Antiscatter grids are used in digital mammography to reduce the scattered radiation from the breast and improve image contrast. They are, however, imperfect and lead to partial absorption of primary radiation, as well as failing to absorb all scattered radiation. Nevertheless, the general consensus has been that antiscatter grids improve image quality for the majority of breast types and sizes. There is, however, inconsistency in the literature, and recent results show that a substantial image quality improvement can be achieved even for thick breasts if the grid is disposed of. The purpose of this study was to investigate if differences in the considered imaging task and experimental setup could explain the different outcomes. We estimated the dose reduction that can be achieved if the grid were to be removed as a function of breast thickness with varying geometries and experimental conditions. Image quality was quantified by the signal-difference-to-noise ratio (SDNR) measured using an aluminum (Al) filter on blocks of poly(methyl methacrylate) (PMMA), and images were acquired with and without grid at a constant exposure. We also used a theoretical model validated with Monte Carlo simulations. Both theoretically and experimentally, the main finding was that when a large 4×8  cm² Al filter was used, the SDNR values for the gridless images were overestimated up to 25% compared to the values for the small 1×1  cm² filter, and gridless imaging was superior for any PMMA thickness. For the small Al filter, gridless imaging was only superior for PMMAs thinner than 4 cm. This discrepancy can be explained by a different sensitivity to and sampling of the angular scatter spread function, depending on the size of the contrast object. The experimental differences were eliminated either by using a smaller region of interest close to the edge of the large filter or by applying a technique of scatter correction by subtracting the estimated scatter image. These results explain the different conclusions reported in the literature and show the importance of the selection of measurement methods. Since the interesting structures in mammography are below the 1-cm scale, we advocate the use of smaller contrast objects for assessment of antiscatter grid performance.

Previously, magnetic induction tomography (MIT) has been considered for noncontact imaging of human tissue electrical properties. Commonly, multiple coils are used, with any one serving as the source while others detect eddy currents generated in the specimen. Here, imaging of low conductivity objects is shown feasible with a single coil acting simultaneously as source and detector, provided that the coil is repeatedly relocated while collecting coil loss data. To enable such “scanning,” an analytical coil loss formula is derived in the quasistatic limit for a single coil consisting of several concentric circular wire loops, all within a common plane. Conductivity may vary arbitrarily in space, whereas permittivity and permeability are treated as uniform. The analytical form is used to build an algorithm for imaging electrical conductivity in human tissues. A practical device operating at 12.5 MHz is described and used in a clinical trial that “scans” the region between the scapulae while collecting coil loss data. Inversion of data leads to electrical conductivity distribution images for the thoracic spinal column which are the first of their kind to correctly distinguish such basic features as size and depth of spinal canal, as well as size, depth, and spacing of transverse spinal processes.

A technology to characterize early enamel lesions is needed in dentistry. Optical coherence tomography (OCT) is a noninvasive method that provides high-resolution cross-sectional images. The aim of this study is to compare OCT with microfocus x-ray computed tomography (μCT) for assessment of natural enamel lesions in vitro. Ten human teeth with visible white spot-like changes on the enamel smooth surface and no cavitation (ICDAS code 2) were subjected to imaging by μCT (SMX-100CT, Shimadzu) and 1300-nm swept-source OCT (Dental SS-OCT, Panasonic Health Care). In μCT, the lesions appeared as radiolucent dark areas, while in SS-OCT, they appeared as areas of increased signal intensity beneath the surface. An SS-OCT attenuation coefficient based on Beer–Lambert law could discriminate lesions from sound enamel. Lesion depth ranged from 175 to 606  μm in SS-OCT. A correlation between μCT and SS-OCT was found regarding lesion depth (R=0.81, p<0.001) and also surface layer thickness (R=0.76, p<0.005). The images obtained clinically in real time using the dental SS-OCT system are suitable for the assessment of natural subsurface lesions and their surface layer, providing comparable images to a laboratory high-resolution μCT without the use of x-ray.

Brain white matter lesions found upon magnetic resonance imaging are often observed in psychiatric or neurological patients. Individuals with these lesions present a more significant cognitive impairment when compared with individuals without them. We propose a computerized method to distinguish tissue containing white matter lesions of different etiologies (e.g., demyelinating or ischemic) using texture-based classifiers. Texture attributes were extracted from manually selected regions of interest and used to train and test supervised classifiers. Experiments were conducted to evaluate texture attribute discrimination and classifiers’ performances. The most discriminating texture attributes were obtained from the gray-level histogram and from the co-occurrence matrix. The best classifier was the support vector machine, which achieved an accuracy of 87.9% in distinguishing lesions with different etiologies and an accuracy of 99.29% in distinguishing normal white matter from white matter lesions.

Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This met hod was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS (R=−0.399 and −0.277, respectively) and with follow-up mRS (R=−0.256 and −0.272), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences <1 point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.

We present a technique to rectify nonrigid registrations by improving their group-wise consistency, which is a widely used unsupervised measure to assess pair-wise registration quality. While pair-wise registration methods cannot guarantee any group-wise consistency, group-wise approaches typically enforce perfect consistency by registering all images to a common reference. However, errors in individual registrations to the reference then propagate, distorting the mean and accumulating in the pair-wise registrations inferred via the reference. Furthermore, the assumption that perfect correspondences exist is not always true, e.g., for interpatient registration. The proposed consistency-based registration rectification (CBRR) method addresses these issues by minimizing the group-wise inconsistency of all pair-wise registrations using a regularized least-squares algorithm. The regularization controls the adherence to the original registration, which is additionally weighted by the local postregistration similarity. This allows CBRR to adaptively improve consistency while locally preserving accurate pair-wise registrations. We show that the resulting registrations are not only more consistent, but also have lower average transformation error when compared to known transformations in simulated data. On clinical data, we show improvements of up to 50% target registration error in breathing motion estimation from four-dimensional MRI and improvements in atlas-based segmentation quality of up to 65% in terms of mean surface distance in three-dimensional (3-D) CT. Such improvement was observed consistently using different registration algorithms, dimensionality (two-dimensional/3-D), and modalities (MRI/CT).

Geographic atrophy (GA) is a manifestation of the advanced or late stage of age-related macular degeneration (AMD). AMD is the leading cause of blindness in people over the age of 65 in the western world. The purpose of this study is to develop a fully automated supervised pixel classification approach for segmenting GA, including uni- and multifocal patches in fundus autofluorescene (FAF) images. The image features include region-wise intensity measures, gray-level co-occurrence matrix measures, and Gaussian filter banks. A k-nearest-neighbor pixel classifier is applied to obtain a GA probability map, representing the likelihood that the image pixel belongs to GA. Sixteen randomly chosen FAF images were obtained from 16 subjects with GA. The algorithm-defined GA regions are compared with manual delineation performed by a certified image reading center grader. Eight-fold cross-validation is applied to evaluate the algorithm performance. The mean overlap ratio (OR), area correlation (Pearson’s r), accuracy (ACC), true positive rate (TPR), specificity (SPC), positive predictive value (PPV), and false discovery rate (FDR) between the algorithm- and manually defined GA regions are 0.72±0.03, 0.98±0.02, 0.94±0.00, 0.87±0.01, 0.96±0.01, 0.80±0.04, and 0.20±0.04, respectively.

Mammographic density (MD) is a significant risk factor for breast cancer and has been shown to reduce the sensitivity of mammography screening. Knowledge of a woman’s density can be used to predict her risk of developing breast cancer and personalize her imaging pathway. However, measurement of breast density has proven to be troublesome with wide variations in density recorded using radiologists’ visual Breast Imaging Reporting and Data System (BIRADS). Several automated methods for assessing breast density have been proposed, each with their own source of measurement error. The use of differing mammographic imaging systems further complicates MD measurement, especially for the same women imaged over time. The purpose of this study was to investigate whether having a mammogram on differing manufacturer’s equipment affects a woman’s MD measurement. Raw mammographic images were acquired on two mammography imaging systems (General Electric and Hologic) one year apart and processed using VolparaDensity™ to obtain the Volpara Density Grade (VDG) and average volumetric breast density percentage (AvBD%). Visual BIRADS scores were also obtained from 20 expert readers. BIRADS scores for both systems showed strong positive correlation (ρ=0.904; p<0.001), while the VDG (ρ=0.978; p<0.001) and AvBD% (ρ=0.973; p<0.001) showed stronger positive correlations. Substantial agreement was shown between the systems for BIRADS (κ=0.692; p<0.001), however, the systems demonstrated an almost perfect agreement for VDG ( κ=0.933; p<0.001).

We used the ideal observer (IO) and IO with model mismatch (IO-MM) applied in the projection domain and an anthropomorphic channelized Hotelling observer (CHO) applied to reconstructed images to optimize the acquisition energy window width and to evaluate various scatter compensation methods in the context of a myocardial perfusion single-photon emission computed tomography (SPECT) defect detection task. The IO has perfect knowledge of the image formation process and thus reflects the performance with perfect compensation for image-degrading factors. Thus, using the IO to optimize imaging systems could lead to suboptimal parameters compared with those optimized for humans interpreting SPECT images reconstructed with imperfect or no compensation. The IO-MM allows incorporating imperfect system models into the IO optimization process. We found that with near-perfect scatter compensation, the optimal energy window for the IO and CHO was similar; in its absence, the IO-MM gave a better prediction of the optimal energy window for the CHO using different scatter compensation methods. These data suggest that the IO-MM may be useful for projection-domain optimization when MM is significant and that the IO is useful when followed by reconstruction with good models of the image formation process.

Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.

We developed robust, three-dimensional methods, as opposed to traditional A-line analysis, for estimating the optical properties of calcified, fibrotic, and lipid atherosclerotic plaques from in vivo coronary artery intravascular optical coherence tomography clinical pullbacks. We estimated attenuation μt and backscattered intensity I₀ from small volumes of interest annotated by experts in 35 pullbacks. Some results were as follows: noise reduction filtering was desirable, parallel line (PL) methods outperformed individual line methods, root mean square error was the best goodness-of-fit, and α-trimmed PL (α-T-PL) was the best overall method. Estimates of μt were calcified (3.84 ± 0.95  mm−¹), fibrotic (2.15 ± 1.08  mm−¹), and lipid (9.99 ± 2.37  mm−¹), similar to those in the literature, and tissue classification from optical properties alone was promising.

We present the first experimental quantification of the tactile spatial responsivity of the cornea and we teach a subject to recognize spatial tactile shapes that are stimulated on their cornea.

We report on a flexible multipurpose nonlinear microscopic imaging system based on a femtosecond excitation source and a photonic crystal fiber with multiple miniaturized time-correlated single-photon counting detectors. The system provides the simultaneous acquisition of e.g., two-photon autofluorescence, second-harmonic generation, and coherent anti-Stokes Raman scattering images. Its flexible scan head permits ex vivo biological imaging with subcellular resolution such as rapid biopsy examination during surgery as well as imaging on small as well as large animals. Above all, such an arrangement perfectly matches the needs for the clinical investigation of human skin in vivo where knowledge about the distribution of endogenous fluorophores, second-harmonic generation–active collagen as well as nonfluorescent lipids is of high interest.

Deidentification of medical images requires attention to both header information as well as the pixel data itself, in which burned-in text may be present. If the pixel data to be deidentified is stored in a compressed form, traditionally it is decompressed, identifying text is redacted, and if necessary, pixel data are recompressed. Decompression without recompression may result in images of excessive or intractable size. Recompression with an irreversible scheme is undesirable because it may cause additional loss in the diagnostically relevant regions of the images. The irreversible (lossy) JPEG compression scheme works on small blocks of the image independently, hence, redaction can selectively be confined only to those blocks containing identifying text, leaving all other blocks unchanged. An open source implementation of selective redaction and a demonstration of its applicability to multiframe color ultrasound images is described. The process can be applied either to standalone JPEG images or JPEG bit streams encapsulated in other formats, which in the case of medical images, is usually DICOM.

Image resolution enhancement is a problem of considerable interest in all medical imaging modalities. Unlike general purpose imaging or video processing, for a very long time, medical image resolution enhancement has been based on optimization of the imaging devices. Although some recent works purport to deal with image postprocessing, much remains to be done regarding medical image enhancement via postprocessing, especially in ultrasound imaging. We face a resolution improvement issue in the case of medical ultrasound imaging. We propose to investigate this problem using multidimensional autoregressive (AR) models. Noting that the estimation of the envelope of an ultrasound radio frequency (RF) signal is very similar to the estimation of classical Fourier-based power spectrum estimation, we theoretically show that a domain change and a multidimensional AR model can be used to achieve super-resolution in ultrasound imaging provided the order is estimated correctly. Here, this is done by means of a technique that simultaneously estimates the order and the parameters of a multidimensional model using relevant regression matrix factorization. Doing so, the proposed method specifically fits ultrasound imaging and provides an estimated envelope. Moreover, an expression that links the theoretical image resolution to both the image acquisition features (such as the point spread function) and a postprocessing feature (the AR model) order is derived. The overall contribution of this work is threefold. First, it allows for automatic resolution improvement. Through a simple model and without any specific manual algorithmic parameter tuning, as is used in common methods, the proposed technique simply and exclusively uses the ultrasound RF signal as input and provides the improved B-mode as output. Second, it allows for the a priori prediction of the improvement in resolution via the knowledge of the parametric model order before actual processing. Finally, to achieve the previous goal, while classical parametric methods would first estimate the model order and then the model parameters, our approach estimates the model parameters and the order simultaneously. The effectiveness of the methodology is validated using two-dimensional synthetic and in vivo data. We show that, compared to other techniques, our method provides better results from a qualitative and a quantitative viewpoint.