Dual-isotope imaging method for Actinium-225 and Bismuth-213 using alpha imaging detector.

Recently, Ac has received considerable attention for its use in targeted alpha therapy because it has a relatively long half-life and yields four more alpha-particles from the daughter nuclides. The performance evaluation should separately assess the distribution of Ac and Bi because daughter nuclides, including Bi, can cause renal toxicity, which may hinder the widespread use of Ac for targeted alpha therapy. In this study, we describe and validate a spectrum decomposition method for dual-isotope imaging of Ac and Bi using an alpha imaging detector.

Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

Objective: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors.

Methods: This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using data set generated from the single-energy material decomposition (SEMD) technique on CT.

An X-ray spectrum estimation method from transmission measurement combined with scatter correction.

Purpose: Conventional x-ray spectrum estimation methods from transmission measurement often lead to inaccurate results when extensive x-ray scatter is present in the measured projection. This study aims to apply the weighted L1-norm scatter correction algorithm in spectrum estimation for reducing residual differences between the estimated and true spectrum.

Method: The scatter correction algorithm is based on a simple radiographic scattering model where the intensity of scattered x-ray is directly estimated from a transmission measurement.

Image reconstruction in region-of-interest (or interior) digital tomosynthesis (DTS) based on compressed-sensing (CS).

Background And Objective: Digital tomosynthesis (DTS) based on filtered-backprojection (FBP) reconstruction requires a full field-of-view (FOV) scan and relatively dense projections, which results in high doses for medical imaging purposes. To overcome these difficulties, we investigated region-of-interest (ROI) or interior DTS reconstruction where the x-ray beam span covers only a small ROI containing a target area.

Methods: An iterative method based on compressed-sensing (CS) scheme was compared with the FBP-based algorithm for ROI-DTS reconstruction.