Purpose:To explore the optical and physiologic properties of normal and lesion-bearing breasts by using a combined optical and digital breast tomosynthesis (DBT) imaging system. Materials and Methods:Institutional review board approval and patient informed consent were obtained for this HIPAA-compliant study. Combined optical and tomosynthesis imaging analysis was performed in 189 breasts from 125 subjects (mean age, 56 years 6 13 [standard deviation]), including 138 breasts with negative fi ndings and 51 breasts with lesions. Threedimensional (3D) maps of total hemoglobin concentration (Hb T ), oxygen saturation (S O 2 ), and tissue reduced scattering coeffi cients were interpreted by using the coregistered DBT images. Paired and unpaired t tests were performed between various tissue types to identify signifi cant differences. Results:The estimated average bulk Hb T from 138 normal breasts was 19.2 m mol/L. The corresponding mean SO 2 was 0.73, within the range of values in the literature. A linear correlation ( R = 0.57, P , .0001) was found between Hb T and the fi broglandular volume fraction derived from the 3D DBT scans. Optical reconstructions of normal breasts revealed structures corresponding to chest-wall muscle, fi broglandular, and adipose tissues in the Hb T , SO 2 , and scattering images. In 26 malignant tumors of 0.6-2.5 cm in size, Hb T was signifi cantly greater than that in the fibroglandular tissue of the same breast ( P = .0062). Solid benign lesions ( n = 17) and cysts ( n = 8) had signifi cantly lower Hb T contrast than did the malignant lesions ( P = .025 and P = .0033, respectively). Conclusion:The optical and DBT images were structurally consistent. The malignant tumors and benign lesions demonstrated different Hb T and scattering contrasts, which can potentially be exploited to reduce the false-positive rate of conventional mammography and unnecessary biopsies. BREAST IMAGING: Combined Optical and X-ray Tomosynthesis Breast Imaging Fang et al cancers and the reduction of the number of biopsies of benign lesions, compared with stand-alone conventional mammography.Our purpose was to explore the optical and physiologic properties of normal and lesion-bearing breasts by using a combined optical and DBT imaging system. Materials and MethodsThe experimental protocols were approved by the institutional review board (Massachusetts General Hospital), and written informed consent was obtained from all subjects. The study was compliant with Health Insurance Portability and Accountability Act guidelines. Imaging InstrumentationA photograph of the combined optical and DBT imaging system and probes is shown in Figures E1 and E2 (online). The detailed confi guration of the imaging physiologic parameters, such as the concentrations of oxygenated hemoglobin (Hb O ), deoxygenated hemoglobin (Hb R ), water, and lipids ( 16 ). With DOT, nearinfrared lasers, either radiofrequencymodulated, continuous-wave or pulsed, are used to probe tissue structure noninvasively ( 13,17 ). The concentrations of the tissue ...

In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (SO2) from 68 breast measurements are 16.2 μm and 71%, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1±6.1 μm for fibroglandular tissue, 15.4±5.0 μm for adipose, and 22.2±7.3 μm for muscle tissue. The differences between fibroglandular tissue and the corresponding adipose tissue are significant (p < 0.0001). At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographical compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis.

A general linear model for time domain (TD) fluorescence tomography is presented that allows a lifetime-based analysis of the entire temporal fluorescence response from a turbid medium. Simulations are used to show that TD fluorescence tomography is optimally performed using two complementary approaches: A direct TD analysis of a few time points near the peak of the temporal response, which provides superior resolution; and an asymptotic multi-exponential analysis based tomography of the decay portion of the temporal response, which provides accurate localization of yield distributions for various lifetime components present in the imaging medium. These results indicate the potential of TD technology for biomedical imaging with lifetime sensitive targeted probes, and provide useful guidelines for an optimal approach to fluorescence tomography with TD data.

A general framework for incorporating single and multiple priors in diffuse optical tomography is described. We explore the use of this framework for simultaneously utilizing spatial and spectral priors in the context of imaging breast cancer. The utilization of magnetic resonance images of water and lipid content as a statistical spatial prior for the diffuse optical image reconstructions is also discussed. Simulations are performed to demonstrate the significant improvement in image quality afforded by combining spatial and spectral priors.

Cardiac abnormalities are a leading cause of death and their early diagnosis are of importance for providing timely interventions. The goal of 2020 PhysioNet/CinC challenge was to develop algorithms to diagnose multiple cardiac abnormalities using 12-lead ECG data. In this work, we develop a wide and deep transformer neural network to classify each 12-lead ECG sequence into 27 cardiac abnormality classes. Our approach combines handcrafted ECG features, which were determined to be important by a random forest model, and discriminative feature representations that are automatically learned from a transformer neural network. Our entry to the 2020 Phys-ioNet/CinC challenge placed 1 st out of 41 official ranking teams (team name = prna). Using the official generalized weighted accuracy metric for evaluation, we achieved a validation score of 0.587 and top score of 0.533 on the full held-out test set.