Contrast-Enhanced Mammography-Guided Breast Biopsy: Single-Center Experience.

This prospective single-center study enrolled 50 women with 51 contrast-enhanced mammography (CEM)-enhancing lesions that lacked a sonographic or mammographic correlate. Trial participants underwent CEM-guided biopsy. Biopsy was technically successful for 46 lesions and was not performed for five nonvisualized lesions (all nonmass enhancement), yielding a cancellation rate of 9.8%. Mean biopsy time was 16.6 minutes. All biopsies revealed concordant pathology (25 benign, 10 high-risk, 11 malignant). No non-visualized or benign lesion yielded malignancy at follow-up.

Likelihood ratio of sonohysterographic findings for discriminating endometrial polyps from submucosal fibroids.

OBJECTIVES: The purpose of this study was to determine which combination of sonohysterographic features has the highest likelihood ratios (LRs) in discriminating polyps from submucosal fibroids. METHODS: This retrospective study included 200 consecutive patients who underwent both sonohysterography and a procedure resulting in a positive pathologic diagnosis. A reader, masked to the imaging and pathologic findings, independently reviewed the 200 sonograms and recorded the findings using a standardized checklist for sonographic features on sonohysterography. The features assessed included angle, echogenicity, endometrial-myometrial interface, and vascular pattern, among others. The reader chose one final diagnosis from the list of possibilities, which included normal, hyperplasia, polyp, submucosal fibroid, cancer, adhesions, and clots. Sonographic observations were then compared to pathologic findings. RESULTS: The LR of 13.4 was achieved for polyps when there was a combination of an intact endometrial-myometrial interface, a single vessel, an acute angle, and homogeneous echogenicity. The highest LR of 27.8 was achieved for submucosal fibroids when the combination of sonographic features included an absent endometrial-myometrial interface, an arborized/multiple vascular pattern, an obtuse angle, and heterogeneous echogenicity. CONCLUSIONS: A combination of sonographic findings may provide high LRs for discriminating endometrial polyps from submucosal fibroids.

Transcutaneous bowel sonography for inflammatory bowel disease is sensitive and specific when performed in a nonexpert low-volume North American center.

OBJECTIVES: Transcutaneous bowel sonography is a nonionizing imaging modality used in inflammatory bowel disease. Although available in Europe, its uptake in North America has been limited. Since the accuracy of bowel sonography is highly operator dependent, low-volume centers in North America may not achieve the same diagnostic accuracy reported in the European literature. Our objective was to determine the diagnostic accuracy of bowel sonography in a nonexpert low-volume center. METHODS: All cases of bowel sonography at a single tertiary care center during an 18-month period were reviewed. Bowel sonography was compared with reference standards, including small-bowel follow-through, computed tomography, magnetic resonance imaging, colonoscopy, and surgical findings. RESULTS: A total of 103 cases were included for analysis during the study period. The final diagnoses included Crohn disease (72), ulcerative colitis (8), hemolytic uremic syndrome (1), and normal (22). The sensitivity and specificity of bowel sonography for intestinal wall inflammation were 87.8% and 92.6%, respectively. In the subset of patients who had complications of Crohn disease, the sensitivity and specificity were 50% and 100% for fistulas and 14% and 100% for strictures. One patient had an abscess, which was detected by bowel sonography. Abnormal bowel sonographic findings contributed to the escalation of treatment in 55% of cases. CONCLUSIONS: Bowel sonography for inflammatory bowel disease can be performed in low-volume centers and provides diagnostic accuracy for luminal disease comparable with published data, although it is less sensitive for complications of Crohn disease.

Sonohysterography: the utility of diagnostic criteria sets.

OBJECTIVE: The objective of our study was to determine whether applying specific diagnostic criteria to the interpretation of sonohysterography would improve the diagnostic accuracy of the interpretation. MATERIALS AND METHODS: This retrospective study included 200 consecutive patients who underwent both sonohysterography and a procedure that resulted in a positive pathologic diagnosis. The initial interpretation (reader 1) was performed at the time of the examination. Subsequently, a reviewer with limited expertise (reader 2) interpreted the sonohysterograms masked to both the original medical imaging report and the final pathologic diagnosis. Reader 2 used a set of standardized diagnostic criteria to aid in arriving at one of the following diagnoses: normal, endometrial polyp, endometrial hyperplasia, endometrial carcinoma, submucosal fibroid, adenomyosis, adhesions, or clots. These results were compared with the initial diagnostic report (reader 1) and the final pathologic findings. RESULTS: Overall agreement with pathology findings was 76.7% for reader 1 and 84.9% for reader 2. Comparison of the readers' interpretations using the pathologic diagnosis as the reference standard showed that reader 2's interpretations, which were established using the diagnostic criteria set, were uniformly better than those of reader 2. CONCLUSION: The application of standardized diagnostic criteria may enhance the diagnostic accuracy of sonohysterography.

Fetal anatomic survey using three-dimensional ultrasound in conjunction with first-trimester nuchal translucency screening.

OBJECTIVE: To determine the visualization rates of fetal anatomic structures by three-dimensional ultrasound (3DUS) at 12-13 weeks of gestation. STUDY DESIGN: This was a prospective observational study of women presenting for nuchal translucency ultrasound. Five 3D volumes of the fetus were acquired transabdominally. Two investigators independently reviewed the stored volumes offline following a standardized protocol. RESULTS: One hundred singleton fetuses were examined. The mean time for 3D volumes acquisition was 4.8 min; and for 3D review 17 min. Anatomic structures were seen as follows: cranium, lateral cerebral ventricles and abdominal wall 100%; stomach, vertebrae, upper and lower limbs >or= 94%; face 71%, bladder 58%, both kidneys 39%, skin overlying spine 26% and heart 18%. Agreement between two observers ranged from 100% (for head, abdominal wall and lower limbs) to 43% (for visualization of skin overlying spine). A complete basic anatomic survey was achieved in 11.4% of the 12-week fetuses and 33.3% of the 13-week fetuses (p-value = 0.038). CONCLUSIONS: First-trimester transabdominal 3DUS was adequate for assessment of the head, abdominal wall, stomach, limbs and vertebral alignment. It was less effective for evaluating the heart and intactness of the skin over the spine.

Multitarget Sparse Latent Regression.

Multitarget regression has recently generated intensive popularity due to its ability to simultaneously solve multiple regression tasks with improved performance, while great challenges stem from jointly exploring inter-target correlations and input-output relationships. In this paper, we propose multitarget sparse latent regression (MSLR) to simultaneously model intrinsic intertarget correlations and complex nonlinear input-output relationships in one single framework. By deploying a structure matrix, the MSLR accomplishes a latent variable model which is able to explicitly encode intertarget correlations via -norm-based sparse learning; the MSLR naturally admits a representer theorem for kernel extension, which enables it to flexibly handle highly complex nonlinear input-output relationships; the MSLR can be solved efficiently by an alternating optimization algorithm with guaranteed convergence, which ensures efficient multitarget regression. Extensive experimental evaluation on both synthetic data and six greatly diverse real-world data sets shows that the proposed MSLR consistently outperforms the state-of-the-art algorithms, which demonstrates its great effectiveness for multivariate prediction.

Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning.

Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and the complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model. In this paper, we propose a semi-automated method for multitype cardiac indices estimation. After the manual labeling of two landmarks for ROI cropping, an integrated deep neural network Indices-Net is designed to jointly learn the representation and regression models. It comprises two tightly-coupled networks, such as a deep convolution autoencoder for cardiac image representation, and a multiple output convolution neural network for indices regression. Joint learning of the two networks effectively enhances the expressiveness of image representation with respect to cardiac indices, and the compatibility between image representation and indices regression, thus leading to accurate and reliable estimations for all the cardiac indices. When applied with five-fold cross validation on MR images of 145 subjects, Indices-Net achieves consistently low estimation error for LV wall thicknesses (1.44 ± 0.71 mm) and areas of cavity and myocardium (204 ± 133 mm2). It outperforms, with significant error reductions, segmentation method (55.1% and 17.4%), and two-phase direct volume-only methods (12.7% and 14.6%) for wall thicknesses and areas, respectively. These advantages endow the proposed method a great potential in clinical cardiac function assessment.

Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.

Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.

Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression.

Cardiac four-chamber volume estimation serves as a fundamental and crucial role in clinical quantitative analysis of whole heart functions. It is a challenging task due to the huge complexity of the four chambers including great appearance variations, huge shape deformation and interference between chambers. Direct estimation has recently emerged as an effective and convenient tool for cardiac ventricular volume estimation. However, existing direct estimation methods were specifically developed for one single ventricle, i.e., left ventricle (LV), or bi-ventricles; they can not be directly used for four chamber volume estimation due to the great combinatorial variability and highly complex anatomical interdependency of the four chambers. In this paper, we propose a new, general framework for direct and simultaneous four chamber volume estimation. We have addressed two key issues, i.e., cardiac image representation and simultaneous four chamber volume estimation, which enables accurate and efficient four-chamber volume estimation. We generate compact and discriminative image representations by supervised descriptor learning (SDL) which can remove irrelevant information and extract discriminative features. We propose direct and simultaneous four-chamber volume estimation by the multioutput sparse latent regression (MSLR), which enables jointly modeling nonlinear input-output relationships and capturing four-chamber interdependence. The proposed method is highly generalized, independent of imaging modalities, which provides a general regression framework that can be extensively used for clinical data prediction to achieve automated diagnosis. Experiments on both MR and CT images show that our method achieves high performance with a correlation coefficient of up to 0.921 with ground truth obtained manually by human experts, which is clinically significant and enables more accurate, convenient and comprehensive assessment of cardiac functions.

Unsupervised Freeview Groupwise Cardiac Segmentation Using Synchronized Spectral Network.

The diagnosis, comparative and population study of cardiac radiology data require heart segmentation on increasingly large amount of images from different modalities/chambers/patients under various imaging views. Most existing automatic cardiac segmentation methods are often limited to single image segmentation with regulated modality/region settings or well-cropped ROI areas, which is impossible for large datasets due to enormous device protocols and institutional differences. A pure data-driven unsupervised segmentation without regulated setting requirements is crucial in this scenario, and will significantly automate the manual work and adopt the various changes of modality, subject or view. In this paper, we propose a general unsupervised groupwise segmentation: a direct simultaneous segmentation for a group of multi-modality, multi-chamber, multi-subject ( M3) cardiac images from a freely chosen imaging view. The segmentation can directly perform not only on regulated two/four-chamber images, but also on non-regulated uncropped raw MR/CT scans. A new Synchronized Spectral Network (SSN) is developed for the simultaneous decomposing, synchronizing, and clustering the spectral features of free-view M3 cardiac images. The SSN-based groupwise analysis of image spectral bases immediately leads to groupwise segmentation of M3 freeview images. The segmentation is quantitatively evaluated by three datasets (MR and CT mixed) with more than 200 subjects. High dice metric ( ) is consistently achieved in validation. Our method provides a powerful tool for medical images under general imaging environment.

Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation.

Direct estimation of cardiac ventricular volumes has become increasingly popular and important in cardiac function analysis due to its effectiveness and efficiency by avoiding an intermediate segmentation step. However, existing methods rely on either intensive user inputs or problematic assumptions. To realize the full capacities of direct estimation, this paper presents a general, fully learning-based framework for direct bi-ventricular volume estimation, which removes user inputs and unreliable assumptions. We formulate bi-ventricular volume estimation as a general regression framework which consists of two main full learning stages: unsupervised cardiac image representation learning by multi-scale deep networks and direct bi-ventricular volume estimation by random forests. By leveraging strengths of generative and discriminant learning, the proposed method produces high correlations of around 0.92 with ground truth by human experts for both the left and right ventricles using a leave-one-subject-out cross validation, and largely outperforms existing direct methods on a larger dataset of 100 subjects including both healthy and diseased cases with twice the number of subjects used in previous methods. More importantly, the proposed method can not only be practically used in clinical cardiac function analysis but also be easily extended to other organ volume estimation tasks.

Intrahepatic ovulation.

Ectopic ovaries are a rare finding in the literature, with fewer than 50 published cases to date. This phenomenon has been found in the omentum, bladder, mesentery, and uterus; attached to the colon; inside the left labia majora; and in the kidney. Various etiologies have been proposed, including postsurgical or postinflammatory transplantation, malignant origins, and abnormal embryologic development. We report the ultrasonographic, computed tomographic (CT), and magnetic resonance (MR) imaging of, what is to the best of our knowledge, the first case of an intrahepatic ectopic ovary.

Direct estimation of cardiac bi-ventricular volumes with regression forests.

Accurate estimation of ventricular volumes plays an essential role in clinical diagnosis of cardiac diseases. Existing methods either rely on segmentation or are restricted to direct estimation of the left ventricle. In this paper, we propose a novel method for direct and joint volume estimation of bi-ventricles, i.e., the left and right ventricles, without segmentation and user inputs. Based on the cardiac image representation by multiple and complementary features, we adopt regression forests to jointly estimate the two volumes. Our method is validated on a dataset of 56 subjects with a total of 3360 MR images which shows that our method can achieve a high correlation coefficient of around 0.9 with manual segmentation obtained by human experts. With our proposed method, the most daily-used estimation of cardiac function, e.g., ejection fraction, can be conducted in a much more efficient, accurate and convenient way.