In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that...

Ultrasound-modulated optical tomography (UOT) detects ultrasonically modulated light to spatially localize multiply scattered photons in turbid media with the ultimate goal of imaging the optical properties in living subjects. A principal challenge of the technique is weak modulated signal strength. We discuss ways to push the limits of signal enhancement with intense acoustic bursts while conf...

Digital Breast Tomosynthesis (DBT) is a technology that creates three dimensional (3D) images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study u...

Model-based image reconstruction (MBIR) methods for X-ray CT reconstruction can improve image quality and reduce patient X-ray dose. These methods produce images by solving high-dimensional, statistically motivated numerical optimization problems, but unfortunately the high computational costs of solving these problems have kept MBIR algorithms from reaching ubiquity in the clinic. In this pape...

Multi-spectral and Fluorescence Diffuse Optical Tomography of Breast Cancer Alper Corlu Arjun G. Yodh Multi-spectral and fluorescence diffuse optical tomography (DOT) techniques are explored and applied to image human breast cancer in vivo. Image reconstruction algorithms that utilize first and second order gradient information are described in detail. Breast DOT requires large computational me...

BACKGROUND In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced...

Biomedical imaging is a rapidly growing field to provide a state-of-the-art tool for preclinical biomedical research and clinical applications, in view of its ability to noninvasively reveal subtle structural variations of biological tissues and visualize in vivo physiological and pathological processes at the cellular and molecular levels. Mathematical methods are involved with imaging theorie...

Hybrid imaging systems combining x-ray computed tomography (CT) and fluorescence tomography can improve fluorescence imaging performance by incorporating anatomical x-ray CT information into the optical inversion problem. While the use of image priors has been investigated in the past, little is known about the optimal use of forward photon propagation models in hybrid optical systems. In this ...

We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD camera pixels. OCT images are reconstructed by solving an optimization problem that minimizes the...