This correspondence introduces a new approach to characterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) reflected a specific scale and orientation sensitivity. Wavelet packet representations for twenty-five natural textures were classified without error by a simple two-layer network classifier. An analyzing function of large regularity (0 2 0) was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments (Ds). In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classification without error for the twenty-five textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.

Introduces a novel approach for accomplishing mammographic feature analysis by overcomplete multiresolution representations. The authors show that efficient representations may be identified within a continuum of scale-space and used to enhance features of importance to mammography. Methods of contrast enhancement are described based on three overcomplete multiscale representations: 1) the dyadic wavelet transform (separable), 2) the phi-transform (nonseparable, nonorthogonal), and 3) the hexagonal wavelet transform (nonseparable). Multiscale edges identified within distinct levels of transform space provide local support for image enhancement. Mammograms are reconstructed from wavelet coefficients modified at one or more levels by local and global nonlinear operators. In each case, edges and gain parameters are identified adaptively by a measure of energy within each level of scale-space. The authors show quantitatively that transform coefficients, modified by adaptive nonlinear operators, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. The authors' results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. They demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology, one can improve chances of early detection while requiring less time to evaluate mammograms for most patients.

This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.

IMPORTANCE While air pollutants at historical levels have been associated with cardiovascular and respiratory diseases, it is not known whether exposure to contemporary air pollutant concentrations is associated with progression of emphysema. OBJECTIVE To assess the longitudinal association of ambient ozone (O 3), fine particulate matter (PM 2.5), oxides of nitrogen (NO x), and black carbon exposure with change in percent emphysema assessed via computed tomographic (CT) imaging and lung function. DESIGN, SETTING, AND PARTICIPANTS This cohort study included participants from the Multi-Ethnic Study of Atherosclerosis (MESA) Air and Lung Studies conducted in 6 metropolitan regions of the United States, which included 6814 adults aged 45 to 84 years recruited between July 2000 and August 2002, and an additional 257 participants recruited from February 2005 to May 2007, with follow-up through November 2018. EXPOSURES Residence-specific air pollutant concentrations (O 3 , PM 2.5 , NO x , and black carbon) were estimated by validated spatiotemporal models incorporating cohort-specific monitoring, determined from 1999 through the end of follow-up. MAIN OUTCOMES AND MEASURES Percent emphysema, defined as the percent of lung pixels less than −950 Hounsfield units, was assessed up to 5 times per participant via cardiac CT scan (2000-2007) and equivalent regions on lung CT scans (2010-2018). Spirometry was performed up to 3 times per participant (2004-2018). RESULTS Among 7071 study participants (mean [range] age at recruitment, 60 [45-84] years; 3330 [47.1%] were men), 5780 were assigned outdoor residential air pollution concentrations in the year of their baseline examination and during the follow-up period and had at least 1 follow-up CT scan, and 2772 had at least 1 follow-up spirometric assessment, over a median of 10 years. Median percent emphysema was 3% at baseline and increased a mean of 0.58 percentage points per 10 years. Mean ambient concentrations of PM 2.5 and NO x , but not O 3 , decreased substantially during follow-up. Ambient concentrations of O 3 , PM 2.5 , NO x , and black carbon at study baseline were significantly associated with greater increases in percent emphysema per 10 years (O 3 : 0.13 per 3 parts per billion [95% CI, 0.03-0.24]; PM 2.5 : 0.11 per 2 μg/m 3 [95% CI, 0.03-0.19]; NO x : 0.06 per 10 parts per billion [95% CI, 0.01-0.12]; black carbon: 0.10 per 0.2 μg/m 3 [95% CI, 0.01-0.18]). Ambient O 3 and NO x concentrations, but not PM 2.5 concentrations, during follow-up were also significantly associated with greater increases in percent emphysema. Ambient O 3 concentrations, but not other pollutants, at baseline and during follow-up were significantly associated with a greater decline in forced expiratory volume in 1 second per 10 years (baseline: 13.41 mL per 3 parts per billion [95% CI, 0.7-26.1]; follow-up: 18.15 mL per 3 parts per billion [95% CI, 1.59-34.71]). CONCLUSIONS AND RELEVANCE In this cohort study conducted between 2000 and 2018 in 6 US metropolitan regions, long-term exposure to...

For time-resolved acquisitions with k-space undersampling, a simulation method was developed for selecting imaging parameters based on minimization of errors in signal intensity versus time and physiologic parameters derived from tracer kinetic analysis. Optimization was performed for time-resolved angiography with stochastic trajectories (TWIST) algorithm applied to contrast-enhanced MR renography. A realistic 4D phantom comprised of aorta and two kidneys, one healthy and one diseased, was created with ideal tissue time-enhancement pattern generated using a three-compartment model with fixed parameters, including glomerular filtration rate (GFR) and renal plasma flow (RPF). TWIST acquisitions with different combinations of sampled central and peripheral k-space portions were applied to this phantom. Acquisition performance was assessed by the difference between simulated signal intensity ( Key words: time-resolved MRI; dynamic contrast-enhanced MRI; MR renography; optimal sampling; TWIST Dynamic contrast-enhanced MR imaging (DCE MRI) plays an important role in many applications, such as perfusion imaging in oncology (1), MR angiography (2), and MR renography (MRR) (3,4). Among the key requirements of DCE MRI is achieving sufficiently high temporal resolution without sacrificing spatial resolution and anatomic coverage. Strategies for achieving both high temporal and spatial resolution often employ k-space undersampling, such as keyhole imaging (5), blocked regional interpolation scheme for k-space (BRISK) (6), continuous update with random encoding (CURE) (7), time-resolved imaging of contrast kinetics (TRICKS) (8,9), and k-t Broad-use Linear Acquisition Speed-up Technique (k-t BLAST) (10). The resulting image artifacts and spatial resolution depend on the size of the frequently updated portion of k-space (the "center") and on the nature and extent of undersampling of the periphery. A large central portion of k-space is likely to produce high-quality images but lower temporal resolution. On the other hand, undersampling of the peripheral k-space regions can result in ringing artifacts, which not only impair postprocessing steps, such as image segmentation, but may also obscure visualization and characterization of smaller structures. Furthermore, undersampling may distort enhancement curves, especially when the signal is changing rapidly, for example, during firstpass perfusion, and can affect the accuracy of kinetic modeling parameters.Despite increasing use of fast acquisition techniques and DCE MRI in diagnostic radiology, few studies have explored the problem of balancing the temporal and spatial properties of the acquisition protocol. A number of studies have evaluated the minimum temporal resolution required for accurate derivation of parameters using tracer kinetic modeling from dynamic data (11); however, there is no general methodology to guide the selection of optimal imaging parameters necessary to achieve proper temporal resolution as well as good-quality images. In humans, the main obstacle to opti...

No abstract

We present the first computational study investigating the electric field (E-field) strength generated by various electroconvulsive therapy (ECT) electrode configurations in specific brain regions of interest (ROIs) that have putative roles in the therapeutic action and/or adverse side effects of ECT. This study also characterizes the impact of the white matter (WM) conductivity anisotropy on the E-field distribution. A finite element head model incorporating tissue heterogeneity and WM anisotropic conductivity was constructed based on structural magnetic resonance imaging (MRI) and diffusion tensor MRI data. We computed the spatial E-field distributions generated by three standard ECT electrode placements including bilateral (BL), bifrontal (BF), and right unilateral (RUL) and an investigational electrode configuration for focal electrically administered seizure therapy (FEAST). The key results are that (1) the median E-field strength over the whole brain is 3.9, 1.5, 2.3, and 2.6 V/cm for the BL, BF, RUL, and FEAST electrode configurations, respectively, which coupled with the broad spread of the BL E-field suggests a biophysical basis for observations of superior efficacy of BL ECT compared to BF and RUL ECT; (2) in the hippocampi, BL ECT produces a median E-field of 4.8 V/cm that is 1.5–2.8 times stronger than that for the other electrode configurations, consistent with the more pronounced amnestic effects of BL ECT; and (3) neglecting the WM conductivity anisotropy results in E-field strength error up to 18% overall and up to 39% in specific ROIs, motivating the inclusion of the WM conductivity anisotropy in accurate head models. This computational study demonstrates how the realistic finite element head model incorporating tissue conductivity anisotropy provides quantitative insight into the biophysics of ECT, which may shed light on the differential clinical outcomes seen with various forms of ECT, and may guide the development of novel stimulation paradigms with improved risk/benefit ratio.

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.