|NSECT | GSECT | Quant CT/Tomo | Dual Energy | Chest Tomo | Breast Tomo | Breast Density|
|Quant. Image | Emerg. Quant. Imaging | Perf. Metrology | Clinical Trials | Emerg. Clinical
Quantitative CT and tomosynthesis
Currently, most modeling metrics for diagnostic imaging are employed to investigate detection performance. However, it is clear that the clinical imaging tasks often encompass other types of tasks. As illustrated in Fig. 1 the clinical imaging tasks can be divided into detection tasks, discrimination tasks, and estimations tasks. An example of a detection task would be the task to determine whether a lesion is present or not, a discrimination task would be that to determine whether that lesion is malignant or benign, and an estimation tasks would be that to determine its size. As shown in the diagram, it is clear that there is some overlap between these tasks. For example, it would be difficult to estimate the size of a lesion without the ability to detect it. It should also be noted that within the context of this proposal, quantitative imaging falls mostly under the category of estimation tasks.
Currently there is a void in terms of metrics developed specifically for predicting performance in terms of estimation tasks for quantitative imaging. Several useful metrics have been developed for assessing and modeling imaging device performance. Examples include signal-to-noise ratio (SNR) and noise equivalent quanta (NEQ) for image quality or detective quantum efficiency (DQE) for system efficiency. These metrics provide powerful tools for system design and evaluation, and in general, are expected to correlate with quantitative imaging performance. However, they don’t address any specific task for quantitative imaging.
Volumetric breast imaging holds great potential for quantitative imaging for several reasons, including: 1) high–spatial resolution, which makes it ideal for determining distances, areas, and volumes; and 2) estimation of linear x-ray attenuation (e.g., Hounsfeld units), which enables tissue characterization (2, 3). Quantitative information obtained from volumetric breast imaging could offer valuable information for improved diagnosis and treatment performance.
The system was used to investigate detection and estimation performance for a small spherical target. The detection performance is plotted in Fig. 3(a) as a function of acquisition angle. Note that “acquisition angle” denotes the total angle spanned by the tube during acquisition. The detection was optimal at an acquisition angle of ~85 degrees, where reconstructed images using a smaller acquisition angle exhibited increased anatomical noise and reconstructed images using a larger acquisition angle exhibited increased quantum and electronic noise. In comparison, performance of size estimation is plotted in terms of precision (i.e., the inverse of the MSE scaled to arbitrary units) [Fig. 3(b)]. Volume estimation is maximized between 100° and 125°. Furthermore, precision for the localization in 3D is shown in Fig. 3(c). Localization in the x- and in the y-direction (i.e., localization in the coronal plane) exhibited similar trends as a function of acquisition angle, whereas precision in the z-direction was significantly lower and required a larger angle to achieve optimal performance (~165°). Overall, precision for a localization task was found to require a larger acquisition angle compared to both detection and size estimation tasks. This is expected as larger acquisition angles provide better three-dimensional definition of the anatomy - demonstrating the task-dependent optimization that needs to be accounted for when investigating advanced imaging systems.
This ongoing research suggests that the ML estimator can provide a useful metric for assessing estimation task performance - identifying clear optima for system performance. Larger angles are required for estimation and localization tasks compared to detection task. These observations point to tomographic imaging techniques with larger acquisition angles than currently employed in breast tomosynthesis and points to further investigation for other optimal acquisition and reconstruction techniques that may also depend on imaging task.
Applications to chest computed tomography:
For example, the precision for estimating the volume of 8 mm nodule inserted in a chest phantom (fig. 5) was investigated as a function of kVp [80, 100, 120, and 140 kVp] and pitch [20, 40, and 55 mm] while holding the CTDI fixed. We generated volume estimates from a clinically available segmentation program. As shown in fig. 6, precision was found to have little dependence on kVp but increases with pitch. Other factors such as slice thickness and reconstruction parameters are currently being investigated as important factors for quantitative imaging performance.