2 research outputs found
Simultaneous Reconstruction and Segmentation for Dynamic SPECT Imaging
This work deals with the reconstruction of dynamic images that incorporate
characteristic dynamics in certain subregions, as arising for the kinetics of
many tracers in emission tomography (SPECT, PET). We make use of a basis
function approach for the unknown tracer concentration by assuming that the
region of interest can be divided into subregions with spatially constant
concentration curves. Applying a regularized variational framework reminiscent
of the Chan-Vese model for image segmentation we simultaneously reconstruct
both the labelling functions of the subregions as well as the subconcentrations
within each region. Our particular focus is on applications in SPECT with
Poisson noise model, resulting in a Kullback-Leibler data fidelity in the
variational approach.
We present a detailed analysis of the proposed variational model and prove
existence of minimizers as well as error estimates. The latter apply to a more
general class of problems and generalize existing results in literature since
we deal with a nonlinear forward operator and a nonquadratic data fidelity. A
computational algorithm based on alternating minimization and splitting
techniques is developed for the solution of the problem and tested on
appropriately designed synthetic data sets. For those we compare the results to
those of standard EM reconstructions and investigate the effects of Poisson
noise in the data
Dynamic PET cardiac and parametric image reconstruction: a fixed-point proximity gradient approach using patch-based DCT and tensor SVD regularization
Our aim was to enhance visual quality and quantitative accuracy of dynamic
positron emission tomography (PET)uptake images by improved image
reconstruction, using sophisticated sparse penalty models that incorporate both
2D spatial+1D temporal (3DT) information. We developed two new 3DT PET
reconstruction algorithms, incorporating different temporal and spatial
penalties based on discrete cosine transform (DCT)w/ patches, and tensor
nuclear norm (TNN) w/ patches, and compared to frame-by-frame methods;
conventional 2D ordered subsets expectation maximization (OSEM) w/
post-filtering and 2D-DCT and 2D-TNN. A 3DT brain phantom with kinetic uptake
(2-tissue model), and a moving 3DT cardiac/lung phantom was simulated and
reconstructed. For the cardiac/lung phantom, an additional cardiac gated
2D-OSEM set was reconstructed. The structural similarity index (SSIM) and
relative root mean squared error (rRMSE) relative ground truth was
investigated. The image derived left ventricular (LV) volume for the
cardiac/lung images was found by region growing and parametric images of the
brain phantom were calculated. For the cardiac/lung phantom, 3DT-TNN yielded
optimal images, and 3DT-DCT was best for the brain phantom. The optimal LV
volume from the 3DT-TNN images was on average 11 and 55 percentage points
closer to the true value compared to cardiac gated 2D-OSEM and 2D-OSEM
respectively. Compared to 2D-OSEM, parametric images based on 3DT-DCT images
generally had smaller bias and higher SSIM. Our novel methods that incorporate
both 2D spatial and 1D temporal penalties produced dynamic PET images of higher
quality than conventional 2D methods, w/o need for post-filtering. Breathing
and cardiac motion were simultaneously captured w/o need for respiratory or
cardiac gating. LV volumes were better recovered, and subsequently fitted
parametric images were generally less biased and of higher quality.Comment: 11 pages, 12 figure