Proj2Proj: self-supervised low-dose CT reconstruction.

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention.

Maximum-A-Posteriori Estimation of Retinal Oxygen Tension Based on Photon Counting Statistics.

The regularization of retinal oxygen tension estimation was previously proposed with an assumption that phosphorescence intensity images are corrupted by additive Gaussian noise. Based on this assumption, a regularized least-squares estimate has been shown to be better than a conventional least-squares estimation. However, this assumption is inconsistent with the acquisition process of phosphorescence intensity images acquired using an intensified charge-coupled device camera.

A miniaturized optical tomography platform for volumetric imaging of engineered living systems.

Volumetric optical microscopy approaches that enable acquisition of three-dimensional (3D) information from a biological sample are attractive for numerous non-invasive imaging applications. The unprecedented structural details that these techniques provide have helped in our understanding of different aspects of architecture of cells, tissues, and organ systems as they occur in their natural states. Nonetheless, the instrumentation for most of these techniques is sophisticated, bulky, and costly, and is less affordable to most laboratory settings.

An iterative reconstruction algorithm for digital breast tomosynthesis imaging using real data at three radiation doses.

Background: Iterative image reconstruction in Digital Breast Tomosynthesis (DBT) is a developing modality that produces three-dimensional (3D) reconstructed images of a breast to detect suspicious lesions. Algebraic reconstruction technique (ART), one of the iterative image reconstruction methods, was applied to reconstruct 3D data of breast and is becoming as one alternative method for the conventional image reconstruction techniques such as filtered back projection (FBP) in DBT imaging.

Objective: A new majorization-minimization (MM) algorithm was presented for TV denoising of signals.

Iterative image reconstruction using non-local means with total variation from insufficient projection data.

In this work, algebraic reconstruction technique (ART) is extended by using non-local means (NLM) and total variation (TV) for reduction of artifacts that are due to insufficient projection data. TV and NLM algorithms use different image models and their application in tandem becomes a powerful denoising method that reduces erroneous variations in the image while preserving edges and details. Simulations were performed on a widely used 2D Shepp-Logan phantom to demonstrate performance of the introduced method (ART + TV) NLM and compare it to TV based ART (ART + TV) and ART.

3D digital breast tomosynthesis image reconstruction using anisotropic total variation minimization.

This paper presents a compressed sensing based reconstruction method for 3D digital breast tomosynthesis (DBT) imaging. Algebraic reconstruction technique (ART) has been in use in DBT imaging by minimizing the isotropic total variation (TV) of the reconstructed image. The resolution in DBT differs in sagittal and axial directions which should be encountered during the TV minimization.

An iterative tomosynthesis reconstruction using total variation combined with non-local means filtering.

Background: After the release of compressed sensing (CS) theory, reconstruction algorithms from sparse and incomplete data have shown great improvements in diminishing artifacts of missing data. Following this progress, both local and non-local regularization induced iterative reconstructions have been actively used in limited view angle imaging problems.

Methods: In this study, a 3D iterative image reconstruction method (ART + TV)NLM was introduced by combining local total variation (TV) with non-local means (NLM) filter.

An object-oriented simulator for 3D digital breast tomosynthesis imaging system.

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging.

Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization.

Background: Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS) techniques.

A fast regularized least-squares method for retinal vascular oxygen tension estimation using a phosphorescence lifetime imaging model.

Background: Monitoring retinal oxygenation is of primary importance in detecting the presence of some common eye diseases. To improve the estimation of oxygen tension in retinal vessels, regularized least-squares (RLS) method was shown to be very effective compared with the conventional least-squares (LS) estimation. In this study, we propose an accelerated RLS estimation method for the problem of assessing the oxygenation of retinal vessels from phosphorescence lifetime images.

Direct regularized estimation of retinal vascular oxygen tension based on an experimental model.

Phosphorescence lifetime imaging is commonly used to generate oxygen tension maps of retinal blood vessels by classical least squares (LS) estimation method. A spatial regularization method was later proposed and provided improved results. However, both methods obtain oxygen tension values from the estimates of intermediate variables, and do not yield an optimum estimate of oxygen tension values, due to their nonlinear dependence on the ratio of intermediate variables.

Regularized estimation of retinal vascular oxygen tension from phosphorescence images.

The level of retinal oxygenation is potentially an important cue to the onset or presence of some common retinal diseases. An improved method for assessing oxygen tension in retinal blood vessels from phosphorescence lifetime imaging data is reported in this paper. The optimum estimate for phosphorescence lifetime and oxygen tension is obtained by regularizing the least-squares (LS) method.

Automated respiratory phase and onset detection using only chest sound signal.

The problem of non-invasive detection of respiratory phases and onsets without making direct airflow measurement is addressed here. Currently available techniques require the use of multichannel recorded sounds of both chest and trachea. In this paper, we propose a method which detects both respiratory phases and onsets using only chest sound data.

A robust method to estimate time split in second heart sound using instantaneous frequency analysis.

Closure of the aortic valve (A2) and the pulmonary valve (P2) generates the second heart sound (S2). The time separation between A2 and P2 is known as the A2-P2 split and it has very important diagnostic potential. Methods proposed in the past to measure the split noninvasively are limited either by prior signal modeling assumptions or by reliance on manual processing in key steps.