Integration of high velocity test object motion into a channelized Hotelling observer for the assessment of x-ray angiography systems

Abstract

Assessment of x-ray angiography system performance is typically performed using stationary test objects with simple geometries such as a disk on a uniform background. However, these methods do not represent realistic imaging conditions in interventional cardiology as anatomy and devices are inherently non-stationary due to cardiac motion. In this work, a novel implementation of the channelized Hotelling observer (CHO) was used to assess the influence of motion blur on object detectability. A standard CHO model assumes imaging system stationarity whereby the detectability index of a test object is independent of location. However, real angiography systems are inherently non-stationary. While vendor correction gain factors and offset maps are used to compensate for visual non-uniformities, these corrections do not restore stationarity to the images. Methods to accommodate non-stationarity and allow assessment of the influence of motion blur on test object detectability will be presented. The effect of motion blur was quantified with the relative detectability index (), where the for an object when moving with constant linear velocity was compared to a low velocity ‘pseudo-stationary’ condition to account for system non-stationarity. The pseudo-stationary condition was used to isolate the influences of spatial non-stationarity and motion blur. Three different test object shapes (disks, spheres and capsules) with linear velocity in the range 0–30 cm · s−1 were tested. For 1 mm diameter objects and linear velocity 30 cm · s−1, was degraded by 37%, 33% and 42% for the disk, sphere and capsule respectively, relative to the pseudo-stationary condition. Considering all test objects with diameter greater than 2 mm and linear velocity 30 cm · s−1, was degraded by less than 10% due to motion. In summary, this work describes a new approach to assess performance of x-ray angiography systems using the CHO model and moving test objects.

Task‐specific efficient channel selection and bias management for Gabor function channelized Hotelling observer model for the assessment of x‐ray angiography system performance

“…For example, rotationally symmetric channels such as LG, Gaussian, or difference of Gaussian channels may have been more efficient for rotationally symmetric test objects. However, such channels were not investigated in this work owing to our desire to develop methods which are also applicable to rotating asymmetric test objects 18 …”

“…Additionally, that work introduced asymmetric test objects. These previous works [16][17][18] were performed with both static and moving test objects (symmetric and asymmetric), but upon uniform-exposure backgrounds and using unprocessed images. A long-term goal of this line of work is to estimate d 0 for symmetric and asymmetric test objects moving upon nonuniform anthropomorphic phantom backgrounds.…”

“…Many of those studies worked with simulated data which, despite providing advantages regarding knowledge of system design and its behavior, may not fully represent real‐world imaging conditions 3,4,6,8–15 . Other works used experimentally acquired images to assess the performance of the imaging system either by means of a channelized Hotelling observer (CHO) model 16–18 or other methods 2,5,7 …”

“…In that work, bias due to TVNS noise was estimated by application of the CHO test methods to two sets of signal absent images and was assumed to be additive to d ′ of the test object in a linear manner 17 . Next, Tao and Fetterly 18 extended the Gabor CHO model and bias correction strategies to evaluate angiographic system performance using moving test objects on a nominally uniform‐exposure background. That work demonstrated correlation between spatiotemporal nonstationarity and the rotational speed of the test objects.…”

“…These observer models overcome challenges of traditional image quality metrics by providing a single figure of merit (FOM) which takes into account an imaging task of interest and essential image quality features such as spatial resolution, contrast, and noise magnitude and texture. In the case of fluoroscopic and angiographic imaging systems, multiple authors have used observer models to assess the performance of these systems under a variety of imaging conditions 2–18 . Many of those studies worked with simulated data which, despite providing advantages regarding knowledge of system design and its behavior, may not fully represent real‐world imaging conditions 3,4,6,8–15 .…”