Multimodality imaging system

Details Multimodality imaging system Multimodality imaging system

2011-01-04

2011-01-04

Porta, David P (Department: 2884)

Radiant energy

Invisible radiant energy responsive electric signalling

With or including a luminophor

Reexamination Certificate

active

07863574

ABSTRACT:
A system and method is provided for performing multimodal imaging of an object. The system and method includes performing spatio-temporal detection of transmission CT data of a fan X-ray beam, performing, and simultaneously with the spatio-temporal detection of transmission CT data, spatio-temporal detection of emission nuclear imaging data emitted from the object with a propagation direction across the propagation direction of the fan X-ray beam. The system and method further includes identifying at least two zones in the object based on the transmission CT data, reconstructing an image object from the emission nuclear imaging data under the constraint that respective portions of detected nuclear events are associated with selected zones, and outputting data representative of the image object.

REFERENCES:
patent: 5155365 (1992-10-01), Cann et al.
patent: 6399951 (2002-06-01), Paulis et al.
patent: 6628743 (2003-09-01), Drummond et al.
patent: 6670614 (2003-12-01), Plut et al.
patent: 6956925 (2005-10-01), Hoffman
patent: 2006/0237652 (2006-10-01), Kimchy et al.
patent: 2007/0050908 (2007-03-01), Kogan et al.
patent: 2008/0103391 (2008-05-01), Dos Santos Varela
patent: 2009/0264753 (2009-10-01), von Schulthess et al.
Beyer et al., “A combined PET/CT Scanner for clinical oncology,” 2000, The Journal of Nuclear Medicine, vol. 41, No. 8, pp. 1369-1379.
Shieh, et al., “Iterative image reconstruction using prior knowledge,” J Opt Soc Am A Opt Image Sci Vis, vol. 23, No. 6, pp. 1292-1300, Jun. 2006.
Adluru, et al., “Temporally constrained reconstruction of dynamic cardiac perfusion MRI”, Magn. Reson. Med., vol. 57, No. 6, pp. 1027-1036, (Jun. 2007).
Hudson, et al., “Accelerated image reconstruction using ordered subsets of projection data”, IEEE Trans. Med. Imaging, vol. 13, No. 4, pp. 601-609 (1994).
Sureau, et al. “Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tornograph,” J Nucl Med, vol. 49, No. 6, pp. 1000-1008, Jun. 1, 2008.
Alenius, et al.., “Generalization of median root prior reconstruction,” IEEE Trans. Med. Imaging, vol. 21, No. 11, pp. 1413-1420, Nov. 2002.
Hsu, C.., “A prior image model with mixed continuity constraints for Bayesian PET image reconstruction,” Nuclear Science Symposium, 1999, Conference Record, 1999 IEEE, vol. 3, pp. 1428-1432, Seattle, Washington, Oct. 1999.
Gindi, G., et al., “Bayesian reconstruction of functional images using anatomical information as priors”, IEEE Trans. Med. Imaging, vol. 12, No. 4, pp. 670-680, 1993.
Zhenyu, et al., “Approximate maximum likelihood hyper-parameter estimation for Gibbs priors.” Image Processing, IEEE Transactions on, vol. 6, No. 6, pp. 844-861, 1997.
Reutter, et al., “Direct least-squares estimation of spatiotemporal distributions from dynamic SPECT projections using a spatial segmentation and temporal B-splines,” Medical Imaging, IEEE Transactions on, vol. 19, No. 5, pp. 434-450, 2000.
Parra, et al., “List-mode likelihood: EM algorithm and image quality estimation demonstrated on 2-D PET,” Medical Imaging, IEEE Transactions on, vol. 17, No. 2, pp. 228-235, 1998.
Nuyts, et al. “Simultaneous maximum a posteriori reconstruction of attenuation and activity distributions from emission sinograms,” Medical Imaging, IEEE Transactions on 5 vol. 18, No. 5. pp. 393-403, 1999.
Hero, et al., “Information analysis of single photon emission computed tomography with count losses”, Medical Imaging 9, IEEE Transactions, pp. 117-127 (1990).
Kadrmas, et al., “Maximum a posteriori algorithm for the reconstruction of dynamic SPECT data”, IEEE Nuclear Science Symposium and Medical Imaging Conference, vol. 3, pp. 2086-2089, Nov. 8-14, 1998.
Kawata, et al., “Constrained Iterative Reconstruction by the Conjugate Gradient Method” IEEE Trans. Med. Imaging, vol. 4, No. 2, pp. 65-71, 1985.
LaRiviere, et al., “Nonparametric regression sonogram smoothing using a roughness-penalized Poisson likelihood objective function”, IEEE Trans. Med. Imaging, vol. 19, No. 8, pp. 773-786, 2000.
Chiao, et al., “Model-based estimation for dynamic cardiac studies using ECT”, IEEE Trans. Med. Imaging, vol. 13, No. 2, pp. 217-226, 1994.
Nichols, et al, “Spatiotemporal reconstruction of list-mode PET data,” Medical Imaging, IEEE Transactions on, vol. 21, No. 4, pp. 396-404, 2002.
Kadrmas, et al., “4D maximum a posteriori reconstruction in dynamic SPECT using a compartmental model-based prior,” Physics in Medicine and Biology, vol. 46, No. 5, pp. 1553-1574, 2001.
D. J. Kadrmas, “Statistically regulated and adaptive EM reconstruction for emission computed tomography,” Nuclear Science, IEEE Transactions on, vol. 48, No. 3, pp. 790-798, 2001.
Shao, L., et al., “Information gain from count correction in SPECT image reconstruction and classification”, IEEE Trans. Nucl. Science, vol. 37, No. 2, pp. 652-657, 1990.
Shieh, et al., “Image reconstruction: a unifying model for resolution enhancement and data extrapolation. Tutorial,” J Opt Soc Am A Opt Image Sci Vis, vol. 23, No. 2, pp. 258-266, Feb. 2006.
Wernick, et al., “Emission tomography: the fundamentals of PET and SPECT”, Chapters 7,11 and 21, Elsevier, Academic Press, 2004.
Galatsanos, et al., “Methods for Choosing the Regularization Parameter and Estimating the Noise Variance in Image Restoration and Their Relation”, IEEE Transactions on Image Processing, vol. 1, No. 3, pp. 322-336 (Jul. 1992).
Fessler, et al., “Space-alternating generalized expectation- maximization algorithm”, Signal Processing, IEEE T-SP 42 (10): 2664-77, (Oct. 1994).
Puetter, et al., “Digital Image reconstruction: deblurring and denoising”, Annu. Rev. Astro. Astrophys., 2005, 43:139-194.
Puetter, et al., “The pixon method of image reconstruction”, Astronomical Data Analysis Software and Systems VIII, Astronomical Society of the Pacific, Conference Series 1999, 172, 307-316.
Lehovich, et al., “Human observer LROC study of lesion detection in Ga-67 SPECT images reconstructed using MAP with anatomical priors”, 2006 IEEE nucl., Sci. Symp. Conf. Rec., 1699-702.
Lehovich, et al., “Choosing anatomical-prior strength for MAP SPECT reconstruction to maximize lesion detectability”, 2007 IEEE Nucl. Sci. Symp. Conf. Rec., 4222-5.
K.J. Mighell, “Parameter estimation in astronomy with Poisson-distributed data. II. The modified chi-square gamma statistic”, 2000, arXiv:astro-ph/0007328.
J. Hadamard, “Sur les problèmes aux dérivées partielles et leur signification physique”, Princeton Uni. Bull., 13, 49-52, (1902).
Tsui, B.M.W., et al., “Quantitative cardiac SPECT reconstruction with reduced image degradation due to patient anatomy”, IEEE Trans. Nuc. Sci., 41, 2838-44, 1994.
Gorman, J.D. and Hero, A.O., “Lower bounds for parametric estimation with constraints”, IEEE Trans. Information Theory, vol. 36, No. 6, pp. 1285-1301, 1990.
Siemens Medical, “Symbia SPECT Working For You”, Product Information, 2007.
Haacke, et al., “Constrained reconstruction: A super-resolution, optimal signal-to-noise alternative to the Fourier transform in magnetic resonance imaging”, Medical Physics, vol. 16, No. 3, pp. 388-397, 1989.
Halder, et al., “Anatomically constrained reconstruction from noisy data”, Magn. Reson. Med., vol. 59, No. 4, pp. 810-818, Apr. 2008.
Rangarajan, et al., “A model-based approach for filtering and edge detection in noisy images”, IEEE Trans. Circuits and Systems, vol. 37, No. 1, pp. 140-144, 1990.
Roggemann, et al., “Model-based image reconstruction by means of a constrained least-squares solution”, Appl Opt, vol. 36, No. 11, pp. 2360-2369, Apr. 10, 1997.
Roy, et al., “Three-d

Affiliated with

Also associated with

Rating

Multimodality imaging system does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Multimodality imaging system, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multimodality imaging system will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-2710638