X‐ray luminescence computed tomography using a hybrid proton propagation model and Lasso‐LSQR algorithm

Abstract

X‐ray luminescence computed tomography (XLCT) uses external X‐rays for luminescence excitation, which is becoming a promising molecular imaging technique with superb penetration depth and spatial resolution. To achieve the tomographic mapping of luminescence distribution, accurate optical propagation model and suitable reconstruction method are two keys for XLCT, but not satisfied. To overcome the limitation of the single proton propagation model (e.g., DE, SP3), we adopted a hybrid diffusion equation with third order simplified spherical harmonics (DE‐SP3) model for XLCT. To enable fast iteration and accurate sparse reconstruction, we also integrated in the inversion optimization, with a novel Least Square QR‐factorization based on the Lasso (Lasso‐LSQR) algorithm. We first simulated the light propagation in various kinds of organs under DE model and SP3 model, respectively. By comparison with the Monte Carlo, these tissues can be categorized into two types, namely DE‐fitted tissues that include muscle and lung, and SP3‐fitted tissues including heart, kidney, liver, and stomach. According to the above classification results, we built a hybrid DE‐SP3 model to more accurately describing light transport. Numerical simulations and in vivo experiments illustrated that hybrid DE‐SP3 model achieves superior reconstruction performance in terms of location accuracy, and spatial resolution than DE, and less computational cost than SP3. The hybrid DE‐SP3 model materializes a balance between accuracy and efficiency for XLCT.

A Finite Element Mesh Regrouping Strategy-Based Hybrid Light Transport Model for Enhancing the Efficiency and Accuracy of XLCT

“…The hybrid diffusion equation– SP N method considered the applicability of SP N and DE models in different biological tissues, and DE was employed to describe light propagation in high-scattering tissues, while SP N was used in other tissues. This method achieved a comparable accuracy and much less computation time compared with the SP N model and a much better accuracy compared with DE as well ( 33 , 34 ). The studies of hybrid models offer ideas to the optical transmission model in our study.…”

Harnessing the Power of Hybrid Light Propagation Model for Three-Dimensional Optical Imaging in Cancer Detection

“…The performance of the hybrid model was validated with both regular geometries and digital mouse model, and results revealed that the HSDE model makes full use of the advantages of SP N and DE in terms of accuracy and efficiency. Subsequently, Chen et al and Wang et al applied the HSDE model as the forward imaging model for fluorescence molecular tomography (28), multispectral Cerenkov luminescence tomography (53), and X-ray luminescence computed tomography (54), respectively. The concise form of the HSDE model can be expressed as ( 29):…”

“…and Wang et al. applied the HSDE model as the forward imaging model for fluorescence molecular tomography ( 28 ), multispectral Cerenkov luminescence tomography ( 53 ), and X-ray luminescence computed tomography ( 54 ), respectively. The concise form of the HSDE model can be expressed as ( 29 ):…”

The Mpi + Cuda Gaia Avu–Gsr Parallel Solver Towards Next-Generation Exascale Infrastructures

“…Concerning the former approach, one of the exploited computational techniques is the LSQR iterative algorithm, to solve large, ill-posed, overdetermined, and possibly sparse systems of equations (Paige and Saunders, 1982a,b). This algorithm is employed in several contexts, such as medicine (Bin et al, 2020;Guo et al, 2021), geophysics (Joulidehsar et al, 2018;Liang et al, 2019a,b), geodesy (Baur and Austen, 2005), industry (Jaffri et al, 2020), and astronomy (Borriello et al, 1986;Van der Marel, 1988;Naghibzadeh and van der Veen, 2017;Becciani et al, 2014;Cesare et al, 2021Cesare et al, , 2022c. For a more in-depth discussion about the LSQR algorithm and other LSQR-based applications and libraries, see Section 2 of Cesare et al (2022b).…”