Attenuation correction (AC) is important for an accurate interpretation and quantitative analysis of SPECT myocardial perfusion imaging. Dedicated cardiac SPECT systems have invaluable efficacy in the evaluation and risk stratification of patients with known or suspected cardiovascular disease. However, most dedicated cardiac SPECT systems are standalone, not combined with a transmission imaging capability such as computed tomography (CT) for generating attenuation maps for AC. To address this problem, we propose to apply a conditional generative adversarial network (cGAN) for generating attenuation-corrected SPECT images (SPECTGAN ) directly from non-corrected SPECT images (SPECTNC) in image domain as a one-step process without requiring additional intermediate step. The proposed network was trained and tested for 100 cardiac SPECT/CT data from a GE Discovery NM 570c SPECT/CT, collected retrospectively at Yale New Haven Hospital.The generated images were evaluated quantitatively through the normalized root mean square error (NRMSE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) and statistically through joint histogram and error maps. In comparison to the reference CT-based correction (SPECTCTAC ), NRMSEs were 0.2258±0.0777 and 0.1410±0.0768 (37.5% reduction of errors); PSNRs 31.7712±2.9965 and 36.3823±3.7424 (14.5% improvement in signal to noise ratio); SSIMs 0.9877±0.0075 and 0.9949±0.0043 (0.7% improvement in structural similarity) for SPECTNC and SPECTGAN , respectively. This work demonstrates that the conditional adversarial training can achieve accurate CT-less attenuation correction for SPECT MPI, that is quantitatively comparable to CTAC. Standalone dedicated cardiac SPECT scanners can benefit from the proposed GAN to reduce attenuation artifacts efficiently.
Cardiac computed tomography (CCT) has been widely used in diagnoses of coronary artery diseases due to the continuously improving temporal and spatial resolution. When helical CT with a lower pitch scanning mode is used, the effective radiation dose can be significant when compared to other radiological exams. Many methods have been developed to reduce radiation dose in coronary CT exams including high pitch scans using dual source CT scanners and step-and-shot scanning mode for both single source and dual source CT scanners. Additionally, software methods have also been proposed to reduce noise in the reconstructed CT images and thus offering the opportunity to reduce radiation dose while maintaining the desired diagnostic performance of a certain imaging task. In this paper, we propose that low-dose scans should be considered in order to avoid the harm from accumulating unnecessary X-ray radiation. However, low dose CT (LDCT) images tend to be degraded by quantum noise and streak artifacts. Accordingly, in this paper, a 3D dictionary representation based image processing method is proposed to reduce CT image noise. Information on both spatial and temporal structure continuity is utilized in sparse representation to improve the performance of the image processing method. Clinical cases were used to validate the proposed method.
Projection and back-projection are the most computational consuming parts in Computed Tomography (CT) reconstruction. Parallelization strategies using GPU computing techniques have been introduced. We in this paper present a new parallelization scheme for both projection and back-projection. The proposed method is based on CUDA technology carried out by NVIDIA Corporation. Instead of build complex model, we aimed on optimizing the existing algorithm and make it suitable for CUDA implementation so as to gain fast computation speed. Besides making use of texture fetching operation which helps gain faster interpolation speed, we fixed sampling numbers in the computation of projection, to ensure the synchronization of blocks and threads, thus prevents the latency caused by inconsistent computation complexity. Experiment results have proven the computational efficiency and imaging quality of the proposed method.
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