Geometry Analysis of an Inverse-Geometry Volumetric CT System With Multiple Detector Arrays

An inverse-geometry volumetric CT (IGCT) system for imaging in a single fast rotation without cone-beam artifacts is being developed. It employs a large scanned source array and a smaller detector array. For a single-source/single-detector implementation, the FOV is limited to a fraction of the source size. Here we explore options to increase the FOV without increasing the source size by using multiple detectors spaced apart laterally to increase the range of radial distances sampled. We also look at multiple source array systems for faster scans. To properly reconstruct the FOV, Radon space must be sufficiently covered and sampled in a uniform manner. Optimal placement of the detectors relative to the source was determined analytically given system constraints (5cm detector width, 25cm source width, 45cm source-to-isocenter distance). For a 1x3 system (three detectors per source) detector spacing (DS) was 18deg and source-to-detector distances (SDD) were 113, 100 and 113cm to provide optimum Radon sampling and a FOV of 44cm. For multiple-source systems, maximum angular spacing between sources cannot exceed 125deg since detectors corresponding to one source cannot be occluded by a second source. Therefore, for 2x3 and 3x3 systems using the above DS and SDD, optimum spacing between sources is 115deg and 61deg respectively, requiring minimum scan rotations of 115deg and 107deg. Also, a 3x3 system can be much faster for full 360deg dataset scans than a 2x3 system (120deg vs. 245deg). We found that a significantly increased FOV can be achieved while maintaining uniform radial sampling as well as a substantial reduction in scan time using several different geometries. Further multi-parameter optimization is underway

Quantifying admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, investigated in compressive sensing (CS) claim potentially large reductions in sampling requirements. Quantifying this claim for computed tomography (CT) is non-trivial, because both full sampling in the discrete-to-discrete imaging model and the reduction in sampling admitted by sparsity-exploiting methods are ill-defined. The present article proposes definitions of full sampling by introducing four sufficient-sampling conditions (SSCs). The SSCs are based on the condition number of the system matrix of a linear imaging model and address invertibility and stability. In the example application of breast CT, the SSCs are used as reference points of full sampling for quantifying the undersampling admitted by reconstruction through TV-minimization. In numerical simulations, factors affecting admissible undersampling are studied. Differences between few-view and few-detector bin reconstruction as well as a relation between object sparsity and admitted undersampling are quantified.Comment: Revised version that was submitted to IEEE Transactions on Medical Imaging on 8/16/201

High resolution image reconstruction with constrained, total-variation minimization

This work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling rate. Such systems pose a challenge for iterative image reconstruction, because a very fine image grid is needed to realize the resolution inherent in such scanners. These image arrays lead to under-determined imaging models whose inversion is unstable and can result in undesirable artifacts and noise patterns. There are many possibilities to stabilize the imaging model, and this work proposes a method which may have an advantage in terms of algorithm efficiency. The proposed method introduces additional constraints in the optimization problem; these constraints set to zero high spatial frequency components which are beyond the sensing capability of the detector. The method is demonstrated with an actual CT data set and compared with another method based on projection up-sampling.Comment: This manuscript appears in the proceedings of the 2010 IEEE medical imaging conferenc

Comparison of imaging geometries for diffuse optical tomography of tissue

Images produced in six different geometries with diffuse optical tomography simulations of tissue have been compared using a finite element-based algorithm with iterative refinement provided by the NewtonRaphson approach. The source-detector arrangements studied include (i) fan-beam tomography, (ii) full reflectance and transmittance tomography, as well as (iii) sub-surface imaging, where each of these three were examined in a circular and a flat slab geometry. The algorithm can provide quantitatively accurate results for all of the tomographic geometries investigated under certain circumstances. For example, quantitatively accurate results occur with sub-surface imaging only when the object to be imaged is fully contained within the diffuse projections. In general the diffuse projections must sample all regions around the target to be characterized in order for the algorithm to recover quantitatively accurate results. Not only is it important to sample the whole space, but maximal angular sampling is required for optimal image reconstruction. Geometries which do not maximize the possible sampling angles cause more noise artifact in the reconstructed images. Preliminary simulations using a mesh of the human brain confirm that optimal images are produced from circularly symmetric source-detector distributions, but that quantitatively accurate images can be reconstructed even with. a sub-surface imaging, although spatial resolution is modest. © 1999 Optical Society of America