Automated Web-based Software for CT Quality Control Testing of Low-contrast Detectability using Model Observers

The Channelized Hotelling observer (CHO) is well correlated with human observer performance in many CT detection/classification tasks but has not been widely adopted in routine CT quality control and performance evaluation, mainly because of the lack of an easily available, efficient, and validated software tool. We developed a highly automated solution - CT image quality evaluation and Protocol Optimization (CTPro), a web-based software platform that includes CHO and other traditional image quality assessment tools such as modulation transfer function and noise power spectrum. This tool can allow easy access to the CHO for both the research and clinical community and enable efficient, accurate image quality evaluation without the need of installing additional software. Its application was demonstrated by comparing the low-contrast detectability on a clinical photon-counting-detector (PCD)-CT with a traditional energy-integrating-detector (EID)-CT, which showed UHR-T3D had 6.2% higher d' than EID-CT with IR (p = 0.047) and 4.1% lower d' without IR (p = 0.122).

[Evaluation of Low-contrast Detectability Using the Digital Phantom Creation Tool in the Late Arterial Phase to Detect Liver Mass Lesions]

PURPOSE

Late arterial phase images of SD 8, SD 10, and SD 12 were acquired in the 3-phase dynamic study of the liver in combination with hybrid iterative reconstruction. We evaluated the low-contrast detectability by adding a simulated tumor to these images and aimed to formulate a standard image quality.

METHODS

We prepared images with and without signal for 60 series of 20 samples, each with 3 image quality types (total: 120 series). The continuous confidence method by 10 observers detected 60 simulated tumors.

RESULTS

The detection sensitivities were 0.765, 0.785, and 0.260 for SD 8, SD 10, and SD 12, respectively (p<0.001) with no significantly different specificities, and the areas under the curve were 0.901, 0.892, and 0.616 (p<0.001), respectively. The simulated mass detection rates were 74.5%, 75.0%, and 21.5% for SD 8, SD 10, and SD 12, respectively (p<0.001), and the intraclass correlation coefficients, which indicate interobserver reliability, were 0.697 at SD 10 without signal, and SD 12 without a signal significantly dropped to 0.185.

CONCLUSION

Therefore, SD 12 images increase the possibility of overlooking lesions. Hence, image quality in the late arterial phase should be SD 10 or less.

Can optimized model-based iterative reconstruction improve the contrast of liver lesions in CT?

BACKGROUND

Computed tomography is a standard imaging procedure for the detection of liver lesions, such as metastases, which can often be small and poorly contrasted, and therefore hard to detect. Advances in image reconstruction have shown promise in reducing image noise and improving low-contrast detectability.

PURPOSE

To examine a novel, specialized, model-based iterative reconstruction (MBIR) technique for improved low-contrast liver lesion detection.

MATERIAL AND METHODS

Patient images with reported poorly contrasted focal liver lesions were retrospectively reconstructed with the low-contrast attenuating algorithm (FIRST-LCD) from primary raw data. Liver-to-lesion contrast, signal-to-noise, and contrast-to-noise ratios for background and liver noise for each lesion were compared for all three FIRST-LCD presets with the established hybrid iterative reconstruction method (AIDR-3D). An additional visual conspicuity score was given by two experienced radiologists for each lesion.

RESULTS

A total of 82 lesions in 57 examinations were included in the analysis. All three FIRST-LCD algorithms provided statistically significant increases in liver-to-lesion contrast, with FIRST_MILD showing the largest increase (40.47 HU in AIDR-3D; 45.84 HU in FIRST_MILD; P < 0.001). Substantial improvement was shown in contrast-to-noise metrics. Visual analysis of the lesions shows decreased lesion visibility with all FIRST methods in comparison to AIDR-3D, with FIRST_STR showing the closest results (P < 0.001).

CONCLUSION

Objective image metrics show promise for MBIR methods in improving the detectability of low-contrast liver lesions; however, subjective image quality may be perceived as inferior. Further improvements are necessary to enhance image quality and lesion detection.

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images

Purpose

We have developed a software to automatically find the contrast–detail (C–D) curve based on the statistical low‐contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics.

Methods

A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2‐pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C–D curve was obtained and the target size at an MDC of 0.6% (i.e., 6‐HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer.

Results

The developed system was able to measure C–D curves from different phantoms and scanners. We found that the C–D curves follow a power‐law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low‐contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD.

Conclusions

The statistical LCD measurement method can generate a C–D curve automatically, quickly, and objectively.

Dose Reduction and Low-Contrast Detectability Using Iterative CBCT Reconstruction Algorithm for Radiotherapy

Introduction: Several studies have reported the relation between the imaging dose and secondary cancer risk and have emphasized the need to minimize the additional imaging dose as low as reasonably achievable. The iterative cone-beam computed tomography (iCBCT) algorithm can improve the image quality by utilizing scatter correction and statistical reconstruction. We investigate the use of a novel iCBCT reconstruction algorithm to reduce the patient dose while maintaining low-contrast detectability and registration accuracy. Methods: Catphan and anthropomorphic phantoms were analyzed. All CBCT images were acquired with varying dose levels and reconstructed with a Feldkamp-Davis-Kress algorithm-based CBCT (FDK-CBCT) and iCBCT. The low-contrast detectability was subjectively assessed using a 9-point scale by 4 reviewers and objectively assessed using structure similarity index (SSIM). The soft tissue-based registration error was analyzed for each dose level and reconstruction technique. Results: The results of subjective low-contrast detectability found that the iCBCT acquired at two-thirds of a dose was superior to the FDK-CBCT acquired at a full dose (6.4 vs 5.4). Relative to FDK-CBCT acquired at full dose, SSIM was higher for iCBCT acquired at one-sixth dose in head and head and neck region while equivalent with iCBCT acquired at two-thirds dose in pelvis region. The soft tissue-based registration was 2.2 and 0.6 mm for FDK-CBCT and iCBCT, respectively. Conclusion: Use of iCBCT reconstruction algorithm can generally reduce the patient dose by approximately two-thirds compared to conventional reconstruction methods while maintaining low-contrast detectability and accuracy of registration.

Rapid measurement of the low contrast detectability of CT scanners

PURPOSE

Low contrast detectability (LCD) is a metric of fundamental importance in computed tomography (CT) imaging. In spite of this, its measurement is challenging in the context of nonlinear data processing. We introduce a new framework for objectively characterizing LCD with a single scan of a special-purpose phantom and automated analysis software. The output of the analysis software is a "machine LCD" metric which is more representative of LCD than contrast-noise ratio (CNR). It is not intended to replace human observer or model observer studies.

METHODS

Following preliminary simulations, we fabricated a phantom containing hundreds of low-contrast beads. These beads are acrylic spheres (1.6 mm, net contrast ~10 HU) suspended and randomly dispersed in a background matrix of nylon pellets and isoattenuating saline. The task was to search for and localize the beads. A modified matched filter was used to automatically scan the reconstruction and select candidate bead localizations of varying confidence. These were compared to bead locations as determined from a high-dose reference scan to produce free-response ROC curves. We compared iterative reconstruction (IR) and filtered backpropagation (FBP) at multiple dose levels between 40 and 240 mAs. The scans at 60, 120, and 180 mAs were performed three times each to estimate uncertainty.

RESULTS

Experimental scans demonstrated the feasibility of our technique. Our metric for machine LCD was the area under the exponential transform of the FROC curve (AUC). AUC increased monotonically from 0.21 at 40 mAs to 0.84 at 240 mAs. The sample standard deviation of AUC was approximately 0.02. This measurement uncertainty in AUC corresponded to a change in tube current of 4% to 8%. Surprisingly, we found that AUCs for IR were slightly worse than AUCs for FBP. While the phantom was sufficient for these experiments, it contained small air bubbles and alternative fabrication methods will be necessary for widespread utilization.

CONCLUSIONS

It is feasible to measure machine LCD using a search task on a phantom with hundreds of beads and to obtain tight error bars using only a single scan. Our method could facilitate routine quality assurance or possibly enable comparisons between different protocols and scanners.

[Improvement of Image Quality in the Axial Section Using High-resolution Scan Mode and Hybrid Iterative Reconstruction in Ultra-high-resolution Computed Tomography]

The purpose of this study is to compare the physical characteristics and visibility of high-resolution and conventional images acquired with the same X-ray dose, and to investigate the superiority of super high-resolution imaging. A Catphan phantom was scanned in the normal resolution (NR), high-resolution (HR), and super high-resolution (SHR) modes of ultra-high-resolution computed tomography at 120 kV and 75 mAs. All images were reconstructed into a 5-mm thick image slices with filtered back-projection (FBP) and hybrid image reconstruction (HIR), which included normal and enhanced adaptive iterative dose reduction 3D (AIDR and eAIDR, respectively). The modulation transfer function (MTF) and noise power spectrum (NPS) were measured using the circular edge method and radial frequency method, respectively. The signal-to-noise ratio (SNR) was then calculated. High-contrast resolution and low-contrast detectability were evaluated visually by five radiological technologists. The MTFs of HR_eAIDR and HR_FBP images were higher than those of NR_FBP images. However, the NPSs of HR_eAIDR and HR_FBP images were larger than those of NR_FBP images. The SNR of HR_eAIDR images was higher than that of NR_FBP and HR_FBP images. The scores of high-contrast resolution of HR_eAIDR, NR_FBP, and HR_FBP images were 13, 8, and 13 cycles/cm, respectively, and the scores of low-contrast detectability were 5, 5, and 6 mm, respectively. Hence, an improvement in high-contrast resolution of signal more than 400 HU in the axial section can be achieved without increasing the radiation dose and decreasing low-contrast detectability with 10 HU using the HR mode and eAIDR.

Objective comparison of high-contrast spatial resolution and low-contrast detectability for various clinical protocols on multiple CT scanners

PURPOSE

We sought to compare objectively computed tomography (CT) scanner performance for three clinically relevant protocols using a task-based image quality assessment method in order to assess the potential for radiation dose reduction.

METHODS

Four CT scanners released between 2003 and 2007 by different manufacturers were compared with four CT scanners released between 2012 and 2014 by the same manufacturers using ideal linear model observers (MO): prewhitening (PW) MO and channelized Hotelling (CHO) MO with Laguerre-Gauss channels for high-contrast spatial resolution and low-contrast detectability (LCD) performance, respectively. High-contrast spatial resolution was assessed using a custom-made phantom that enabled the computation of the target transfer function (TTF) and noise power spectrum (NPS). Low-contrast detectability was assessed using a commercially available anthropomorphic abdominal phantom providing equivalent diameters of 24, 29.6, and 34.6 cm. Three protocols were reviewed: a head (trauma) and an abdominal (urinary stones) protocol were applied to assess high-contrast spatial resolution performance; and another abdominal (focal liver lesions) protocol was applied for LCD. The liver protocol was tested using fixed and modulated tube currents. The PW MO was proposed for assessing high-contrast detectability performance of the various CT scanners.

RESULTS

Compared with older generation CT scanners, three newer systems displayed significant improvements in high-contrast detectability over that of their predecessors. A fourth, newer system had lower performance. The CHO MO was appropriate for assessing LCD performance and revealed that an excellent level of image quality could be obtained with newer scanners at significantly lower dose levels.

CONCLUSIONS

This study shows that MO can objectively benchmark CT scanners using a task-based image quality method, thus helping to estimate the potential for further dose reductions offered by the latest systems. Such an approach may be useful for adequately and quantitatively comparing clinically relevant image quality among various scanners.

[Development of New Digital Phantom Creation Tool for Evaluation of Low-contrast Detectability Using Iterative Reconstruction]

PURPOSE

We developed a novel digital phantom-creation tool that will help formulate the standard shooting method for a three-phase dynamic liver study. Here, we present data demonstrating the usefulness of this tool in the assessment of low-contrast detectability and visibility.

METHODS

We performed a visual evaluation by adding a spherical digital phantom with a diameter of 8 mm and a computed tomography (CT) value difference of 10 Hounsfield unit (HU) to images taken using filtered back projection and seven types of adaptive iterative dose reduction 3D (Weak, Mild, eMild, Standard, eStandard, Strong, and eStrong). We also examined the partial-volume effect by drawing a profile curve using a digital phantom with a CT value difference of 30 HU and a diameter of 5 mm. Furthermore, a digital phantom with two kinds of filters (smoothing and Gaussian) was added to the image of the home-made simulated tumor phantom to visual valuate its visibility in the phantom's low-contrast module and the digital phantom.

RESULTS

Detection sensitivity was significantly decreased in Standard, eStandard, Strong, and eStrong, and the area under the curve also decreased in a similar fashion. We confirmed that the partial-volume effect was due to the different maximum CT values in the profile curve at 4 and 5 mm thickness. The visibility of the low-contrast module and digital phantom was most consistent when using the Gaussian filter.

CONCLUSION

This tool can be used for low-contrast detection ability evaluation.

[Proposal of a New Index Based on Signal-to-Noise Ratio for Low-contrast Detectability in Computed Tomographic Imaging]

The low-contrast detectability of computed tomography (CT) images is commonly evaluated by the contrast-to-noise ratio (CNR) because of its convenience to measure. However, the correlation between CNR and visual detectability is poor because the CNR is a simple index determined by both the contrast of the object and the standard deviation of the image noise. On the other hand, the signal-to-noise ratio (SNR), especially SNR based on the statistical decision theory model (SNR_{S, D}) and SNR based on the matched-filter model (SNR_M) are considered superior to CNR. In this study, we investigated a new physical image quality index for evaluating low-contrast detectability (SNR_A), which is approximately derived from SNR_{S, D} and SNR_M. The new index, which was calculated using the object size, contrast of the object and the noise power spectrum, provided good approximations when the diameter of the rod object was equal and >5 mm. The diameter dependency of the SNR_A was also found to provide better sensitivity than the sensitivities of CNR and object-specific CNR, similar to SNR_{S, D} and SNR_M. The results suggested that the proposed convenient index should be useful for evaluating the low-contrast detectability of CT images.