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Author |
Cabello, J.; Torres-Espallardo, I.; Gillam, J.E.; Rafecas, M. |
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Title |
PET Reconstruction From Truncated Projections Using Total-Variation Regularization for Hadron Therapy Monitoring |
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Journal Article |
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Year |
2013 |
Publication |
IEEE Transactions on Nuclear Science |
Abbreviated Journal |
IEEE Trans. Nucl. Sci. |
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Volume |
60 |
Issue |
5 |
Pages |
3364-3372 |
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Abstract |
Hadron therapy exploits the properties of ion beams to treat tumors by maximizing the dose released to the target and sparing healthy tissue. With hadron beams, the dose distribution shows a relatively low entrance dose which rises sharply at the end of the range, providing the characteristic Bragg peak that drops quickly thereafter. It is of critical importance in order not to damage surrounding healthy tissues and/or avoid targeting underdosage to know where the delivered dose profile ends-the location of the Bragg peak. During hadron therapy, short-lived beta(+)-emitters are produced along the beam path, their distribution being correlated with the delivered dose. Following positron annihilation, two photons are emitted, which can be detected using a positron emission tomography (PET) scanner. The low yield of emitters, their short half-life, and the wash out from the target region make the use of PET, even only a few minutes after hadron irradiation, a challenging application. In-beam PET represents a potential candidate to estimate the distribution of beta(+)-emitters during or immediately after irradiation, at the cost of truncation effects and degraded image quality due to the partial rings required of the PET scanner. Time-of-flight (ToF) information can potentially be used to compensate for truncation effects and to enhance image contrast. However, the highly demanding timing performance required in ToF-PET makes this option costly. Alternatively, the use of maximum-a-posteriori-expectation-maximization (MAP-EM), including total variation (TV) in the cost function, produces images with low noise, while preserving spatial resolution. In this paper, we compare data reconstructed with maximum-likelihood-expectation-maximization (ML-EM) and MAP-EM using TV as prior, and the impact of including ToF information, from data acquired with a complete and a partial-ring PET scanner, of simulated hadron beams interacting with a polymethyl methacrylate (PMMA) target. The results show that MAP-EM, in the absence of ToF information, produces lower noise images and more similar data compared to the simulated beta(+) distributions than ML-EM with ToF information in the order of 200-600 ps. The investigation is extended to the combination of MAP-EM and ToF information to study the limit of performance using both approaches. |
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0018-9499 |
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WOS:000325827200023 |
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no |
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yes |
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yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1610 |
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Author |
Oliver, J.F.; Fuster-Garcia, E.; Cabello, J.; Tortajada, S.; Rafecas, M. |
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Title |
Application of Artificial Neural Network for Reducing Random Coincidences in PET |
Type |
Journal Article |
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Year |
2013 |
Publication |
IEEE Transactions on Nuclear Science |
Abbreviated Journal |
IEEE Trans. Nucl. Sci. |
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Volume |
60 |
Issue |
5 |
Pages |
3399-3409 |
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Abstract |
Positron Emission Tomography (PET) is based on the detection in coincidence of the two photons created in a positron annihilation. In conventional PET, this coincidence identification is usually carried out through a coincidence electronic unit. An accidental coincidence occurs when two photons arising from different annihilations are classified as a coincidence. Accidental coincidences are one of the main sources of image degradation in PET. Some novel systems allow coincidences to be selected post-acquisition in software, or in real time through a digital coincidence engine in an FPGA. These approaches provide the user with extra flexibility in the sorting process and allow the application of alternative coincidence sorting procedures. In this work a novel sorting procedure based on Artificial Neural Network (ANN) techniques has been developed. It has been compared to a conventional coincidence sorting algorithm based on a time coincidence window. The data have been obtained from Monte-Carlo simulations. A small animal PET scanner has been implemented to this end. The efficiency (the ratio of correct identifications) can be selected for both methods. In one case by changing the actual value of the coincidence window used, and in the other by changing a threshold at the output of the neural network. At matched efficiencies, the ANN-based method always produces a sorted output with a smaller random fraction. In addition, two differential trends are found: the conventional method presents a maximum achievable efficiency, while the ANN-based method is able to increase the efficiency up to unity, the ideal value, at the cost of increasing the random fraction. Images reconstructed using ANN sorted data (no compensation for randoms) present better contrast, and those image features which are more affected by randoms are enhanced. For the image quality phantom used in the paper, the ANN method decreases the spill-over ratio by a factor of 18%. |
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0018-9499 |
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WOS:000325827200027 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1611 |
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Author |
Aguiar, P.; Rafecas, M.; Ortuño, J.E.; Kontaxakis, G.; Santos, A.; Pavia, J.; Rosetti, M. |
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Title |
Geometrical and Monte Carlo projectors in 3D PET reconstruction |
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Journal Article |
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Year |
2010 |
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Medical Physics |
Abbreviated Journal |
Med. Phys. |
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37 |
Issue |
11 |
Pages |
5691-5702 |
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Keywords |
3D PET; iterative reconstruction; list-mode reconstruction; ray-tracing techniques; Monte Carlo simulation; system response matrix |
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Abstract |
Purpose: In the present work, the authors compare geometrical and Monte Carlo projectors in detail. The geometrical projectors considered were the conventional geometrical Siddon ray-tracer (S-RT) and the orthogonal distance-based ray-tracer (OD-RT), based on computing the orthogonal distance from the center of image voxel to the line-of-response. A comparison of these geometrical projectors was performed using different point spread function (PSF) models. The Monte Carlo-based method under consideration involves an extensive model of the system response matrix based on Monte Carlo simulations and is computed off-line and stored on disk. Methods: Comparisons were performed using simulated and experimental data of the commercial small animal PET scanner rPET. Results: The results demonstrate that the orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions yield better images in terms of contrast and spatial resolution than those obtained after using the conventional method and the multiray-based S-RT. Furthermore, the Monte Carlo-based method yields slight improvements in terms of contrast and spatial resolution with respect to these geometrical projectors. Conclusions: The orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions represent satisfactory alternatives to factorizing the system matrix or to the conventional on-the-fly ray-tracing methods for list-mode reconstruction, where an extensive modeling based on Monte Carlo simulations is unfeasible. |
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Address |
[Aguiar, Pablo] Univ Santiago de Compostela, Dept Fis Particulas, Complexo Hosp Univ Santiago de Compostela, Fdn IDICHUS IDIS, Santiago De Compostela, Spain, Email: pablo.aguiar.fernandez@sergas.es |
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Amer Assoc Physicists Medicine Amer Inst Physics |
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English |
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0094-2405 |
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ISI:000283747600015 |
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no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ elepoucu @ |
Serial |
338 |
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Author |
Blume, M.; Martinez-Moller, A.; Keil, A.; Navab, N.; Rafecas, M. |
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Title |
Joint Reconstruction of Image and Motion in Gated Positron Emission Tomography |
Type |
Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
IEEE Trans. Med. Imaging |
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Volume |
29 |
Issue |
11 |
Pages |
1892-1906 |
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Keywords |
Gating; motion compensation; positron emission tomography (PET); reconstruction |
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Abstract |
We present a novel intrinsic method for joint reconstruction of both image and motion in positron emission tomography (PET). Intrinsic motion compensation methods exclusively work on the measured data, without any external motion measurements. Most of these methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually reconstructed gates. Then, the image is estimated based on this motion information. With these methods, a main problem lies in the motion estimation step, which is based on the noisy gated frames. The more noise is present, the more inaccurate the image registration becomes. As we show both visually and quantitatively, joint reconstruction using a simple deformation field motion model can compete with state-of-the-art image registration methods which use robust multilevel B-spline motion models. |
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Address |
[Blume, Moritz; Rafecas, Magdalena] Univ Valencia, CSIC, IFIC, E-46071 Valencia, Spain, Email: moritz.blume@cs.tum.edu |
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Ieee-Inst Electrical Electronics Engineers Inc |
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English |
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ISSN |
0278-0062 |
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Notes |
ISI:000283941800007 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ elepoucu @ |
Serial |
340 |
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Author |
Blume, M.; Navab, N.; Rafecas, M. |
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Title |
Joint image and motion reconstruction for PET using a B-spline motion model |
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Journal Article |
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Year |
2012 |
Publication |
Physics in Medicine and Biology |
Abbreviated Journal |
Phys. Med. Biol. |
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57 |
Issue |
24 |
Pages |
22pp |
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Abstract |
We present a novel joint image and motion reconstruction method for PET. The method is based on gated data and reconstructs an image together with amotion function. The motion function can be used to transform the reconstructed image to any of the input gates. All available events (from all gates) are used in the reconstruction. The presented method uses a B-spline motion model, together with a novel motion regularization procedure that does not need a regularization parameter (which is usually extremely difficult to adjust). Several image and motion grid levels are used in order to reduce the reconstruction time. In a simulation study, the presented method is compared to a recently proposed joint reconstruction method. While the presented method provides comparable reconstruction quality, it is much easier to use since no regularization parameter has to be chosen. Furthermore, since the B-spline discretization of the motion function depends on fewer parameters than a displacement field, the presented method is considerably faster and consumes less memory than its counterpart. The method is also applied to clinical data, for which a novel purely data-driven gating approach is presented. |
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Address |
[Blume, Moritz; Rafecas, Magdalena] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, E-46071 Valencia, Spain, Email: moritz.blume@fasterplan.com |
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Publisher |
Iop Publishing Ltd |
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English |
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ISSN |
0031-9155 |
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Notes |
WOS:000312106200009 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
1267 |
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