Search

Background Purpose Methods Results Conclusions Multi‐organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector‐based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentation performance.We propose UNet‐based multi‐organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral information effectively.The proposed method consists of the following steps: Noise reduction in bin‐wise images, image‐based material decomposition, generating VMIs, and deep learning‐based segmentation. VMIs are synthesized for various x‐ray energies using basis images. The UNet‐based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual‐ and quad‐energy bins. We compared it to the conventional PCCT‐based segmentation, which uses only the (noise‐reduced) bin‐wise images. The experiments were conducted on two cases by adjusting the dose levels.The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin‐wise images from dual‐energy bins; 3D UNet).The proposed method using VMIs improves training stability for multi‐organ segmentation in PCCT, particularly when the number of energy bins is small. [ABSTRACT FROM AUTHOR]

Diffuse optical tomography (DOT) is a sensitive and relatively low cost imaging modality that reconstructs optical properties of a highly scattering medium. However, due to the diffusive nature of light propagation, the problem is severely ill-conditioned and highly nonlinear. Even though nonlinear iterative methods have been commonly used, they are computationally expensive especially for three dimensional imaging geometry. Recently, compressed sensing theory has provided a systematic understanding of high resolution reconstruction of sparse objects in many imaging problems; hence, the goal of this paper is to extend the theory to the diffuse optical tomography problem. The main contributions of this paper are to formulate the imaging problem as a joint sparse recovery problem in a compressive sensing framework and to propose a novel noniterative and exact inversion algorithm that achieves the l0 optimality as the rank of measurement increases to the unknown sparsity level. The algorithm is based on the recently discovered generalized MUSIC criterion, which exploits the advantages of both compressive sensing and array signal processing. A theoretical criterion for optimizing the imaging geometry is provided, and simulation results confirm that the new algorithm outperforms the existing algorithms and reliably reconstructs the optical inhomogeneities when we assume that the optical background is known to a reasonable accuracy. [ABSTRACT FROM AUTHOR]