Assessment of a fluorescence-enhanced optical imaging system using the Hotelling observer

Abstract

This study represents a first attempt to assess the detection capability of a fluorescence-enhanced optical imaging system as quantified by the Hotelling observer. The imaging system is simulated by the diffusion approximation of the time-dependent radiative transfer equation, which describes near infra-red (NIR) light propagation through a breast phantom of clinically relevant volume. Random structures in the background are introduced using a lumpy-object model as a representation of anatomical structure as well as non-uniform distribution of disease markers. The systematic errors and noise associated with the actual experimental conditions are incorporated into the simulated boundary measurements to acquire imaging data sets. A large number of imaging data sets is considered in order to perform Hotelling observer studies. We find that the signal-to-noise ratio (SNR) of Hotelling observer (i) decreases as the strength of lumpy perturbations in the background increases, (ii) decreases as the target depth increases, and (iii) increases as excitation light leakage decreases, and reaches a maximum for filter optical density values of 5 or higher.

Experimental Comparison of Continuous-Wave and Frequency-Domain Fluorescence Tomography in a Commercial Multi-Modal Scanner

“…The influence of heterogeneous endogenous properties on CW measurements used for image reconstruction has been reported with a number of literature investigations that seek to independently ascribe endogenous tissue optical properties with hybrid anatomical CT/MRI or even DOT measurements [25]. Whether the addition of FD measurements approaches can completely alleviate the need to account for the presence of highly light absorptive structures such as the liver, remains to be determined, but the data presented herein agrees with past work [54], [55] suggesting the use of time-dependent measurements as a means to reduce the impact of endogenous properties on image reconstruction. We have additionally made the assumption that the endogenous optical properties remain constant between the 50 nm Stoke's shift, which does not appear to deteriorate reconstruction results.…”

“…Although it has long been postulated that time-dependent fluorescent measurements provide more information than CW measurements and that they can facilitate model-based reconstruction of [18], [19], [54], [55], experimental assessment has been limited because of the inability to conduct both types of measurements within the same imaging system. Herein, we took advantage of a hybrid fluorescence/CT system we have previously reported upon and used it to conduct both FD and CW measurements in well-defined phantoms.…”

“…Because CW measurements of intensity are attenuated by scattering and nonfluorescent absorption cross sections more than FD measurements [18], [19], [54], [55], we compared reconstructions of from FD and CW measurements as the input parameters of and were varied by 20% and 60% from their experimentally measured values. In addition, we also tested the and DA reconstruction algorithms on CW and FD data acquired on the phantom with three high absorption rods [ Fig.…”

Generation of anatomically realistic numerical phantoms for photoacoustic and ultrasonic breast imaging

“…In addition to the PACT validation study performed in this paper, another example is the computation of task-based measures. It is advocated in the modern imaging science literature to utilize task-based measures of imaging system performance to guide the optimization of system design and image reconstruction algorithms 18,[42][43][44] to produce images that are not simply visually attractive, but are the most informative with respect to the diagnostic task at hand. For the computed task-based measures to be informative not only for a specific subject but also for a general patient population, the structural variations in breasts across different patients need to be taken into consideration.…”

Bias in Hotelling observer performance computed from finite data

“…We represent the imaging process by the equation, (1) where g is the image data, f(r) is the object being imaged as a function of position r, n is the detector noise, and ℋ represents the continuous-to-discrete imaging system. Bold-faced symbols denote vectors.…”