Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes.

PURPOSE: To develop a set of accurate 2D models of compressed breasts undergoing mammography or breast tomosynthesis, based on objective analysis, to accurately characterize mammograms with few linearly independent parameters, and to generate novel clinically realistic paired cranio-caudal (CC) and medio-lateral oblique (MLO) views of the breast. METHODS: We seek to improve on an existing model of compressed breasts by overcoming detector size bias, removing the nipple and non-mammary tissue, pairing the CC and MLO views from a single breast, and incorporating the pectoralis major muscle contour into the model. The outer breast shapes in 931 paired CC and MLO mammograms were automatically detected with an in-house developed segmentation algorithm. From these shapes three generic models (CC-only, MLO-only, and joint CC/MLO) with linearly independent components were constructed via principal component analysis (PCA). The ability of the models to represent mammograms not used for PCA was tested via leave-one-out cross-validation, by measuring the average distance error (ADE). RESULTS: The individual models based on six components were found to depict breast shapes with accuracy (mean ADE-CC = 0.81 mm, ADE-MLO = 1.64 mm, ADE-Pectoralis = 1.61 mm), outperforming the joint CC/MLO model (P ≤ 0.001). The joint model based on 12 principal components contains 99.5% of the total variance of the data, and can be used to generate new clinically realistic paired CC and MLO breast shapes. This is achieved by generating random sets of 12 principal components, following the Gaussian distributions of the histograms of each component, which were obtained from the component values determined from the images in the mammography database used. CONCLUSION: Our joint CC/MLO model can successfully generate paired CC and MLO view shapes of the same simulated breast, while the individual models can be used to represent with high accuracy clinical acquired mammograms with a small set of parameters. This is the first step toward objective 3D compressed breast models, useful for dosimetry and scatter correction research, among other applications.

Air quality monitoring using mobile microscopy and machine learning.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 µm have been classified as carcinogenic by the World Health Organization. Here we present a field-portable cost-effective platform for high-throughput quantification of particulate matter using computational lens-free microscopy and machine-learning. This platform, termed c-Air, is also integrated with a smartphone application for device control and display of results. This mobile device rapidly screens 6.5 L of air in 30 s and generates microscopic images of the aerosols in air. It provides statistics of the particle size and density distribution with a sizing accuracy of ~93%. We tested this mobile platform by measuring the air quality at different indoor and outdoor environments and measurement times, and compared our results to those of an Environmental Protection Agency-approved device based on beta-attenuation monitoring, which showed strong correlation to c-Air measurements. Furthermore, we used c-Air to map the air quality around Los Angeles International Airport (LAX) over 24 h to confirm that the impact of LAX on increased PM concentration was present even at >7 km away from the airport, especially along the direction of landing flights. With its machine-learning-based computational microscopy interface, c-Air can be adaptively tailored to detect specific particles in air, for example, various types of pollen and mold and provide a cost-effective mobile solution for highly accurate and distributed sensing of air quality.

High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader.

Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array. An inexpensive optical fiber-array enables the capture of the transmitted light of each well through the smartphone camera. A custom-designed application sends the captured image to a server to automatically determine well-turbidity, with results returned to the smartphone in ~1 minute. We tested this mobile-reader using MTPs prepared with 17 antibiotics targeting Gram-negative bacteria on clinical isolates of Klebsiella pneumoniae, containing highly-resistant antimicrobial profiles. Using 78 patient isolate test-plates, we demonstrated that our mobile-reader meets the FDA-defined AST criteria, with a well-turbidity detection accuracy of 98.21%, minimum-inhibitory-concentration accuracy of 95.12%, and a drug-susceptibility interpretation accuracy of 99.23%, with no very major errors. This mobile-reader could eliminate the need for trained diagnosticians to perform AST, reduce the cost-barrier for routine testing, and assist in spatio-temporal tracking of bacterial resistance.

Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays.

Standard microplate based enzyme-linked immunosorbent assays (ELISA) are widely utilized for various nanomedicine, molecular sensing, and disease screening applications, and this multiwell plate batched analysis dramatically reduces diagnosis costs per patient compared to nonbatched or nonstandard tests. However, their use in resource-limited and field-settings is inhibited by the necessity for relatively large and expensive readout instruments. To mitigate this problem, we created a hand-held and cost-effective cellphone-based colorimetric microplate reader, which uses a 3D-printed opto-mechanical attachment to hold and illuminate a 96-well plate using a light-emitting-diode (LED) array. This LED light is transmitted through each well, and is then collected via 96 individual optical fibers. Captured images of this fiber-bundle are transmitted to our servers through a custom-designed app for processing using a machine learning algorithm, yielding diagnostic results, which are delivered to the user within ∼1 min per 96-well plate, and are visualized using the same app. We successfully tested this mobile platform in a clinical microbiology laboratory using FDA-approved mumps IgG, measles IgG, and herpes simplex virus IgG (HSV-1 and HSV-2) ELISA tests using a total of 567 and 571 patient samples for training and blind testing, respectively, and achieved an accuracy of 99.6%, 98.6%, 99.4%, and 99.4% for mumps, measles, HSV-1, and HSV-2 tests, respectively. This cost-effective and hand-held platform could assist health-care professionals to perform high-throughput disease screening or tracking of vaccination campaigns at the point-of-care, even in resource-poor and field-settings. Also, its intrinsic wireless connectivity can serve epidemiological studies, generating spatiotemporal maps of disease prevalence and immunity.

Quantification of plant chlorophyll content using Google Glass.

Measuring plant chlorophyll concentration is a well-known and commonly used method in agriculture and environmental applications for monitoring plant health, which also correlates with many other plant parameters including, e.g., carotenoids, nitrogen, maximum green fluorescence, etc. Direct chlorophyll measurement using chemical extraction is destructive, complex and time-consuming, which has led to the development of mobile optical readers, providing non-destructive but at the same time relatively expensive tools for evaluation of plant chlorophyll levels. Here we demonstrate accurate measurement of chlorophyll concentration in plant leaves using Google Glass and a custom-developed software application together with a cost-effective leaf holder and multi-spectral illuminator device. Two images, taken using Google Glass, of a leaf placed in our portable illuminator device under red and white (i.e., broadband) light-emitting-diode (LED) illumination are uploaded to our servers for remote digital processing and chlorophyll quantification, with results returned to the user in less than 10 seconds. Intensity measurements extracted from the uploaded images are mapped against gold-standard colorimetric measurements made through a commercially available reader to generate calibration curves for plant leaf chlorophyll concentration. Using five plant species to calibrate our system, we demonstrate that our approach can accurately and rapidly estimate chlorophyll concentration of fifteen different plant species under both indoor and outdoor lighting conditions. This Google Glass based chlorophyll measurement platform can display the results in spatiotemporal and tabular forms and would be highly useful for monitoring of plant health in environmental and agriculture related applications, including e.g., urban plant monitoring, indirect measurements of the effects of climate change, and as an early indicator for water, soil, and air quality degradation.

Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning.

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 µm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 µm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.

Imaging and sizing of single DNA molecules on a mobile phone.

DNA imaging techniques using optical microscopy have found numerous applications in biology, chemistry and physics and are based on relatively expensive, bulky and complicated set-ups that limit their use to advanced laboratory settings. Here we demonstrate imaging and length quantification of single molecule DNA strands using a compact, lightweight and cost-effective fluorescence microscope installed on a mobile phone. In addition to an optomechanical attachment that creates a high contrast dark-field imaging setup using an external lens, thin-film interference filters, a miniature dovetail stage and a laser-diode for oblique-angle excitation, we also created a computational framework and a mobile phone application connected to a server back-end for measurement of the lengths of individual DNA molecules that are labeled and stretched using disposable chips. Using this mobile phone platform, we imaged single DNA molecules of various lengths to demonstrate a sizing accuracy of <1 kilobase-pairs (kbp) for 10 kbp and longer DNA samples imaged over a field-of-view of ∼2 mm2.

Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay.

Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99% of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed. Such a prescreening can be performed using a small attachment on a cellphone. To this end, a cellphone-based imaging platform for a microsphere fluorescence immunoassay that detects the presence of anti-recombinant bovine somatotropin (rbST) antibodies in milk extracts was developed. RbST administration to cows increases their milk production, but is illegal in the EU and a public health concern in the USA. The cellphone monitors the presence of anti-rbST antibodies (rbST biomarker), which are endogenously produced upon administration of rbST and excreted in milk. The rbST biomarker present in milk extracts was captured by rbST covalently coupled to paramagnetic microspheres and labeled by quantum dot (QD)-coupled detection antibodies. The emitted fluorescence light from these captured QDs was then imaged using the cellphone camera. Additionally, a dark-field image was taken in which all microspheres present were visible. The fluorescence and dark-field microimages were analyzed using a custom-developed Android application running on the same cellphone. With this setup, the microsphere fluorescence immunoassay and cellphone-based detection were successfully applied to milk sample extracts from rbST-treated and untreated cows. An 80% true-positive rate and 95% true-negative rate were achieved using this setup. Next, the cellphone-based detection platform was benchmarked against a newly developed planar imaging array alternative and found to be equally performing versus the much more sophisticated alternative. Using cellphone-based on-site analysis in future residue monitoring can limit the number of samples for laboratory analysis already at an early stage. Therewith, the entire monitoring process can become much more efficient and economical.

X-ray scatter correction in breast tomosynthesis with a precomputed scatter map library.

PURPOSE: To develop and evaluate the impact on lesion conspicuity of a software-based x-ray scatter correction algorithm for digital breast tomosynthesis (DBT) imaging into which a precomputed library of x-ray scatter maps is incorporated. METHODS: A previously developed model of compressed breast shapes undergoing mammography based on principal component analysis (PCA) was used to assemble 540 simulated breast volumes, of different shapes and sizes, undergoing DBT. A Monte Carlo (MC) simulation was used to generate the cranio-caudal (CC) view DBT x-ray scatter maps of these volumes, which were then assembled into a library. This library was incorporated into a previously developed software-based x-ray scatter correction, and the performance of this improved algorithm was evaluated with an observer study of 40 patient cases previously classified as BI-RADS® 4 or 5, evenly divided between mass and microcalcification cases. Observers were presented with both the original images and the scatter corrected (SC) images side by side and asked to indicate their preference, on a scale from -5 to +5, in terms of lesion conspicuity and quality of diagnostic features. Scores were normalized such that a negative score indicates a preference for the original images, and a positive score indicates a preference for the SC images. RESULTS: The scatter map library removes the time-intensive MC simulation from the application of the scatter correction algorithm. While only one in four observers preferred the SC DBT images as a whole (combined mean score = 0.169 ± 0.37, p > 0.39), all observers exhibited a preference for the SC images when the lesion examined was a mass (1.06 ± 0.45, p < 0.0001). When the lesion examined consisted of microcalcification clusters, the observers exhibited a preference for the uncorrected images (-0.725 ± 0.51, p < 0.009). CONCLUSIONS: The incorporation of the x-ray scatter map library into the scatter correction algorithm improves the efficiency of the algorithm. The observer study presented here is also the first test of the scatter correction algorithm with patient images and human observers, and demonstrates its potential to improve the clinical performance of DBT.

Immunochromatographic diagnostic test analysis using Google Glass.

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.e., the user) and also stored on a central server along with the RDT image, QR code, and other related information (e.g., demographic data). The same server also provides a dynamic spatiotemporal map and real-time statistics for uploaded RDT results accessible through Internet browsers. We tested this Google Glass-based diagnostic platform using qualitative (i.e., yes/no) human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) tests. For the quantitative RDTs, we measured activated tests at various concentrations ranging from 0 to 200 ng/mL for free and total PSA. This wearable RDT reader platform running on Google Glass combines a hands-free sensing and image capture interface with powerful servers running our custom image processing codes, and it can be quite useful for real-time spatiotemporal tracking of various diseases and personal medical conditions, providing a valuable tool for epidemiology and mobile health.

Detection and spatial mapping of mercury contamination in water samples using a smart-phone.

Detection of environmental contamination such as trace-level toxic heavy metal ions mostly relies on bulky and costly analytical instruments. However, a considerable global need exists for portable, rapid, specific, sensitive, and cost-effective detection techniques that can be used in resource-limited and field settings. Here we introduce a smart-phone-based hand-held platform that allows the quantification of mercury(II) ions in water samples with parts per billion (ppb) level of sensitivity. For this task, we created an integrated opto-mechanical attachment to the built-in camera module of a smart-phone to digitally quantify mercury concentration using a plasmonic gold nanoparticle (Au NP) and aptamer based colorimetric transmission assay that is implemented in disposable test tubes. With this smart-phone attachment that weighs <40 g, we quantified mercury(II) ion concentration in water samples by using a two-color ratiometric method employing light-emitting diodes (LEDs) at 523 and 625 nm, where a custom-developed smart application was utilized to process each acquired transmission image on the same phone to achieve a limit of detection of ∼ 3.5 ppb. Using this smart-phone-based detection platform, we generated a mercury contamination map by measuring water samples at over 50 locations in California (USA), taken from city tap water sources, rivers, lakes, and beaches. With its cost-effective design, field-portability, and wireless data connectivity, this sensitive and specific heavy metal detection platform running on cellphones could be rather useful for distributed sensing, tracking, and sharing of water contamination information as a function of both space and time.

Objective models of compressed breast shapes undergoing mammography.

PURPOSE: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes. METHODS: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE). RESULTS: The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material. CONCLUSIONS: Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing 'slide-level' diagnosis by using individual 'cell-level' diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.

Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry.

PURPOSE: To compare the estimate of normalized glandular dose in mammography and breast CT imaging obtained using the actual glandular tissue distribution in the breast to that obtained using the homogeneous tissue mixture approximation. METHODS: Twenty volumetric images of patient breasts were acquired with a dedicated breast CT prototype system and the voxels in the breast CT images were automatically classified into skin, adipose, and glandular tissue. The breasts in the classified images underwent simulated mechanical compression to mimic the conditions present during mammographic acquisition. The compressed thickness for each breast was set to that achieved during each patient's last screening cranio-caudal (CC) acquisition. The volumetric glandular density of each breast was computed using both the compressed and uncompressed classified images, and additional images were created in which all voxels representing adipose and glandular tissue were replaced by a homogeneous mixture of these two tissues in a proportion corresponding to each breast's volumetric glandular density. All four breast images (compressed and uncompressed; heterogeneous and homogeneous tissue) were input into Monte Carlo simulations to estimate the normalized glandular dose during mammography (compressed breasts) and dedicated breast CT (uncompressed breasts). For the mammography simulations the x-ray spectra used was that used during each patient's last screening CC acquisition. For the breast CT simulations, two x-ray spectra were used, corresponding to the x-ray spectra with the lowest and highest energies currently being used in dedicated breast CT prototype systems under clinical investigation. The resulting normalized glandular dose for the heterogeneous and homogeneous versions of each breast for each modality was compared. RESULTS: For mammography, the normalized glandular dose based on the homogeneous tissue approximation was, on average, 27% higher than that estimated using the true heterogeneous glandular tissue distribution (Wilcoxon Signed Rank Test p = 0.00046). For dedicated breast CT, the overestimation of normalized glandular dose was, on average, 8% (49 kVp spectrum, p = 0.00045) and 4% (80 kVp spectrum, p = 0.000089). Only two cases in mammography and two cases in dedicated breast CT with a tube voltage of 49 kVp resulted in lower dose estimates for the homogeneous tissue approximation compared to the heterogeneous tissue distribution. CONCLUSIONS: The normalized glandular dose based on the homogeneous tissue mixture approximation results in a significant overestimation of dose to the imaged breast. This overestimation impacts the use of dose estimates in absolute terms, such as for risk estimates, and may impact some comparative studies, such as when modalities or techniques with different x-ray energies are used. The error introduced by the homogeneous tissue mixture approximation in higher energy x-ray modalities, such as dedicated breast CT, although statistically significant, may not be of clinical concern. Further work is required to better characterize this overestimation and potentially develop new metrics or correction factors to better estimate the true glandular dose to breasts undergoing imaging with ionizing radiation.

Crowd-sourced BioGames: managing the big data problem for next-generation lab-on-a-chip platforms.

We describe a crowd-sourcing based solution for handling large quantities of data that are created by e.g., emerging digital imaging and sensing devices, including next generation lab-on-a-chip platforms. We show that in cases where the diagnosis is a binary decision (e.g., positive vs. negative, or infected vs. uninfected), it is possible to make accurate diagnosis by crowd-sourcing the raw data (e.g., microscopic images of specimens/cells) using entertaining digital games (i.e., ) that are played on PCs, tablets or mobile phones. We report the results and the analysis of a large-scale public experiment toward diagnosis of malaria infected human red blood cells (RBCs), where binary responses from approximately 1000 untrained individuals from more than 60 different countries are combined together (corresponding to more than 1 million cell diagnoses), resulting in an accuracy level that is comparable to those of expert medical professionals. This platform holds promise toward cost-effective and accurate tele-pathology, improved training of medical personnel, and can also be used to manage the "Big Data" problem that is emerging through next generation digital lab-on-a-chip devices.

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.

Clinical digital breast tomosynthesis system: dosimetric characterization.

PURPOSE: To comprehensively characterize the dosimetric properties of a clinical digital breast tomosynthesis (DBT) system for the acquisition of mammographic and tomosynthesis images. MATERIALS AND METHODS: Compressible water-oil mixture phantoms were created and imaged by using the automatic exposure control (AEC) of the Selenia Dimensions system (Hologic, Bedford, Mass) in both DBT and full-field digital mammography (FFDM) mode. Empirical measurements of the x-ray tube output were performed with a dosimeter to measure the air kerma for the range of tube current-exposure time product settings and to develop models of the automatically selected x-ray spectra. A Monte Carlo simulation of the system was developed and used in conjunction with the AEC-chosen settings and spectra models to compute and compare the mean glandular dose (MGD) resulting from both imaging modalities for breasts of varying sizes and glandular compositions. RESULTS: Acquisition of a single craniocaudal view resulted in an MGD ranging from 0.309 to 5.26 mGy in FFDM mode and from 0.657 to 3.52 mGy in DBT mode. For a breast with a compressed thickness of 5.0 cm and a 50% glandular fraction, a DBT acquisition resulted in an only 8% higher MGD than an FFDM acquisition (1.30 and 1.20 mGy, respectively). For a breast with a compressed thickness of 6.0 cm and a 14.3% glandular fraction, a DBT acquisition resulted in an 83% higher MGD than an FFDM acquisition (2.12 and 1.16 mGy, respectively). CONCLUSION: For two-dimensional-three-dimensional fusion imaging with the Selenia Dimensions system, the MGD for a 5-cm-thick 50% glandular breast is 2.50 mGy, which is less than the Mammography Quality Standards Act limit for a two-view screening mammography study.

BioGames: A Platform for Crowd-Sourced Biomedical Image Analysis and Telediagnosis.

We have recently proposed a mathematical framework for crowd-sourcing of biomedical image analysis and diagnosis through digital gaming. Here we review our recent progress on this gaming platform and demonstrate its viability for telediagnosis of malaria, achieving an accuracy that is within less than 2 percent of that of a trained expert.

A software-based x-ray scatter correction method for breast tomosynthesis.

PURPOSE: To develop a software-based scatter correction method for digital breast tomosynthesis (DBT) imaging and investigate its impact on the image quality of tomosynthesis reconstructions of both phantoms and patients. METHODS: A Monte Carlo (MC) simulation of x-ray scatter, with geometry matching that of the cranio-caudal (CC) view of a DBT clinical prototype, was developed using the Geant4 toolkit and used to generate maps of the scatter-to-primary ratio (SPR) of a number of homogeneous standard-shaped breasts of varying sizes. Dimension-matched SPR maps were then deformed and registered to DBT acquisition projections, allowing for the estimation of the primary x-ray signal acquired by the imaging system. Noise filtering of the estimated projections was then performed to reduce the impact of the quantum noise of the x-ray scatter. Three dimensional (3D) reconstruction was then performed using the maximum likelihood-expectation maximization (MLEM) method. This process was tested on acquisitions of a heterogeneous 50∕50 adipose∕glandular tomosynthesis phantom with embedded masses, fibers, and microcalcifications and on acquisitions of patients. The image quality of the reconstructions of the scatter-corrected and uncorrected projections was analyzed by studying the signal-difference-to-noise ratio (SDNR), the integral of the signal in each mass lesion (integrated mass signal, IMS), and the modulation transfer function (MTF). RESULTS: The reconstructions of the scatter-corrected projections demonstrated superior image quality. The SDNR of masses embedded in a 5 cm thick tomosynthesis phantom improved 60%-66%, while the SDNR of the smallest mass in an 8 cm thick phantom improved by 59% (p < 0.01). The IMS of the masses in the 5 cm thick phantom also improved by 15%-29%, while the IMS of the masses in the 8 cm thick phantom improved by 26%-62% (p < 0.01). Some embedded microcalcifications in the tomosynthesis phantoms were visible only in the scatter-corrected reconstructions. The visibility of the findings in two patient images was also improved by the application of the scatter correction algorithm. The MTF of the images did not change after application of the scatter correction algorithm, indicating that spatial resolution was not adversely affected. CONCLUSIONS: Our software-based scatter correction algorithm exhibits great potential in improving the image quality of DBT acquisitions of both phantoms and patients. The proposed algorithm does not require a time-consuming MC simulation for each specific case to be corrected, making it applicable in the clinical realm.

Lens-free optical tomographic microscope with a large imaging volume on a chip.

We present a lens-free optical tomographic microscope, which enables imaging a large volume of approximately 15 mm(3) on a chip, with a spatial resolution of < 1 µm × < 1 µm × < 3 µm in x, y and z dimensions, respectively. In this lens-free tomography modality, the sample is placed directly on a digital sensor array with, e.g., ≤ 4 mm distance to its active area. A partially coherent light source placed approximately 70 mm away from the sensor is employed to record lens-free in-line holograms of the sample from different viewing angles. At each illumination angle, multiple subpixel shifted holograms are also recorded, which are digitally processed using a pixel superresolution technique to create a single high-resolution hologram of each angular projection of the object. These superresolved holograms are digitally reconstructed for an angular range of ± 50°, which are then back-projected to compute tomograms of the sample. In order to minimize the artifacts due to limited angular range of tilted illumination, a dual-axis tomography scheme is adopted, where the light source is rotated along two orthogonal axes. Tomographic imaging performance is quantified using microbeads of different dimensions, as well as by imaging wild-type Caenorhabditis elegans. Probing a large volume with a decent 3D spatial resolution, this lens-free optical tomography platform on a chip could provide a powerful tool for high-throughput imaging applications in, e.g., cell and developmental biology.