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

Recently, 225Ac 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 225Ac and 213Bi because daughter nuclides, including 213Bi, can cause renal toxicity, which may hinder the widespread use of 225Ac for targeted alpha therapy. In this study, we describe and validate a spectrum decomposition method for dual-isotope imaging of 225Ac and 213Bi using an alpha imaging detector. We implemented an experiment to demonstrate the feasibility of using the alpha imaging detector to obtain distribution images using therapeutic amounts of 225Ac. In addition, we designed and conducted a Monte Carlo simulation under realistic conditions based on the experimental results to evaluate the spectrum decomposition method for dual-isotope imaging. The alpha imaging detector exhibited a detection efficiency of 18.5% and an energy resolution of 13.4% at 5.5 MeV. In the simulation, the distributions of 225Ac and 213Bi were obtained in each region with a relative error of 5%. The results of this study confirmed the feasibility of the dual-isotope imaging method for discriminating alpha-emitters using small amounts of 225Ac.

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. Synthetic projection images and soft-tissue selective images were obtained from the CT data set via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs. RESULTS: Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the American Association of Physicists in Medicine lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The peak signal-to-noise ratio and structural similarity values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively. CONCLUSION: The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays. ADVANCES IN KNOWLEDGE: The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.

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. Then, the scatter-corrected measurement is used for the spectrum estimation method that consists of deciding the weights of predefined spectra and representing the spectrum as a linear combination of the predefined spectra with the weights. The performances of the estimation method combined with scatter correction are evaluated on both simulated and experimental data. RESULTS: The results show that the estimated spectra using the scatter-corrected projection nearly match the true spectra. The normalized-root-mean-square-error and the mean energy difference between the estimated spectra and corresponding true spectra are reduced from 5.8% and 1.33 keV without the scatter correction to 3.2% and 0.73 keV with the scatter correction for both simulation and experimental data, respectively. CONCLUSIONS: The proposed method is more accurate for the acquisition of x-ray spectrum than the estimation method without scatter correction and the spectrum can be successfully estimated even the materials of the filters and their thicknesses are unknown. The proposed method has the potential to be used in several diagnostic x-ray imaging applications.

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. We implemented both algorithms and performed a systematic simulation and experiments on body and skull phantoms. The image characteristics were evaluated and compared. RESULTS: The CS-based algorithm yielded much better reconstruction quality in ROI-DTS compared to the FBP-based algorithm, preserving superior image homogeneity, edge sharpening, and in-plane resolution. The image characteristics of the CS-reconstructed images in ROI-DTS were not significantly different from those in full-FOV DTS. The measured CNR value of the CS-reconstructed ROI-DTS image was about 12.3, about 1.9 times larger than that of the FBP-reconstructed ROI-DTS image. CONCLUSIONS: ROI-DTS images of substantially high accuracy were obtained using the CS-based algorithm and at reduced imaging doses and less computational cost, compared to typical full-FOV DTS images. We expect that the proposed method will be useful for the development of new DTS systems.