Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.

Optimizing angular range in digital breast tomosynthesis: A phantom study investigating lesion detection across varied breast density and thickness.

PURPOSE: To determine the optimal angular range (AR) for digital breast tomosynthesis (DBT) systems that provides highest lesion visibility across various breast densities and thicknesses. METHOD: A modular DBT phantom, consisting of tissue-equivalent adipose and glandular modules, along with a module embedded with test objects (speckles, masses, fibers), was used to create combinations simulating different breast thicknesses, densities, and lesion locations. A prototype DBT system operated at four ARs (AR±7.5°, AR±12.5°, AR±19°, and AR±25°) to acquire 11 projection images for each combination, with separate fixed doses for thin and thick combinations. Three blinded radiologists independently assessed lesion visibility in reconstructed images; assessments were averaged and compared using linear mixed models. RESULTS: Speckle visibility was highest with AR±7.5° or AR±12.5°, decreasing with wider ARs in all density and thickness combinations. The difference between AR±7.5° and AR±12.5° was not statistically significant, except for the tube-side speckles in thin-fatty combinations (5.83 [AR±7.5°] vs. 5.39 [AR±12.5°], P = 0.019). Mass visibility was not affected by AR in thick combinations, while AR±12.5° exhibited the highest mass visibility for both thin-fatty and thin-dense combinations (P = 0.032 and 0.007, respectively). Different ARs provided highest fiber visibility for different combinations; however, AR±12.5° consistently provided highest or comparable visibility. AR±12.5° showed highest overall lesion visibility for all density and thickness combinations. CONCLUSIONS: AR±12.5° exhibited the highest overall lesion visibility across various phantom thicknesses and densities using a projection number of 11.

A feasibility study on deep-neural-network-based dose-neutral dual-energy digital breast tomosynthesis.

BACKGROUND: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet. PURPOSE: As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT. METHODS: The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method. RESULTS: The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data. CONCLUSIONS: The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts.

Total Knee Arthroplasty: Is It Safe? A Single-Center Study of 4,124 Patients in South Korea.

Background: Although total knee arthroplasty (TKA) is considered an effective treatment for knee osteoarthritis, it carries risks of complications. With a growing number of TKAs performed on older patients, understanding the cause of mortality is crucial to enhance the safety of TKA. This study aimed to identify the major causes of short- and long-term mortality after TKA and report mortality trends for major causes of death. Methods: A total of 4,124 patients who underwent TKA were analyzed. The average age at surgery was 70.7 years. The average follow-up time was 73.5 months. The causes of death were retrospectively collected through Korean Statistical Information Service and classified into 13 subgroups based on the International Classification of Diseases-10 code. The short- and long-term causes of death were identified within the time-to-death intervals of 30, 60, 90, 180, 180 days, and > 180 days. Standard mortality ratios (SMRs) and cumulative incidence of deaths were computed to examine mortality trends after TKA. Results: The short-term mortality rate was 0.07% for 30 days, 0.1% for 60 days, 0.2% for 90 days, and 0.2% for 180 days. Malignant neoplasm and cardiovascular disease were the main short-term causes of death. The long-term (> 180 days) mortality rate was 6.2%. Malignant neoplasm (35%), others (11.7%), and respiratory disease (10.1%) were the major long-term causes of death. Men had a higher cumulative risk of death for respiratory, metabolic, and cardiovascular diseases. Age-adjusted mortality was significantly higher in TKA patients aged 70 years (SMR, 4.3; 95% confidence interval [CI], 3.3-5.4) and between 70 and 79 years (SMR 2.9; 95% CI, 2.5-3.5) than that in the general population. Conclusions: The short-term mortality rate after TKA was low, and most of the causes were unrelated to TKA. The major causes of long-term death were consistent with previous findings. Our findings can be used as counseling data to understand the survival and mortality of TKA patients.

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection.

Diffuse optical tomography (DOT) non-invasively measures the functional characteristics of breast lesions using near infrared light to probe tissue optical properties. This study aimed to evaluate a new digital breast tomosynthesis (DBT)/DOT fusion imaging technique and obtain preliminary data for breast cancer detection. Twenty-eight women were prospectively enrolled and underwent both DBT and DOT examinations. DBT/DOT fusion imaging was created after acquisition of both examinations. Two breast radiologists analyzed DBT and DOT images independently, and then finally evaluated the fusion images. The diagnostic performance of each reading session was compared and interobserver agreement was assessed. The technical success rate was 96.4%, with one failure due to an error during DOT data storage. Among the 27 women finally included in the analysis, 13 had breast cancer. The areas under the receiver operating characteristic curve (AUCs) for DBT were 0.783 and 0.854 for readers 1 and 2, respectively. DOT showed comparable diagnostic performance to DBT for both readers. The AUCs were significantly improved (P = 0.004) when the DBT/DOT fusion images were used. Interobserver agreements were highest for the DBT/DOT fusion images. In conclusion, this study suggests that DBT/DOT fusion imaging technique appears to be a promising tool for breast cancer diagnosis.

Deep Learning Diffuse Optical Tomography.

Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.

Sampling scheme optimization for diffuse optical tomography based on data and image space rankings.

We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source­detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.

Radium isotopes in the Ulsan Bay.

In order to estimate the fluxes of Ra isotopes, we measured 224Ra, 226Ra, and 228Ra activities in the Ulsan Bay mixing zone. The convex upwards curvature of the plot of Ra isotope activities versus salinity for the mixing zone suggests that Ra isotopes are supplied from particles entering the mixing zone from both the river and bottom sediments. This addition increases the estuarine flux of 226Ra and 228Ra to the outer sea by factors of 15 and 95 over the flux attributable to Ra dissolved in the riverine water alone. In order to estimate the residence time of the water in Ulsan Bay, we applied a mass balance model to the distribution of 224Ra and 226Ra activities in the Ulsan Bay mixing zone with the inflow from the Taehwa River. The obtained residence times of the waters in the Ulsan Bay were estimated to be 6.8-11.4 d. The waters in the upper part of the estuary have long residence times whereas those in the lower part, in contact with the open sea, have shorter residence times. The mean residence time of the water in the Ulsan Bay was estimated to be 9.1 d.

Massive sedimentation of fine sediment with organic matter and enhanced benthic-pelagic coupling by an artificial dyke in semi-enclosed Chonsu Bay, Korea.

To assess the impact of an artificial dyke in Chonsu Bay (CBD) on the organic carbon (C(org)) cycle, we measured excess (210)Pb activities, C(org) and nitrogen content in sediment cores. The C(org) oxidation rates (C(ox)) on the surface sediment and benthic nutrient fluxes were also quantified with an in situ benthic chamber. The higher excess (210)Pb inventory, C(org) and nitrogen in cores near the CBD indicated lateral transport and local, massive deposition of particulate matter due to tidal circulation altered by artificial dyke construction. The C(ox) in sediment near the CBD was about twice as high as that out of the bay, suggesting the importance of benthic remineralization of organic matter. The benthic fluxes of dissolved inorganic nitrogen and phosphate were four to six times higher than those outside the bay, corresponding to 141% and 131% respectively, of the requirements for primary production.

A portable microfluidic flow cytometer based on simultaneous detection of impedance and fluorescence.

A portable microfluidic flow cytometer with dual detection ability of impedance and fluorescence was developed for cell analysis and particle-based assays. In the proposed system, fluorescence from microparticles and cells is measured through excitation by a light emitting diode (LED) and detection by a solid-stated photomultiplier (SSPM). Simultaneous impedometric detection provides information on the existence and size of microparticles and cells through polyelectrolyte gel electrodes (PGEs) operated by custom designed circuits for signal detection, amplification, and conversion. Fluorescence and impedance signals were sampled at 1 kHz with 12 bit resolution. The resulting microfluidic cytometer is 15x10x10 cm(3) in width, depth, and height, with a weight of about 800 g. Such a miniaturized and battery powered system yielded a portable microfluidic cytometer with high performance. Various microbeads and human embryonic kidney 293 (HEK-293) cells were employed to evaluate the system. Impedance and fluorescence signals from each bead or cell made classification of micro particles or cells easy and fast.