A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors

Abstract

In emission tomography, anatomical side information, in the form of organ and lesion boundaries, derived from intra-patient coregistered CT or MR scans can be incorporated into the reconstruction. Our interest is in exploring the efficacy of such side information for lesion detectability. To assess detectability we used the SNR of a channelized Hotelling observer and a signal-known exactly/background-known exactly detection task. In simulation studies, we incorporated anatomical side information into a SPECT MAP (maximum a posteriori) reconstruction by smoothing within but not across organ or lesion boundaries. A non-anatomical prior was applied by uniform smoothing across the entire image. We investigated whether the use of anatomical priors with organ boundaries alone or with perfect lesion boundaries alone would change lesion detectability relative to the case of a prior with no anatomical information. Furthermore, we investigated whether any such detectability changes for the organ-boundary case would be a function of the distance of the lesion to the organ boundary. We also investigated whether any detectability changes for the lesion-boundary case would be a function of the degree of proximity, i.e. a difference in the radius of the true functional lesion and the radius of the anatomical lesion boundary. Our results showed almost no detectability difference with versus without organ boundaries at any lesion-to-organ boundary distance. Our results also showed no difference in lesion detectability with and without lesion boundaries, and no variation of lesion detectability with degree of proximity.

A Perceptually Relevant Channelized Joint Observer (PCJO) for the Detection-Localization of Parametric Signals

“…On the other hand, MOs [13], [14], [35]- [38] for the detection task compute a scalar test statistic via a discriminant function of the test image and they differ by their discriminant functions. The ideal observer (IO) [13] achieves the maximum AUC attainable amongst all the MOs, but requires full knowledge of the probability density functions (PDFs) of the image for each hypothesis (presence or absence of a signal) which are difficult to compute except for some special cases.…”

“…(12) and the validation of H h (detection decision) in Eq. (14), hence the name JDE. The JDE approach has a nonlinear estimation part (cf.…”

“…When estimation and detection tasks are combined, the overall procedure is termed a joint estimation-detection task. So far to our knowledge, numerical observers are limited in task range: most of them are dedicated to the detection task [10]- [14], much fewer are concerned with signal localization in images that contain at most one signal [15], none is concerned with signal localization in images that contain more than one signal, and none for the characterization task. In this paper, we propose a new numerical observer for the signal detection-localization in images that contain multiple signals.…”

Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems

“…For detection tasks and combined detection/estimation tasks, in which the observer must detect a signal and estimate parameters of the signal (e.g., contrast and size), the channelized Hotelling observer (CHO) [1][2][3][4][5][6][7][8][9][10][11] and the channelized scanning-linear observer (CSLO) 12 have successfully been utilized to measure image quality. To implement these two observers, first-order and second-order statistics are needed, which are typically estimated using sample images from a training dataset.…”

The use of mutual information and joint entropy for anatomical priors in emission tomography

“…M Any groups have studied the use of anatomical side information during emission reconstruction, aiming at improved image quality [1]- [10]. The side information is typically obtained from a spatially aligned MR or CT image.…”