Spectrotemporal CT data acquisition and reconstruction at low dose.

Clark, Darin P

Lee, Chang-Lung

Kirsch, David G

Badea, Cristian T

United States

2015-12-15T19:23:16Z

2015-11

PURPOSE: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D+dual energy+time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. METHODS: The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. RESULTS: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. CONCLUSIONS: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time.

http://www.ncbi.nlm.nih.gov/pubmed/26520724

0094-2405

https://hdl.handle.net/10161/11181

eng

Wiley

Med Phys

10.1118/1.4931407

Algorithms

Animals

Cardiac-Gated Imaging Techniques

Humans

Imaging, Three-Dimensional

Mice

Phantoms, Imaging

Radiation Dosage

Radiation Protection

Radiographic Image Enhancement

Radiographic Image Interpretation, Computer-Assisted

Reproducibility of Results

Sensitivity and Specificity

Spatio-Temporal Analysis

Tomography, X-Ray Computed

Spectrotemporal CT data acquisition and reconstruction at low dose.

Journal article

Lee, Chang-Lung|0000-0002-0673-633X

Badea, Cristian T|0000-0002-1850-2522

http://www.ncbi.nlm.nih.gov/pubmed/26520724

6317

6336

11

Basic Science Departments

Clinical Science Departments

Duke

Duke Cancer Institute

Institutes and Centers

Pharmacology & Cancer Biology

Radiation Oncology

Radiology

School of Medicine

Published

42