Hospitalization of patients with opioid use disorder (OUD) is increasing, yet little is known about opioid agonist therapy (OAT: methadone and buprenorphine) administration during admission. Objective Describe and examine patient- and hospital-level characteristics associated with OAT receipt during hospitalization in the Veterans Health Administration (VHA). Participants A total of 12,407 unique patients, ≥ 18 years old, with an OUD-related ICD-10 diagnosis within 12 months prior to or during index hospitalization in fiscal year 2017 from 109 VHA hospitals in the continental U.S. Main Measure OAT received during hospitalization. Key Results Few admissions received OAT (n = 1914; 15%) and when provided it was most often for withdrawal management (n = 834; 7%). Among patients not on OAT prior to admission who survived hospitalization (n = 10,969), 2.0% (n = 203) were newly initiated on OAT with linkage to care after hospital discharge. Hospitals varied in the frequency of OAT delivery (range, 0 to 43% of qualified admissions). Patients with pre-admission OAT (adjusted odds ratio [AOR] = 15.30; 95% CI [13.2, 17.7]), acute OUD diagnosis (AOR = 2.3; 95% CI [1.99, 2.66]), and male gender (AOR 1.52; 95% CI [1.16, 2.01]) had increased odds of OAT receipt. Patients who received non-OAT opioids (AOR 0.53; 95% CI [0.46, 0.61]) or surgical procedures (AOR 0.75; 95% CI [0.57, 0.99]) had decreased odds of OAT receipt. Large-sized (AOR = 2.0; 95% CI [1.39, 3.00]) and medium-sized (AOR = 1.9; 95% CI [1.33, 2.70]) hospitals were more likely to provide OAT. Conclusions In a sample of VHA inpatient medical admissions, OAT delivery was infrequent, varied across the health system, and was associated with specific patient and hospital characteristics. Policy and educational interventions should promote hospital-based OAT delivery

Diffusion imaging of human breast

It is shown that diffusion-weighted imaging is possible in the human breast. Diffusion constants were measured in the breast parenchyma of four volunteers with no known breast lesions. The apparent diffusion constant of water measured in regions of interest chosen in normal human breast fibroglandular tissue was 1.64±0.19x10-5 cm2/s and that measured in the area of fatty breast tissue was 0.32±0.18x10-5 cm2/s. The resulting images indicate that fibroglandular tissue and fat can be clearly distinguished in diffusion-weighted as well as in absolute diffusion images of the breast. Potential future applications of this technology for the study of breast pathologies are suggested

Computerized Image Analysis for Identifying Triple-Negative Breast Cancers and Differentiating Them from Other Molecular Subtypes of Breast Cancer on Dynamic Contrast-enhanced MR Images: A Feasibility Study

PurposeTo determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images.Materials and methodsThis is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board-approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (Az) was calculated for each of five lesion classification tasks involving triple-negative breast cancers.ResultsFor each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an Az of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non-triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma.ConclusionTriple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women

Computerized Image Analysis for Identifying Triple-Negative Breast Cancers and Differentiating Them from Other Molecular Subtypes of Breast Cancer on Dynamic Contrast-enhanced MR Images: A Feasibility Study

PURPOSE: To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)–positive cancer, human epidermal growth factor receptor type 2 (HER2)–positive cancer, and benign fibroadenoma lesions on dynamic contrast material–enhanced (DCE) magnetic resonance (MR) images. MATERIALS AND METHODS: This is a retrospective study of prospectively acquired breast MR imaging data collected from an institutional review board–approved, HIPAA-compliant study between 2002 and 2007. Written informed consent was obtained from all patients. The authors collected DCE MR images from 65 women with 76 breast lesions who had been recruited into a larger study of breast MR imaging. The women had triple-negative (n = 21), ER-positive (n = 25), HER2-positive (n = 18), or fibroadenoma (n = 12) lesions. All lesions were classified as Breast Imaging Reporting and Data System category 4 or higher on the basis of previous imaging. Images were subject to quantitative feature extraction, feed-forward feature selection by means of linear discriminant analysis, and lesion classification by using a support vector machine classifier. The area under the receiver operating characteristic curve (A(z)) was calculated for each of five lesion classification tasks involving triple-negative breast cancers. RESULTS: For each pair-wise lesion type comparison, linear discriminant analysis helped identify the most discriminatory features, which in conjunction with a support vector machine classifier yielded an A(z) of 0.73 (95% confidence interval [CI]: 0.59, 0.87) for triple-negative cancer versus all non–triple-negative lesions, 0.74 (95% CI: 0.60, 0.88) for triple-negative cancer versus ER- and HER2-positive cancer, 0.77 (95% CI: 0.63, 0.91) for triple-negative versus ER-positive cancer, 0.74 (95% CI: 0.58, 0.89) for triple-negative versus HER2-positive cancer, and 0.97 (95% CI: 0.91, 1.00) for triple-negative cancer versus fibroadenoma. CONCLUSION: Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non–triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women. © RSNA, 2014 Online supplemental material is available for this article

"When I am teaching German, I put on a persona": Exploring lived experiences of teaching a foreign language.

The field of teacher identity is a complex area that has attracted the interest of numerous researchers over the last decades. Within this field the topic of the identity of language teachers has recently emerged. Many of the studies devoted to language teachers’ identity have focused on the teachers as object of study, (Morgan and Clarke 2011; Ying Cheung et al. 2015; Sachs 2005) little attention, however, has been paid in relation to how teachers themselves make sense of their experience of teaching a foreign language. This present study adresses this underexplored aspect by using a pheneomenological approach, focusing on the lived experience of two language teachers. The method used facilitates an insight into the lived world of language teachers. It aims to offer a relevant contribution to the teacher identity research, in particular in how language teachers experience their self; it also reveals a different insight in relation to the way multiple identities are experienced by language teachers