526 research outputs found
A multi-resolution image reconstruction method in X-ray computed tomography
International audienceWe propose a multiresolution X-ray imaging method designed for non-destructive testing/ evaluation (NDT/NDE) applications which can also be used for small animal imaging studies. Two sets of projections taken at different magnifications are combined and a multiresolution image is reconstructed. A geometrical relation is introduced in order to combine properly the two sets of data and the processing using wavelet transforms is described. The accuracy of the reconstruction procedure is verified through a comparison to the standard filtered backprojection (FBP) algorithm on simulated data
A Multiresolution Approach to Discrete Tomography Using DART
In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant spee
Dynamic CBCT Imaging using Prior Model-Free Spatiotemporal Implicit Neural Representation (PMF-STINR)
Dynamic cone-beam computed tomography (CBCT) can capture
high-spatial-resolution, time-varying images for motion monitoring, patient
setup, and adaptive planning of radiotherapy. However, dynamic CBCT
reconstruction is an extremely ill-posed spatiotemporal inverse problem, as
each CBCT volume in the dynamic sequence is only captured by one or a few X-ray
projections. We developed a machine learning-based technique, prior-model-free
spatiotemporal implicit neural representation (PMF-STINR), to reconstruct
dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a
joint image reconstruction and registration approach to address the
under-sampling challenge. Specifically, PMF-STINR uses spatial implicit neural
representation to reconstruct a reference CBCT volume, and it applies temporal
INR to represent the intra-scan dynamic motion with respect to the reference
CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a
learning-based B-spline motion model to capture time-varying deformable motion
during the reconstruction. Compared with previous methods, the spatial INR, the
temporal INR, and the B-spline model of PMF-STINR are all learned on the fly
during reconstruction in a one-shot fashion, without using any patient-specific
prior knowledge or motion sorting/binning. PMF-STINR was evaluated via digital
phantom simulations, physical phantom measurements, and a multi-institutional
patient dataset featuring various imaging protocols (half-fan/full-fan, full
sampling/sparse sampling, different energy and mAs settings, etc.). The results
showed that the one-shot learning-based PMF-STINR can accurately and robustly
reconstruct dynamic CBCTs and capture highly irregular motion with high
temporal (~0.1s) resolution and sub-millimeter accuracy. It can be a promising
tool for motion management by offering richer motion information than
traditional 4D-CBCTs
Recommended from our members
Mathematical Methods in Tomography
This is the seventh Oberwolfach conference on the mathematics of tomography, the first one taking place in 1980. Tomography is the most popular of a series of medical and scientific imaging techniques that have been developed since the mid seventies of the last century
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
- …