Collimator optimization in SPECT based on an LROC ideal observer

In SPECT the collimator is a crucial element of the imaging chain and controls the noise-resolution tradeoff of the collected data. Optimizing collimator design has been a long studied topic, with many different criteria used to evaluate the design. One class of criteria is tasked-based, in which the collimator is designed to optimize detection of a signal (lesion). Here we consider a new, more realistic, task, the joint detection and localization of a signal. Furthermore, we use an ideal observer - one that attains a theoretically maximum task performance - to optimize collimator design. The ideal observer operates on the sinogram data. We consider a family of parallel-hole low-energy collimators of varying resolution and efficiency and optimize over this set. We observe that for a 2-D object characterized by noise due to background variability and a sinogram with photon noise, the optimal collimator tends to of lower resolution and higher efficiency than equivalent commercial collimators. Furthermore, this optimal design is insensitive to the tolerance radius within which the signal must be localized. So for this scenario, the addition of a localization task does not change the optimal collimator. Optimal collimator resolution gets worse as signal size grows, and improves as the level of background variability noise increases. These latter two trends are also observed when the detection task is signal-known-exactly and background variable.

[1]  H H Barrett,et al.  Effect of random background inhomogeneity on observer detection performance. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[2]  Anand Rangarajan,et al.  An accelerated convergent ordered subsets algorithm for emission tomography , 2004, Physics in medicine and biology.

[3]  Matthew A Kupinski,et al.  Experimental task-based optimization of a four-camera variable-pinhole small-animal SPECT system , 2005, SPIE Medical Imaging.

[4]  Gengsheng L. Zeng,et al.  A channelized hotelling trace collimator design method based on reconstruction rather than projections , 2001 .

[5]  Bin Liu,et al.  On ideal AFROC and FROC observers , 2009, Medical Imaging.

[6]  Georges El Fakhri,et al.  Collimator optimization for detection and quantitation tasks: application to gallium-67 imaging , 2005, IEEE Transactions on Medical Imaging.

[7]  M F Kijewski,et al.  Collimator optimization for lesion detection incorporating prior information about lesion size. , 1995, Medical physics.

[8]  Gene Gindi,et al.  SPECT image system optimization using ideal observer for detection and localization , 2008, SPIE Medical Imaging.

[9]  Robert M Lewitt,et al.  Small nodule detectability evaluation using a generalized scan-statistic model , 2006, Physics in medicine and biology.

[10]  Xin He,et al.  Toward Realistic and Practical Ideal Observer (IO) Estimation for the Optimization of Medical Imaging Systems , 2008, IEEE Transactions on Medical Imaging.

[11]  R. F. Wagner,et al.  Aperture optimization for emission imaging: effect of a spatially varying background. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[12]  P.P. Bruyant,et al.  Numerical observer study of MAP-OSEM regularization methods with anatomical priors for lesion detection in /sup 67/Ga images , 2002, IEEE Transactions on Nuclear Science.

[13]  Anand Rangarajan,et al.  Aperture optimization in emission imaging using ideal observers for joint detection and localization. , 2008, Physics in medicine and biology.

[14]  Jannick P. Rolland,et al.  Linear discriminants and image quality , 1991, Image Vis. Comput..

[15]  R. Swensson Unified measurement of observer performance in detecting and localizing target objects on images. , 1996, Medical physics.

[16]  H. Barrett,et al.  Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Eric Clarkson,et al.  Efficiency of the human observer detecting random signals in random backgrounds. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  M. King,et al.  Impact of mismatched detector-blur models on 67Ga SPECT tumor detection , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[19]  C E Metz,et al.  A comparison of optimum detector spatial resolution in nuclear imaging based on statistical theory and on observer performance. , 1978, Physics in medicine and biology.

[20]  M. Wernick,et al.  Emission Tomography: The Fundamentals of PET and SPECT , 2004 .

[21]  Harrison H. Barrett,et al.  Hotelling trace criterion as a figure of merit for the optimization of imaging systems , 1986 .

[22]  G. Zeng,et al.  High-sensitivity SPECT imaging using large collimator holes and geometric blurring compensation , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[23]  Harrison H Barrett,et al.  Validating the use of channels to estimate the ideal linear observer. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[24]  G T Gullberg,et al.  Iterative reconstruction of fluorine-18 SPECT using geometric point response correction. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[25]  Gene Gindi,et al.  Decision strategies maximizing the area under the LROC curve , 2005, SPIE Medical Imaging.

[26]  Harrison H. Barrett,et al.  Foundations of Image Science , 2003, J. Electronic Imaging.

[27]  Donald L. Gunter CHAPTER 8 – Collimator Design for Nuclear Medicine , 2004 .