Development and validation of an automated methodology to assess perceptual in vivo noise texture in liver CT

An inventory of patient-image based risk/dose, image quality and body habitus/size metrics for adult abdomino-pelvic CT protocol optimisation

PURPOSE

Patient-specific protocol optimisation in abdomino-pelvic Computed Tomography (CT) requires measurement of body habitus/size (BH), sensitivity-specificity (surrogates image quality (IQ) metrics) and risk (surrogates often dose quantities) (RD). This work provides an updated inventory of metrics available for each of these three categories of optimisation variables derivable directly from patient measurements or images. We consider objective IQ metrics mostly in the spatial domain (i.e., those related directly to sharpness, contrast, noise quantity/texture and perceived detectability as these are used by radiologists to assess the acceptability or otherwise of patient images in practice).

MATERIALS AND METHODS

The search engine used was PubMed with the search period being 2010-2024. The key words used were: 'comput* tomography', 'CT', 'abdom*', 'dose', 'risk', 'SSDE', 'image quality', 'water equivalent diameter', 'size', 'body composition', 'habit*', 'BMI', 'obes*', 'overweight'. Since BH is critical for patient specific optimisation, articles correlating RD vs BH, and IQ vs BH were reviewed.

RESULTS

The inventory includes 11 BH, 12 IQ and 6 RD metrics. 25 RD vs BH correlation studies and 9 IQ vs BH correlation studies were identified. 7 articles in the latter group correlated metrics from all three categories concurrently.

CONCLUSIONS

Protocol optimisation should be fine-tuned to the level of the individual patient and particular clinical query. This would require a judicious choice of metrics from each of the three categories. It is suggested that, for increased utility in clinical practice, more future optimisation studies be clinical task based and involve the three categories of metrics concurrently.

Visualization of the distortion induced by nonlinear noise reduction in computed tomography

Purpose

We developed a method to visualize the image distortion induced by nonlinear noise reduction algorithms in computed tomography (CT) systems.

Approach

Nonlinear distortion was defined as the induced residual when testing a reconstruction algorithm by the criteria for a linear system. Two types of images were developed: a nonlinear distortion of an object (NLD object ) image and a nonlinear distortion of noise (NLD noise ) image to visualize the nonlinear distortion induced by an algorithm. Calculation of the images requires access to the sinogram data, which is seldomly fully provided. Hence, an approximation of the NLD object image was estimated. Using simulated CT acquisitions, four noise levels were added onto forward projected sinograms of a typical CT image; these were noise reduced using a median filter with the simultaneous iterative reconstruction technique or a total variation filter with the conjugate gradient least-squares algorithm. The linear reconstruction technique filtered back-projection was also analyzed for comparison.

Results

Structures in the NLD object image indicated contrast and resolution reduction of the nonlinear denoising. Although the approximated NLD object image represented the original NLD object image well, it had a higher random uncertainty. The NLD noise image for the median filter indicated both stochastic variations and structures reminding of the object while for the total variation filter only stochastic variations were indicated.

Conclusions

The developed images visualize nonlinear distortions of denoising algorithms. The object may be distorted by the noise and vice versa. Analyzing the distortion correlated to the object is more critical than analyzing a distortion of stochastic variations. The absence of nonlinear distortion may measure the robustness of the denoising algorithm.

Task-based validation and application of a scanner-specific CT simulator using an anthropomorphic phantom

BACKGROUND

Quantitative analysis of computed tomography (CT) images traditionally utilizes real patient data that can pose challenges with replicability, efficiency, and radiation exposure. Instead, virtual imaging trials (VITs) can overcome these hurdles through computer simulations of models of patients and imaging systems. DukeSim is a scanner-specific CT imaging simulator that has previously been validated with simple cylindrical phantoms, but not with anthropomorphic conditions and clinically relevant measurements.

PURPOSE

To validate a scanner-specific CT simulator (DukeSim) for the assessment of lung imaging biomarkers under clinically relevant conditions across multiple scanners using an anthropomorphic chest phantom, and to demonstrate the utility of virtual trials by studying the effects or radiation dose and reconstruction kernels on the lung imaging quantifications.

METHODS

An anthropomorphic chest phantom with customized tube inserts was imaged with two commercial scanners (Siemens Force and Siemens Flash) at 28 dose and reconstruction conditions. A computational version of the chest phantom was used with a scanner-specific CT simulator (DukeSim) to simulate virtual images corresponding to the settings of the real acquisitions. Lung imaging biomarkers were computed from both real and simulated CT images and quantitatively compared across all imaging conditions. The VIT framework was further utilized to investigate the effects of radiation dose (20-300 mAs) and reconstruction settings (Qr32f, Qr40f, and Qr69f reconstruction kernels using ADMIRE strength 3) on the accuracy of lung imaging biomarkers, compared against the ground-truth values modeled in the computational chest phantom.

RESULTS

The simulated CT images matched closely the real images for both scanners and all imaging conditions qualitatively and quantitatively, with the average biomarker percent error of 3.51% (range 0.002%-18.91%). The VIT study showed that sharper reconstruction kernels had lower accuracy with errors in mean lung HU of 84-94 HU, lung volume of 797-3785 cm3 , and lung mass of -800 to 1751 g. Lower tube currents had the lower accuracy with errors in mean lung HU of 6-84 HU, lung volume of 66-3785 cm3 , and lung mass of 170-1751 g. Other imaging biomarkers were consistent under the studied reconstruction settings and tube currents.

CONCLUSION

We comprehensively evaluated the realism of DukeSim in an anthropomorphic setup across a diverse range of imaging conditions. This study paves the way toward utilizing VITs more reliably for conducting medical imaging experiments that are not practical using actual patient images.

Special Section Guest Editorial: Computed tomography (CT) at 50 years

Guest editors Patrick La Riviere, Rebecca Fahrig, and Norbert Pelc introduce the JMI Special Section Celebrating X-Ray Computed Tomography at 50.