Rapid measurement of the low contrast detectability of CT scanners

Model observer task-based assessment of computed tomography metal artifact reduction using a hip arthroplasty phantom

BACKGROUND

The United States Food and Drug Administration (FDA) recently published a model observer-based framework for the objective performance assessment of computed tomography (CT) metal artifact reduction (MAR) algorithms and demonstrated the framework's feasibility in the low-contrast detectability (LCD) task-based assessment of MAR performance in a mathematical phantom.

PURPOSE

This study investigates the feasibility of the model observer-based framework in LCD task-based assessment of MAR performance using a physical arthroplasty phantom, results of which were then compared with the performance of human observers.

METHODS

A phantom simulating a unilateral hip prosthesis was designed with a rotatable insert containing a metal implant (cobalt-chromium spheres attached to titanium rods) and 16 unique low-contrast spherical lesions. Each lesion was scanned 100 times on a CT scanner (Somatom Force, Siemens Healthineers) with standard full-dose and half-dose protocols (140 kVp, 300 and 150 quality reference mAs) in each of four different insert rotations to supply 100 pairs of signal-present (lesion) and signal-absent (background) images needed for model observer analyses. Lesion detectability (d') using channelized Hotelling observers (CHO) was optimized by testing different image transformation techniques and channel selection (Gabor and Laguerre-Gauss [LG]) and calculated for each lesion reconstructed with and without iterative MAR (iMAR, Siemens Healthineers). Linear regression was used to assess the d' in each image set. Spearman's correlation was used to compare d' results to human detectability and confidence scores from a previously published human observer study involving the same phantom.

RESULTS

CHO d' measurements using LG channels were less sensitive to artifacts than those using Gabor channels and were therefore selected for the LCD assessment. Image masking and thresholding provided more accurate d' by isolating the signal and minimizing background differences. For all lesions, d' values of full-dose iMAR images were significantly greater than those of filtered back projection (FBP) images at full dose (p < 0.001) and half dose (p < 0.001). Additionally, d' values of half-dose iMAR images were significantly greater than those of FBP images at full dose (p = 0.010) and half dose (p < 0.001). The d' values were not significantly different between full-dose and half-dose FBP (p = 0.620) or between full-dose and half-dose iMAR (p = 0.358). Pooling across all lesions, d' measurements were positively correlated with human detection rate (Spearman correlation coefficient = 0.723; p < 0.001) and confidence scores (Spearman correlation coefficient = 0.727; p < 0.001).

CONCLUSIONS

The use of CHO in the LCD assessment of MAR performance can be feasibly performed on a physical phantom, and results using this method correlated well with findings from human observers.

Emergency CT Scans: Unveiling the Risks of Contrast-Associated Acute Kidney Injury

OBJECTIVES

This study aimed to identify the incidence and risk factors for contrast-associated acute kidney injury nephropathy (CA-AKI) in patients undergoing contrast-enhanced computed tomography (CCT) in the emergency department.

MATERIALS AND METHODS

In this retrospective single-center study, patients aged 18 and older who visited the emergency department and underwent CCT between January and February 2022 were included. The Mehran score, calculated from patient data, was used to assess risk. CA-AKI development was determined by measuring serum creatinine (SCr) levels 48-72 h post-contrast administration.

RESULTS

The study included 532 patients, with a mean age of 57 ± 19 years; 53.2% were male. CA-AKI developed in 16% of cases, 5.82% required hemodialysis, and 7.9% died. The Mehran score was the only significant predictor of CA-AKI development. Patients with a Mehran score of 16 or higher had a 161-fold increased risk of developing CA-AKI compared to those with a score of 5 or lower. The model achieved a 91.3% correct classification rate. Logistic regression analysis showed that CA-AKI significantly increased mortality risk by 15.7 times.

CONCLUSION

The Mehran score, originally developed for predicting CA-AKI risk post-coronary intervention, is also effective for predicting CA-AKI risk after CCT. While CA-AKI is a significant factor affecting mortality, it is not the sole cause of death (Nagelkerke R2 value 0.310).

A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability

Purpose. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images.Method. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between -980 HU (Hounsfield units) and -1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images.Results. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with R2values of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves.Conclusion. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment

BACKGROUND

The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results.

PURPOSE

In this work, we fabricated a full-size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose.

METHODS

Designed to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of -350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high-dose reference. Scans were reconstructed with three different settings: filtered-backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs.

RESULTS

The printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59.

CONCLUSIONS

Our 3D-printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

A Practical Method for Slice Spacing Measurement Using the American Association of Physicists in Medicine Computed Tomography Performance Phantom

Background

The slice spacing has a crucial role in the accuracy of computed tomography (CT) images in sagittal and coronal planes. However, there is no practical method for measuring the accuracy of the slice spacing.

Purpose

This study proposes a novel method to automatically measure the slice spacing using the American Association of Physicists in Medicine (AAPM) CT performance phantom.

Methods

The AAPM CT performance phantom module 610-04 was used to measure slice spacing. The process of slice spacing measurement involves a pair of axial images of the module containing ramp aluminum objects located at adjacent slice positions. The middle aluminum plate of each image was automatically segmented. Next, the two segmented images were combined to produce one image with two stair objects. The centroid coordinates of two stair objects were automatically determined. Subsequently, the distance between these two centroids was measured to directly indicate the slice spacing. For comparison, the slice spacing was calculated by accessing the slice position attributes from the DICOM header of both images. The proposed method was tested on phantom images with variations in slice spacing and field of view (FOV).

Results

The results showed that the automatic measurement of slice spacing was quite accurate for all variations of slice spacing and FOV, with average differences of 9.0% and 9.3%, respectively.

Conclusion

A new automated method for measuring the slice spacing using the AAPM CT phantom was successfully demonstrated and tested for variations of slice spacing and FOV. Slice spacing measurement may be considered an additional parameter to be checked in addition to other established parameters.

A dense search challenge phantom fabricated with pixel-based 3D printing for precise detectability assessment

The performance of a CT scanner for detectability tasks is difficult to precisely measure. Metrics such as contrast-to-noise ratio, modulation transfer function, and noise power spectrum do not predict detectability in the context of nonlinear reconstruction. We propose to measure detectability using a dense search challenge: a phantom is embedded with hundreds of target objects at random locations, and a human or numerical observer analyzes the reconstruction and reports on suspected locations of all target objects. The reported locations are compared to ground truth to produce a figure of merit, such as area under the curve (AUC), that is sensitive to the acquisition dose and the dose efficiency of the CT scanner. We used simulations to design such a dense search challenge phantom and found that detectability could be measured with precision better than 5%. Test 3D prints using the PixelPrint technique showed the feasibility of this technique.

Complex Relationship Between Artificial Intelligence and CT Radiation Dose

Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.