Statistical model based iterative reconstruction in clinical CT systems. Part III. Task-based kV/mAs optimization for radiation dose reduction

Toward a comparison and an optimization of CT protocols using new metrics of dose and image quality part I: prediction of human observers using a model observer for detection and discrimination tasks in low-dose CT images in various scanning conditions

In the context of reducing the patient dose coming from CT scanner examinations without penalizing the diagnosis, the assessment of both patient dose and image quality (IQ) with relevant metrics is crucial. The present study represents the first stage in a larger work, aiming to compare and optimize CT protocols using dose and IQ new metrics. We proposed here to evaluate the capacity of the Non-PreWhitening matched filter with an eye (NPWE) model observer to be a robust and accurate estimation of IQ. We focused our work on two types of clinical tasks: a low contrast detection task and a discrimination task. We designed a torso-shaped phantom, including Plastic Water^®slabs with cylindrical inserts of different diameters, sections and compositions. We led a human observer study with 13 human observers on images acquired in multiple irradiation and reconstruction scanning conditions (voltage, pitch, slice thickness, noise level of the reconstruction algorithm, energy level in dual-energy mode and dose), to evaluate the behavior of the model observer compared to the human responses faced to changing conditions. The model observer presented the same trends as the human observers with generally better results. We rescaled the NPWE model on the human responses by scanning conditions (kVp, pitch, slice thickness) to obtain the best agreement between both observer types, estimated using the Bland-Altman method. The impact of some scanning parameters was estimated using the correct answer rate given by the rescaled NPWE model, for both tasks and each insert size. In particular, the comparison between the dual-energy mode at 74 keV and the single-energy mode at 120 kVp showed that, if the 120 kVp voltage provided better results for the smallest insert at the lower doses for both tasks, their responses were equivalent in many cases.

Time-resolved C-arm cone beam CT angiography (TR-CBCTA) imaging from a single short-scan C-arm cone beam CT acquisition with intra-arterial contrast injection

Time-resolved C-arm cone-beam CT (CBCT) angiography (TR-CBCTA) images can be generated from a series of CBCT acquisitions that satisfy data sufficiency condition in analytical image reconstruction theory. In this work, a new technique was developed to generate TR-CBCTA images from a single short-scan CBCT data acquisition with contrast media injection. The reconstruction technique enabling this application is a previously developed image reconstruction technique, synchronized multi-artifact reduction with tomographic reconstruction (SMART-RECON). In this new application, the acquired short-scan CBCT projection data were sorted into a union of several sub-sectors of view angles and each sub-sector of view angles corresponds to an individual image volume to be reconstructed. The SMART-RECON method was then used to jointly reconstruct all of these individual image volumes under two constraints: (1) each individual image volume is maximally consistent with the measured cone-beam projection data within the corresponding view angle sector and (2) the nuclear norm of the image matrix is minimized. The difference between these reconstructed individual image volumes is used to generated the desired subtracted angiograms. To validate the technique, numerical simulation data generated from a fractal tree angiogram phantom were used to quantitatively study the accuracy of the proposed method and retrospective in vivo human subject studies were used to demonstrate the feasibility of generating TR-CBCTA in clinical practice.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization)

PURPOSE

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods.

METHODS

Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p_l and p_h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region and an anterior ROI, located further from the noise streaks region. Optimal results derived from the task-based detectability index metric were compared to other operating points in the parameter space with different noise and spatial resolution trade-offs.

RESULTS

The optimal operating points determined through the d' metric depended on the interplay between the major spatial frequency components of each imaging task and the highly shift-variant and anisotropic noise and spatial resolution properties associated with each operating point in the LSC parameter space. This interplay influenced imaging performance the most when the major spatial frequency component of a given imaging task coincided with the direction of spatial resolution loss or with the dominant noise spatial frequency component; this was the case of imaging task II. The performance of imaging tasks I and III was influenced by this interplay in a smaller scale than imaging task II, since the major frequency component of task I was perpendicular to imaging task II, and because imaging task III did not have strong directional dependence. For both LSC methods, there was a strong dependence of the overall d' magnitude and shape of the contours on the spatial location within the phantom, particularly for imaging tasks II and III. The d' value obtained at the optimal operating point for each spatial location and imaging task was similar when comparing the LSC methods studied in this work.

CONCLUSIONS

A local task-based detectability framework to optimize the selection of parameters for LSC methods was developed. The framework takes into account the potential shift-variant and anisotropic spatial resolution and noise properties to maximize the imaging performance of the CT system. Optimal parameters for a given LSC method depend strongly on the spatial location within the image object.

Assessment of structural similarity in CT using filtered backprojection and iterative reconstruction: a phantom study with 3D printed lung vessels

OBJECTIVE

To compare the performance of three generations of CT reconstruction techniques using structural similarity (SSIM) as a measure of image quality for CT scans of a chest phantom with 3D printed lung vessels.

METHODS

CT images of the chest phantom were acquired at seven dose levels by changing the tube current while other acquisition parameters were kept constant. Three CT reconstruction techniques were applied on each acquisition. The first technique was filtered backprojection (FBP), the second technique was FBP with iterative filtering (adaptive iteration dose reduction in 3 dimensions (AIDR 3D)) and the third technique was model-based iterative reconstruction (Forward projected model-based Iterative Reconstruction SoluTion (FIRST)). Image quality of the CT data was quantified in terms of SSIM. The SSIM index was used for image quality comparison between the dose levels and different reconstruction techniques. The SSIM index gives a value between 0 and 1, with 0 as the lowest image quality and 1 as an excellent image quality.

RESULTS

The lowest SSIM index was observed for FBP at all dose levels. The reconstruction technique with the highest SSIM depends on the dose level. For tube currents higher than 80 mA, AIDR 3D showed the highest SSIM index, and for tube currents lower or equal to 80 mA FIRST showed the highest SSIM index.

CONCLUSION

SSIM index is a robust quantity and is correlated to the image quality as perceived by the humans. Advanced CT reconstruction techniques provide better image quality in all conditions compared to FBP. Advances in knowledge: SSIM is a robust measure to compare CT image quality for advanced reconstruction techniques relative to a reference. The 3D print technology is an useful method for the development of dedicated phantoms for CT image quality evaluation.

Evaluation of a projection-domain lung nodule insertion technique in thoracic computed tomography

Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Prospective case acquisition can be time-consuming. Inserting lesions into existing cases to simulate positive cases is a promising alternative. The aim was to evaluate a recently developed projection-based lesion insertion technique in thoracic CT. In total, 32 lung nodules of various attenuations were segmented from 21 patient cases, forward projected, inserted into projections, and reconstructed. Two experienced radiologists and two residents independently evaluated these nodules in two substudies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a score from 1 to 10 (1 = absolutely artificial to 10 = absolutely realistic). Second, the inserted and the corresponding original lesions were presented side-by-side to each reader. For the randomized evaluation, discrimination of real versus inserted nodules was poor with areas under the receiver operative characteristic curves being 0.57 [95% confidence interval (CI): 0.46 to 0.68], 0.69 (95% CI: 0.58 to 0.78), and 0.62 (95% CI: 0.54 to 0.69) for the two residents, two radiologists, and all four readers, respectively. Our projection-based lung nodule insertion technique provides a robust method to artificially generate positive cases that prove to be difficult to differentiate from real cases.

Impact of number of repeated scans on model observer performance for a low-contrast detection task in computed tomography

Channelized Hotelling observer (CHO) models have been shown to correlate well with human observers for several phantom-based detection/classification tasks in clinical computed tomography (CT). A large number of repeated scans were used to achieve an accurate estimate of the model's template. The purpose of this study is to investigate how the experimental and CHO model parameters affect the minimum required number of repeated scans. A phantom containing 21 low-contrast objects was scanned on a 128-slice CT scanner at three dose levels. Each scan was repeated 100 times. For each experimental configuration, the low-contrast detectability, quantified as the area under receiver operating characteristic curve, [Formula: see text], was calculated using a previously validated CHO with randomly selected subsets of scans, ranging from 10 to 100. Using [Formula: see text] from the 100 scans as the reference, the accuracy from a smaller number of scans was determined. Our results demonstrated that the minimum number of repeated scans increased when the radiation dose level decreased, object size and contrast level decreased, and the number of channels increased. As a general trend, it increased as the low-contrast detectability decreased. This study provides a basis for the experimental design of task-based image quality assessment in clinical CT using CHO.