Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial

OBJECTIVES

To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT.

METHODS

This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale.

RESULTS

SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38).

CONCLUSION

Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT.

CLINICAL RELEVANCE STATEMENT

Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT.

KEY POINTS

Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.

An efficient dual-domain deep learning network for sparse-view CT reconstruction

BACKGROUND AND OBJECTIVE

We develop an efficient deep-learning based dual-domain reconstruction method for sparse-view CT reconstruction with small training parameters and comparable running time. We aim to investigate the model's capability and its clinical value by performing objective and subjective quality assessments using clinical CT projection data acquired on commercial scanners.

METHODS

We designed two lightweight networks, namely Sino-Net and Img-Net, to restore the projection and image signal from the DD-Net reconstructed images in the projection and image domains, respectively. The proposed network has small training parameters and comparable running time among dual-domain based reconstruction networks and is easy to train (end-to-end). We prospectively collected clinical thoraco-abdominal CT projection data acquired on a Siemens Biograph 128 Edge CT scanner to train and validate the proposed network. Further, we quantitatively evaluated the CT Hounsfield unit (HU) values on 21 organs and anatomic structures, such as the liver, aorta, and ribcage. We also analyzed the noise properties and compared the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of the reconstructed images. Besides, two radiologists conducted the subjective qualitative evaluation including the confidence and conspicuity of anatomic structures, and the overall image quality using a 1-5 likert scoring system.

RESULTS

Objective and subjective evaluation showed that the proposed algorithm achieves competitive results in eliminating noise and artifacts, restoring fine structure details, and recovering edges and contours of anatomic structures using 384 views (1/6 sparse rate). The proposed method exhibited good computational cost performance on clinical projection data.

CONCLUSION

This work presents an efficient dual-domain learning network for sparse-view CT reconstruction on raw projection data from a commercial scanner. The study also provides insights for designing an organ-based image quality assessment pipeline for sparse-view reconstruction tasks, potentially benefiting organ-specific dose reduction by sparse-view imaging.

Tube voltage in chest and abdomen DR imaging: Which settings return maximal non-prewhitening model observer with eye filter (NPWE) detectability?

BACKGROUND

The non-prewhitening computational model observer with eye filter (NPWE) has been shown to reasonably predict human observer performance in general radiography and is an appropriate substitute when real clinical trials are not feasible. In this study, the NPWE model observer is used to detect specific tasks (circular designer nodules) ranging between 1 and 30 mm in diameter using chest and abdomen phantom images acquired across the diagnostic energy range (60-125 kVp) with and without an anti-scatter grid.

PURPOSE

The aim of this study was to derive tube voltage (kVp) settings that return maximal NPWE detectability (d') of designer nodules, for digital radiography (DR) chest and abdomen imaging.

METHODS

Images of a chest phantom (LucAl phantom) and a surrogate for an abdomen (18.5 cm PMMA) were acquired across the diagnostic energy range (60-125 kVp) with matched effective dose (for the respective anatomies), with and without an anti-scatter grid, on a general x-ray system. Images were captured using an Agfa DX-D 40C wireless indirect caesium iodide (CsI) imaging panel. Modulation transfer function (MTF), normalized noise power spectrum (NNPS), and contrast (C) were measured in each image and the detectability index d' was calculated for circular designer nodules with diameters ranging from 1 to 30 mm (in steps of 1 mm).

RESULTS

The calculated d' peaked at a nodule diameter of 3 mm irrespective of tube voltage, for both chest and abdomen images. A tube voltage of 80 kVp returned maximal d' for chest imaging across all nodule diameters both with and without an anti-scatter grid. A tube voltage of 70 kVp returned maximal d' for abdomen imaging.

CONCLUSION

The NPWE observer model has been used to derive tube voltages (kVp) that return maximal detectability (d') of designer nodules for chest and abdomen radiography using a modern DR imaging system. This will provide the medical physicist with a starting point in the task of optimising tube voltage range for chest and abdomen imaging.

Development, validation, and application of a generic image-based noise addition method for simulating reduced dose computed tomography images

BACKGROUND

Major efforts in computed tomography (CT) have been focused on reducing radiation dose to patients while maintaining adequate diagnostic quality. To that end, research tools have been developed to simulate reduced-dose images via either image-based or projection-based methods. The former is limited to fully capturing realistic texture, streak, and non-stationary characteristics of reduced dose, while the latter is impractical clinically.

PURPOSE

To develop and validate an image-based noise addition method that accounts for such attributes while being practical in clinical settings.

METHODS

A noise addition method was developed to add realistic noise in the image domain. The method first estimates the noise power spectrum (NPS) of CT images, which are also forward-projected to form synthetic projections. The projection data are supplemented with random white noise proportional to their attenuation values. The noise sinogram is then back-projected onto the image, filtered by the NPS, and scaled according to the desired dose reduction level. The tool was evaluated using both phantom images and patient data. The phantom images were acquired using a multi-sized image quality phantom (Mercury Phantom 3.0, Duke University), and a thorax anthropomorphic phantom (Lungman Phantom, Kyoto Kagaku) at different dose levels and reconstruction settings. The patient images consisted of two dose levels of various CT examinations and reconstruction settings. The simulated and real reduced-dose images were compared in terms of the noise magnitude and texture (i.e., NPS average frequency, NPS-fav). The utility of this methodology was also assessed for routine clinical use for CT protocol review.

RESULTS

For the phantom images, the percent errors in the noise magnitude between the simulated images and the actual images of the Mercury Phantom and anthropomorphic phantom images were 3.34% and 3.50%, respectively. The difference in fav was 0.07 mm-1 for the Mercury Phantom and 0.06 mm-1 for the anthropomorphic phantom between the simulated and actual images. The average noise magnitude percent error between the simulated and actual patient images was 4.61% with noise texture judged to be visually comparable with some kernel dependencies. When implemented clinically, the tool proved practical to simplify the process of estimating radiation dose reduction for CT protocols, resulting in a 50% dose reduction of our multiple myeloma protocol.

CONCLUSIONS

The method generated simulated CT images with realistic noise properties similar to images acquired at the same radiation exposure without needing access to raw projection data.

Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study

PURPOSE

The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms.

MATERIALS AND METHODS

Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists.

RESULTS

For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs.

CONCLUSION

By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.

AAPM Task Group Report 299: Quality control in multi-energy computed tomography

Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

[Simple Method to Measure Slice Sensitivity Profile in Non-helical CT Using Tilted Metal Wire]

PURPOSE

The measurement of slice sensitivity profile (SSP) in non-helical CT is conventionally performed by repeated scans with moving a micro-coin phantom little by little in the longitudinal direction at a small interval, which is reliable but laborious and time-consuming. The purpose of this study was to propose a simple method for measuring the SSP in non-helical CT based on a previous method that measured the slice thickness using a tilted metal wire.

METHODS

In the proposed method, a CT image was obtained by scanning a wire tilted at an angle θ=30° to the scan plane. By deconvolving the image with the point spread function (PSF) measured at the scanner, we obtained an image that was not affected by the PSF blurring. The CT value profile along the wire was acquired on the obtained image. The SSP was determined by multiplying the profile by tan θ. In addition, the SSP was measured by the conventional method using a micro-coin phantom and compared with the SSP obtained by the proposed method.

RESULTS

The SSP measured by the proposed method agreed well with that measured by the conventional method. The full-width at half-maximum values of these SSPs were the same.

CONCLUSION

The proposed method was demonstrated to easily and accurately measure the SSP in non-helical CT.

Benchmarking deep learning-based low-dose CT image denoising algorithms

BACKGROUND

Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reduction algorithms.

PURPOSE

Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reported results.

METHODS

In this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardized setup.

RESULTS

Our evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal at best.

CONCLUSIONS

This study highlights the need for a more rigorous and fair evaluation of novel deep learning-based methods for low-dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate their algorithms.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity

BACKGROUND

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.

PURPOSE

To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI.

MATERIAL AND METHODS

The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity.

RESULTS

The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010).

CONCLUSION

DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

Comparison of low-energy virtual monoenergetic images between photon-counting CT and energy-integrating detectors CT: A phantom study

PURPOSE

The purpose of this study was to assess image quality and dose level using a photon-counting CT (PCCT) scanner by comparison with a dual-source CT (DSCT) scanner on virtual monoenergetic images (VMIs) at low energy levels.

MATERIALS AND METHODS

A phantom was scanned using a DSCT and a PCCT with a volume CT dose index of 11 mGy, and additionally at 6 mGy and 1.8 mGy for PCCT. Noise power spectrum and task-based transfer function were evaluated from 40 to 70 keV on VMIs to assess noise magnitude and noise texture (fav) and spatial resolution on two iodine inserts (f50), respectively. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the energy level used.

RESULTS

For all energy levels, noise magnitude values were lower with PCCT than with DSCT at 11 and 6 mGy, but greater at 1.8 mGy. fav values were higher with PCCT than with DSCT at 11 mGy (8.6 ± 1.5 [standard deviation [SD]%), similar at 6 mGy (1.6 ± 1.5 [SD]%) and lower at 1.8 mGy (-17.8 ± 2.2 [SD]%). For both inserts, f50 values were higher with PCCT than DSCT at 11- and 6 mGy for all keV levels, except at 6 mGy and 40 keV. d' values were higher with PCCT than with DSCT at 11- and 6 mGy for all keV and both simulated lesions. Similar d' values to those of the DSCT at 11 mGy, were obtained at 2.25 mGy for iodine insert at 2 mg/mL and at 0.96 mGy for iodine insert at 4 mg/mL at 40 keV.

CONCLUSION

Compared to DSCT, PCCT reduces noise magnitude and improves noise texture, spatial resolution and detectability on VMIs for all low-keV levels.

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic andex-vivophantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

Comparison of the spectral performance between two dual-source CT systems on low-energy virtual monoenergetic images: A phantom study

PURPOSE

To compare the spectral performance of two different DSCT (DSCT-Pulse and DSCT-Force) on virtual monoenergetic images (VMIs) at low energy levels.

METHODS

An image quality phantom was scanned on the two DSCTs at three dose levels: 11/6/1.8 mGy. Level 3 of an advanced modeled iterative reconstruction algorithm was used. Noise power spectrum and task-based transfer function were computed on VMIs from 40 to 70 keV to assess noise magnitude and noise texture (fav) and spatial resolution (f50). A detectability index (d') was computed to assess the detection of one contrast-enhanced abdominal lesion as a function of the keV level used.

RESULTS

For all dose levels and all energy levels, noise magnitude was significantly higher (p < 0.05) with DSCT-Pulse than with DSCT-Force (12.6 ± 2.7 % at 1.8 mGy, 9.1 ± 2.9 % at 6 mGy and 4.0 ± 2.7 % at 11 mGy). For all energy levels, fav values were significantly higher (p < 0.05) with DSCT-Pulse than with DSCT-Force at 1.8 mGy (4.8 ± 3.9 %) and at 6 mGy (5.5 ± 2.5 %) but similar at 11 mGy (0.2 ± 3.6 %; p = 0.518). For all energy levels, f50 values were significantly higher with DSCT-Pulse than with DSCT-Force (12.7 ± 5.6 % at 1.8 mGy, 17.9 ± 4.5 % at 6 mGy and 13.1 ± 2.6 % at 11 mGy). For all keV, similar d' values were found with both DSCT-Force and DSCT-Pulse at 11 mGy (-1.0 ± 3.1 %; p = 0.084). For other dose levels, d' values were significantly lower with DSCT-Pulse than with DSCT-Force (9.1 ± 3.2 % at 1.8 mGy and -6.3 ± 3.9 % at 6 mGy).

CONCLUSION

Compared with the DSCT-Force, the DSCT-Pulse improved noise texture and spatial resolution, but noise magnitude was slightly higher and detectability slightly lower, particularly when the dose level was reduced.

Task-based image quality assessment of an intraoperative CBCT for spine surgery compared with conventional CT

PURPOSE

To analyze the image quality of a novel, state-of-the art platform for CBCT image-guided spine surgery, focusing particularly on the dose-effectiveness compared with conventional CT (the gold standard for postoperative assessment).

METHODS

The ClarifEye platform (Philips Healthcare) with integrated augmented-reality surgical navigation, has been compared with a GE Revolution CT (GE Healthcare). The 3D spatial resolution (TTF) and noise (NPS) were evaluated considering relevant feature contrasts (200-900 HU) and background noise for differently sized patients (200-300 mm water-equivalent diameter). These measures were used to determine the noise equivalent quanta (NEQ) and observer model detectability.

RESULTS

The CBCT system exhibited a linear response with 50% TTF at 5.7 cycles/cm (10% TTF at 9.2 cycles/cm), and the axial noise power peaking at about 3.6 cycles/cm (average frequency of 4.1 cycles/cm). The noise magnitude and texture differed markedly compared to iteratively reconstructed CT images (GE ASiR-V). The CBCT system had 26% lower detectability for a high-frequency task (related to edge detection) compared with CT images reconstructed using the Bone kernel combined with ASiR-V 50%. Likewise, it had 18% lower detectability for low- and mid-frequency tasks compared with CT images reconstructed using the Standard kernel. This difference translates to 50%-80% higher CBCT imaging doses required to match the CT image quality.

CONCLUSIONS

The ClarifEye platform demonstrates intraoperative CBCT-imaging capabilities that under certain circumstances are comparable with conventional CT. However, due to limited dose-effectiveness, a trade-off between timeliness and radiation exposure must be considered if end-of-procedure CBCT is to replace postoperative CT.

A nonparametric measure of contrast in x-ray images

Objective.We propose a nonparametric figure of merit, the contrast equivalent distance CED, to measure contrast directly from clinical images.Approach.A relative brightness distanceδis calculated by making use of the order statistic of the pixel values. By multiplyingδwith the grey value rangeR, the mean brightness distance MBD is obtained. From the MBD, the CED and the distance-to-noise ratio DNR can be derived. The latter is the ratio of the MBD and a previously suggested nonparametric measureτfor the noise. Since the order statistic is independent of the spatial arrangement of the pixel values, the measures can be obtained directly from clinical images. We apply the new measures to mammography images of an anthropomorphic phantom and of a phantom with a step wedge as well as to CT images of a head phantom.Main results.For low-noise images of a step wedge, the MBD is equivalent to the conventional grey value distance. While this measure permits the evaluation of clinical images, it is sensitive to noise. Therefore, noise has to be quantified at the same time. When the ratioσ/τof the noise standard deviationσtoτis available, validity limits for the CED as a measure of contrast can be established. The new figures of merit can be calculated for entire images as well as on regions of interest (ROI) with an edge length not smaller than 32 px.Significance.The new figures of merit are suited to quantify the quality of clinical images without relying on the assumption of a linear, shift-invariant system. They can be used for any kind of greyscale image, provided the ratioσ/τcan be estimated. This will hopefully help to achieve the optimisation of image quality vs dose required by radioprotection laws.

An image quality assessment index based on image features and keypoints for X-ray CT images

Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. The proposed index quantifies and visualizes the distance between feature vectors associated with keypoints, which varies depending on changes in the image quality. This metric was validated on images with varying noise levels and resolution characteristics, and its applicability and effectiveness were examined by evaluating images subjected to various affine transformations. In the verification of X-ray computed tomography imaging using a head phantom, the distances between feature descriptors for each keypoint increased as the image quality degraded, exhibiting a strong correlation with the changes in the SSIM. Notably, the proposed index outperformed conventional full-reference metrics in terms of robustness to various transformations which are without changes in the image quality. Overall, the results suggested that image analysis performed using the proposed framework could effectively visualize the corresponding feature points, potentially harnessing lost feature information owing to changes in the image quality. These findings demonstrate the feasibility of applying the novel index to analyze changes in the image quality. This method may overcome limitations inherent in conventional evaluation methodologies and contribute to medical image analysis in the broader domain.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography

Objective.With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.Approach.The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml-1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data.Main Results.Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.Signficance.In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale

OBJECTIVES

The aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans.

METHODS

Abdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC).

RESULTS

Twenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396-0.814) to 0.785 (95%CI 0.676-0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034-0.500) to 0.562 (95%CI 0.337-0.729). Intraobserver agreement increased for four out of six observers.

CONCLUSION

The PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement.

CLINICAL RELEVANCE STATEMENT

This study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms.

KEY POINTS

• Subjective assessment of diagnostic image quality via Likert scale has limited reliability. • A pairwise comparison method improves the inter- and intraobserver agreement. • The pairwise comparison method is more reliable for CT optimization studies.

Assessment of image quality and establishment of local acceptable quality dose for computed tomography based on patient effective diameter

Purpose

We aim to develop modified clinical indication (CI)-based image quality scoring criteria (IQSC) for assessing image quality (IQ) and establishing acceptable quality doses (AQDs) in adult computed tomography (CT) examinations, based on CIs and patient sizes.

Approach

CT images, volume CT dose index (CTDI vol ), and dose length product (DLP) were collected retrospectively between September 2020 and September 2021 for eight common CIs from two CT scanners at a central hospital in the Kingdom of Bahrain. Using the modified CI-based IQSC and a Likert scale (0 to 4), three radiologists assessed the IQ of each examination. AQDs were then established as the median value ofCTDI vol and DLP for images with an average score of 3 and compared to national diagnostic reference levels (NDRLs).

Results

Out of 581 examinations, 60 were excluded from the study due to average scores above or below 3. The established AQDs were lower than the NDRLs for all CIs, except AQDs / CTDI vol for oncologic follow-up for large patients (28 versus 26 mGy) in scanner A, besides abdominal pain for medium patients (16 versus 15 mGy) and large patients (34 versus 27 mGy), and diverticulitis/appendicitis for medium patients (15 versus 12 mGy) and large patients (33 versus 30 mGy) in scanner B, indicating the need for optimization.

Conclusions

CI-based IQSC is crucial for IQ assessment and establishing AQDs according to patient size. It identifies stations requiring optimization of patient radiation exposure.

Development of a separability index for task specific characterization of spectral computed tomography

PURPOSE

In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions.

METHOD

Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated.

RESULTS

Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability.

CONCLUSION

The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Super resolution deep learning reconstruction for coronary CT angiography: A structured phantom study

Purpose

Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography.

Methods

The structural phantom had ribs and vertebrae made of plaster, a left ventricle filled with dilute contrast medium, a coronary artery with simulated stenosis, and an implanted stent graft. By scanning the structured phantom, we evaluated noise and spatial resolution on the images reconstructed with SR-DLR and conventional reconstructions.

Results

The spatial resolution of SR-DLR was higher than conventional reconstructions; the 10 % modulation transfer function of hybrid IR (HIR), DLR, and SR-DLR were 0.792-, 0.976-, and 1.379 cycle/mm, respectively. At the same time, image noise was lowest (HIR: 21.1-, DLR: 19.0-, and SR-DLR: 13.1 HU). SR-DLR could accurately assess coronary artery stenosis and the lumen of the implanted stent graft.

Conclusions

SR-DLR can obtain CT images with high spatial resolution and lower noise without special CT equipments, and will help diagnose coronary artery disease in CCTA and other CT examinations that require high spatial resolution.

[Validation of Optimal Imaging Conditions for Coronary Computed Tomography Angiography Using High-definition Mode and Deep Learning Image Reconstruction Algorithm]

PURPOSE

To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination.

METHOD

A chest phantom and an in-house phantom using 3D printer were scanned with a 256-row detector CT scanner. The scan parameters were as follows - acquisition mode: ON (HD mode) and OFF (normal resolution [NR] mode), rotation time: 0.28 s/rotation, beam coverage width: 160 mm, and the radiation dose was adjusted based on CT-AEC. Image reconstruction was performed using ASiR-V (Hybrid-IR), TrueFidelity Image (DLIR), and HD-Standard (HD mode) and Standard (NR mode) reconstruction kernels. The task-based transfer function (TTF) and noise power spectrum (NPS) were measured for image evaluation, and the detectability index (d') was calculated. Visual evaluation was also performed on an in-house coronary phantom.

RESULT

The in-plane TTF was better for the HD mode than for the NR mode, while the z-axis TTF was lower for DLIR than for Hybrid-IR. The NPS values in the high-frequency region were higher for the HD mode compared to those for the NR mode, and the NPS was lower for DLIR than for Hybrid-IR. The combination of HD mode and DLIR showed the best value for in-plane d', whereas the combination of NR mode and DLIR showed the best value for z-axis d'. In the visual evaluation, the combination of NR mode and DLIR showed the best values from a noise index of 45 HU.

CONCLUSION

The optimal combination of HD mode and DLIR depends on the image noise level, and the combination of NR mode and DLIR was the best imaging condition under noisy conditions.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study

Objective. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.Approach.We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition.Main results.We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners.Significance.This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.

Optimization of CT pulmonary angiography for pulmonary embolism using task-based image quality assessment and diagnostic reference levels: A multicentric study

PURPOSE

To establish size-specific diagnostic reference levels (DRLs) for pulmonary embolism (PE) based on patient CT examinations performed on 74 CT devices. To assess task-based image quality (IQ) for each device and to investigate the variability of dose and IQ across different CTs. To propose a dose/IQ optimization.

METHODS

1051 CT pulmonary angiography dose data were collected. DRLs were calculated as the 75th percentile of CT dose index (CTDI) for two patient categories based on the thoracic perimeters. IQ was assessed with two thoracic phantom sizes using local acquisition parameters and three other dose levels. The area under the ROC curve (AUC) of a 2 mm low perfused vessel was assessed with a non-prewhitening with eye-filter model observer. The optimal IQ-dose point was mathematically assessed from the relationship between IQ and dose.

RESULTS

The DRLs of CTDIvol were 6.4 mGy and 10 mGy for the two patient categories. 75th percentiles of phantom CTDIvol were 6.3 mGy and 10 mGy for the two phantom sizes with inter-quartile AUC values of 0.047 and 0.066, respectively. After the optimization, 75th percentiles of phantom CTDIvol decreased to 5.9 mGy and 7.55 mGy and the interquartile AUC values were reduced to 0.025 and 0.057 for the two phantom sizes.

CONCLUSION

DRLs for PE were proposed as a function of patient thoracic perimeters. This study highlights the variability in terms of dose and IQ. An optimization process can be started individually and lead to a harmonization of practice throughout multiple CT sites.

Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm?

BACKGROUND

ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose.

OBJECTIVES

To define the potential dose reduction using DLIR with an anthropomorphic phantom.

METHOD

An anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed.

RESULTS

DLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50.

CONCLUSION

DLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.

Computed tomography dose index determination in dose modulation prospectively involving the third-generation iterative reconstruction and noise index

PURPOSE

Optimizing CT protocols is challenging in the presence of automatic dose modulation because the CT dose index (CTDIvol) at different patient sizes is unknown to the operator. The task is more difficult when both the image quality index and iterative reconstruction prospectively affect the dose determination. It is of interest in practice to be informed of the CTDIvol during the protocol initialization and evaluation. It was our objective to obtain a predictive relationship between CTDIvol, the image quality index, and iterative reconstruction strength at various patient sizes.

METHODS

Dose modulation data were collected on a GE Revolution 256-slice scanner utilizing a Mercury phantom and selections of the noise index (NI) from 8 to 17, the third generation iterative reconstruction (ASIR-V) from 0% to 80%, and phantom diameters from 16 to 36 cm. The fixed parameters were 120 kVp, a pitch of .984, and a collimation of 40 mm with a primary slice width of 2.5 mm. The CTDIvol per diameter was based on the average tube current over three adjacent slices (same or similar diameter) multiplied by a conversion factor between the average mA of the series and the reported CTDIvol. The relationship between CTDIvol, NI, and ASIR-V for each diameter was fitted with a 2nd order polynomial of ASIR-V multiplied by a power law of NI.

RESULTS

The ASIR-V fit parameters versus diameter followed a Lorentz function while the NI exponent versus diameter followed an exponential growth function. The CTDIvol predictions were accurate within 15% compared to phantom results on a separate GE Revolution. For clinical relevance, the phantom diameter was converted to an abdomen or chest equivalent diameter and was well matched to patient data.

CONCLUSION

The fitted relationship for CTDIvol. for given values of NI and ASIR-V blending for a range of phantom sizes was a good match to phantom and patient data. The results can be of direct help for selecting adequate parameters in CT protocol development.

Objective image quality assurance in cone-beam CT: Test methods, analysis, and workflow in longitudinal studies

BACKGROUND

Standards for image quality evaluation in multi-detector CT (MDCT) and cone-beam CT (CBCT) are evolving to keep pace with technological advances. A clear need is emerging for methods that facilitate rigorous quality assurance (QA) with up-to-date metrology and streamlined workflow suitable to a range of MDCT and CBCT systems.

PURPOSE

To evaluate the feasibility and workflow associated with image quality (IQ) assessment in longitudinal studies for MDCT and CBCT with a single test phantom and semiautomated analysis of objective, quantitative IQ metrology.

METHODS

A test phantom (CorgiTM Phantom, The Phantom Lab, Greenwich, New York, USA) was used in monthly IQ testing over the course of 1 year for three MDCT scanners (one of which presented helical and volumetric scan modes) and four CBCT scanners. Semiautomated software analyzed image uniformity, linearity, contrast, noise, contrast-to-noise ratio (CNR), 3D noise-power spectrum (NPS), modulation transfer function (MTF) in axial and oblique directions, and cone-beam artifact magnitude. The workflow was evaluated using methods adapted from systems/industrial engineering, including value stream process modeling (VSPM), standard work layout (SWL), and standard work control charts (SWCT) to quantify and optimize test methodology in routine practice. The completeness and consistency of DICOM data from each system was also evaluated.

RESULTS

Quantitative IQ metrology provided valuable insight in longitudinal quality assurance (QA), with metrics such as NPS and MTF providing insight on root cause for various forms of system failure-for example, detector calibration and geometric calibration. Monthly constancy testing showed variations in IQ test metrics owing to system performance as well as phantom setup and provided initial estimates of upper and lower control limits appropriate to QA action levels. Rigorous evaluation of QA workflow identified methods to reduce total cycle time to ∼10 min for each system-viz., use of a single phantom configuration appropriate to all scanners and Head or Body scan protocols. Numerous gaps in the completeness and consistency of DICOM data were observed for CBCT systems.

CONCLUSION

An IQ phantom and test methodology was found to be suitable to QA of MDCT and CBCT systems with streamlined workflow appropriate to busy clinical settings.

Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment

BACKGROUND

Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation dose.

PURPOSE

This study first evaluated tube voltages and radiation doses applied in patients who underwent coronary CT angiography with PCCT and energy-integrating detector CT (EID-CT). Next, image quality of PCCT and EID-CT was objectively evaluated in a phantom study simulating different patient sizes at these tube voltages and radiation doses.

METHODS

We conducted a retrospective analysis of clinical doses of patients scanned on a conventional and PCCT system. Average patient water equivalent diameters for different tube voltages were extracted from the dose reports for both EID-CT and PCCT. A conical phantom made of polyethylene with multiple diameters (26/31/36 cm) representing different patient sizes and containing an iodine insert was scanned with a EID-CT scanner using tube voltages and phantom diameters that match the patient scans and characteristics. Next, phantom scans were made with PCCT at a fixed tube voltage of 120 kV and with CTDIVOL values and phantom diameters identical to the EID-CT scans. Clinical image reconstructions at 0.6 mm slice thickness for conventional CT were compared to PCCT images with 0.4 mm slice thickness. Image quality was quantified using the detectability index (d'), which estimated the visibility of a 3 mm diameter contrast-enhanced coronary artery by considering noise, contrast, resolution, and human visual perception. Alongside d', noise, contrast and resolution were also individually assessed. In addition, the influence of various kernels (Bv40/Bv44/Bv48/Bv56), quantum iterative reconstruction strengths (QIR, 3/4) and monoenergetic levels (40/45/50/55 keV) for PCCT on d' was investigated.

RESULTS

In this study, 143 patients were included: 47 were scanned on PCCT (120 kV) and the remaining on EID-CT (74 small-sized at 70 kV, 18 medium-sized at 80 kV and four large-sized at 90 kV). EID-CT showed 7%-17% higher d' than PCCT with Bv40 kernel and strength four for small/medium patients. Lower monoenergetic images (40 keV) helped mitigate the difference to 1%-6%. For large patients, PCCT's detectability was up to 31% higher than EID-CT. PCCT has thinner slices but similar noise levels for similar reconstruction parameters. The noise increased with lower keV levels in PCCT (≈30% increase), but higher QIR strengths reduced noise. PCCT's iodine contrast was stable across patient sizes, while EID-CT had 33% less contrast in large patients than in small-sized patients.

CONCLUSION

At 120 kV, thin slice PCCT enables CCTA in phantom scans representing large patients without raising radiation dose or affecting vessel detectability. However, higher doses are needed for small and medium-sized patients to obtain a similar image quality as in EID-CT. The alternative of using lower mono-energetic levels requires further evaluation in clinical practice.

The effect of a pre-reconstruction process in a filtered back projection reconstruction on an image quality of a low tube voltage computed tomography

This study aims to evaluate the effect of pre-reconstruction process for low tube voltage computed tomography (CT) on image quality of filtered back projection (FBP) reconstruction. Small and large quality assurance water phantoms (19 and 33 cm diameter) were scanned on a third-generation dual-source CT with 70 kVp and 120 kVp at various dose levels. Image quality was assessed in terms of the noise power spectrum (NPS) and task-based transfer function (TTF). NPSs and TTFs in the small phantom were comparable between 70 and 120 kVp protocols. In the large phantom, the curves of the NPS changed and the TTF decreased even at the high-dose levels for 70 kVp protocol compared to 120 kVp protocol. Our results indicated that the pre-reconstruction process is performed in low tube voltage CT for large objects even for the FBP reconstruction and has an effect on the image quality.

Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study

PURPOSE

The purpose of this study was to compare the performance of Precise IQ Engine (PIQE) and Advanced intelligent Clear-IQ Engine (AiCE) algorithms on image-quality according to the dose level in a cardiac computed tomography (CT) protocol.

MATERIALS AND METHODS

Acquisitions were performed using the CT ACR 464 phantom at three dose levels (volume CT dose indexes: 7.1/5.2/3.1 mGy) using a prospective cardiac CT protocol. Raw data were reconstructed using the three levels of AiCE and PIQE (Mild, Standard and Strong). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone and acrylic inserts were computed. The detectability index (d') was computed to model the detectability of the coronary lumen (350 Hounsfield units and 4-mm diameter) and non-calcified plaque (40 Hounsfield units and 2-mm diameter).

RESULTS

Noise magnitude values were lower with PIQE than with AiCE (-13.4 ± 6.0 [standard deviation (SD)] % for Mild, -20.4 ± 4.0 [SD] % for Standard and -32.6 ± 2.6 [SD] % for Strong levels). The average NPS spatial frequencies shifted towards higher frequencies with PIQE than with AiCE (21.9 ± 3.5 [SD] % for Mild, 20.1 ± 3.0 [SD] % for Standard and 12.5 ± 3.5 [SD] % for Strong levels). The TTF values at fifty percent (f50) values shifted towards higher frequencies with PIQE than with AiCE for acrylic inserts but, for bone inserts, f50 values were found to be close. Whatever the dose and DLR level, d' values of both simulated cardiac lesions were higher with PIQE than with AiCE. For the simulated coronary lumen, d' values were better by 35.1 ± 9.3 (SD) % on average for all dose levels for Mild, 43.2 ± 5.0 (SD) % for Standard, and 62.6 ± 1.2 (SD) % for Strong levels.

CONCLUSION

Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.

Half-value layer measurements using solid-state detectors and single-rotation technique with lead apertures in spiral computed tomography with and without a tin filter

Solid-state detectors (SSDs) may be used along with a lead collimator for half-value layer (HVL) measurement using computed tomography (CT) with or without a tin filter. We aimed to compare HVL measurements obtained using three SSDs (AGMS-DM+ , X2 R/F sensor, and Black Piranha) with those obtained using the single-rotation technique with lead apertures (SRTLA). HVL measurements were performed using spiral CT at tube voltages of 70-140 kV without a tin filter and 100-140 kV (Sn 100-140 kV) with a tin filter in increments of 10 kV. For SRTLA, a 0.6-cc ionization chamber was suspended at the isocenter to measure the free-in-air kerma rate (K ˙ air ) values. Five apertures were made on the gantry cover using lead sheets, and four aluminum plates were placed on these apertures. HVLs in SRTLA were obtained fromK ˙ air decline curves. Subsequently, SSDs inserted into the lead collimator were placed on the gantry cover and used to measure HVLs. Maximum HVL differences of AGMS-DM+ , X2 R/F sensor, and Black Piranha with respect to SRTLA without/with a tin filter were - 0.09/0.6 (only two Sn 100-110 kV) mm, - 0.50/ - 0.6 mm, and - 0.17/(no data available) mm, respectively. These values were within the specification limit. SSDs inserted into the lead collimator could be used to measure HVL using spiral CT without a tin filter. HVLs could be measured with a tin filter using only the X2 R/F sensor, and further improvement of its calibration accuracy with respect to other SSDs is warranted.

Task-based assessment of resolution properties of CT images with a new index using deep convolutional neural network

In this study, we propose a method for obtaining a new index to evaluate the resolution properties of computed tomography (CT) images in a task-based manner. This method applies a deep convolutional neural network (DCNN) machine learning system trained on CT images with known modulation transfer function (MTF) values to output an index representing the resolution properties of the input CT image [i.e., the resolution property index (RPI)]. Sample CT images were obtained for training and testing of the DCNN by scanning the American Radiological Society phantom. Subsequently, the images were reconstructed using a filtered back projection algorithm with different reconstruction kernels. The circular edge method was used to measure the MTF values, which were used as teacher information for the DCNN. The resolution properties of the sample CT images used to train the DCNN were created by intentionally varying the field of view (FOV). Four FOV settings were considered. The results of adapting this method to the filtered back projection (FBP) and hybrid iterative reconstruction (h-IR) images indicated highly correlated values with the MTF10% in both cases. Furthermore, we demonstrated that the RPIs could be estimated in the same manner under the same imaging conditions and reconstruction kernels, even for other CT systems, where the DCNN was trained on CT systems produced by the same manufacturer. In conclusion, the RPI, which is a new index that represents the resolution property using the proposed method, can be used to evaluate the resolution of a CT system in a task-based manner.

A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms

PURPOSE

To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images.

METHODS

Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index.

RESULTS

The five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values.

CONCLUSIONS

The improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.

Dual-energy computed tomography quality control: Initial experiences with a semi-automatic analysis tool

PURPOSE

A quality control (QC) system for dual-energy CT (DECT) was developed. The scope of the QC system was to monitor both the constancy of the CT images and the software used in calculating the DECT derived maps. Longitudinal analysis was based on a standard imaging protocol, a commercial multi-energy phantom, and a semi-automatic analysis tool.

METHODS

The phantom consisted of an elliptical body section with round slots for interchangeable inserts. It was scanned with 90kVp/Sn150kVp, automatic tube current modulation, and 9.6 mGy CTDIvol. From the two conventional CT images, scanner manufacturer's software was used to provide virtual monoenergetic images at two different energies, effective atomic number (Zeff) maps, and iodine concentration maps. The images were analyzed using an open-source tool allowing user-selected statistics of interest. The means and standard deviations of the phantom background and the iodine, calcium, and water inserts were recorded. The QC tool is available at github.com/tomakela/dectqatool.

RESULTS

The obtained results were generally highly consistent over time, except for the smaller diameter iodine inserts. A small change inZeff was observed after a DECT software update. The developed QC tool aided the analysis robustness: the segmentations were modifiable when needed, and small rotations or air bubbles in the water insert were easily corrected.

CONCLUSION

The developed QC system provided easy-to-use workflow for constancy measurements. A small deviation due to change in the post-processing was detected. The proposed imaging protocol and analysis steps, and the reported measurement variations can aid in determining action levels for DECT QC.

A systematic task-based image quality assessment of photon-counting and energy integrating CT as a function of reconstruction kernel and phantom size

BACKGROUND

Image quality of photon-counting and energy integrating CT scanners changes with object size, dose to the object, and kernel selection.

PURPOSE

To comprehensively compare task-generic image quality of photon-counting CT (PCCT) and energy integrating CT (EICT) systems as a function of phantom size, dose, and reconstruction kernel.

METHODS

A size-variant phantom (Mercury Phantom 3.0) was used to characterize the image quality of PCCT and EICT systems as a function of object size. The phantom contained five cylinders attached by slanted tapered sections. Each cylinder contained two sections: one uniform for noise, and the other with inserts for spatial resolution and contrast measurements. The phantom was scanned on Siemens' SOMATOM Force and NAEOTOM Alpha at 1.18 and 7.51 mGy without tube current modulation. CTDIvol was matched across two systems by setting the required tube currents, else, all other acquisition settings were fixed. CT sinograms were reconstructed using FBP and iterative (ADMIRE2 - Force; QIR2 - Alpha) algorithms with Body regular (Br) kernels. Noise Power Spectrum (NPS), Task Transfer Function (TTF), contrast-to-noise ratio (CNR), and detectability index (d') for a task of identifying 2-mm disk were evaluated based on AAPM TG-233 metrology using imQuest, an open-source software package. Averaged noise frequency (fav ) and 50% cut-off frequency for TTF (f50 ) were used as scalar metrics to quantify noise texture and spatial resolution, respectively. The difference between image quality metrics' measurements was calculated as IQPCCT - IQEICT .

RESULTS

From Br40 (soft) to Br64 (sharp), f50 for air insert increased from 0.35 mm-1  ± 0.04 (standard deviation) to 0.76 mm-1  ± 0.17, 0.34 mm-1  ± 0.04 to 0.77 mm-1  ± 0.17, 0.37 mm-1  ± 0.02 to 0.95 mm-1  ± 0.17 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively, when averaged over all sizes and dose levels. Similarly, from Br40 to Br64, noise magnitude increased from 10.86 HU ± 7.12 to 38.61 HU ± 18.84, 10.94 HU ± 7.08 to 38.82 HU ± 18.70, 13.74 HU ± 11.02 to 52.11 HU ± 26.22 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. The difference in fav was consistent across all sizes and dose levels. PCCT-70keV-VMI showed better consistency in contrast measurements for iodine and bone inserts than PCCT-T3D and EICT; however, PCCT-T3D had higher contrast for both inserts. From Br40 to Br64, considering all sizes and dose levels, CNR for iodine insert decreased from 52.30 ± 46.44 to 12.18 ± 10.07, 40.42 ± 33.42 to 9.48 ± 7.16, 39.94 ± 37.60 to 7.84 ± 6.67 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively.

CONCLUSIONS

Both PCCT image types, T3D and 70-keV-VMI exhibited similar or better noise, contrast, CNR than EICT when comparing kernels with similar names. For 512 × 512 matrix, PCCT's sharp kernels had lower resolution than EICT's sharp kernels. For all image quality metrics, except extreme low, every dose condition had a similar set of IQ-matching kernels. It suggests that considering patient size and dose level to determine IQ-matching kernel pairs across PCCT and EICT systems may not be imperative while translating protocols, except when the signal to the detector is extremely low.

Grating-based x-ray dark-field CT for lung cancer diagnosis in mice

BACKGROUND

The low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.

METHODS

Eight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.

RESULTS

The nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).

CONCLUSIONS

Compared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.

RELEVANCE STATEMENT

Dark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.

KEY POINTS

• Lung cancer progression was observed using x-ray dark-field CT. • Dark-field modality complements with attenuation modality in lung nodule detection. • Dark-field modality showed a detectability index higher than that attenuation in nodule detection.

Image quality assessment in low-dose COVID-19 chest CT examinations

BACKGROUND

Low-dose thoracic protocols were developed massively during the COVID-19 outbreak.

PURPOSE

To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols.

MATERIAL AND METHODS

COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models.

RESULTS

In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ.

CONCLUSION

Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study

OBJECTIVES

To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms.

METHODS

An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions.

RESULTS

NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy.

CONCLUSIONS

DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

BACKGROUND

Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial.

OBJECTIVES

To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V).

AIMS

This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality.

METHOD

In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared.

RESULTS

The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30.

CONCLUSION

DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

AntiHalluciNet: A Potential Auditing Tool of the Behavior of Deep Learning Denoising Models in Low-Dose Computed Tomography

Gaining the ability to audit the behavior of deep learning (DL) denoising models is of crucial importance to prevent potential hallucinations and adversarial clinical consequences. We present a preliminary version of AntiHalluciNet, which is designed to predict spurious structural components embedded in the residual noise from DL denoising models in low-dose CT and assess its feasibility for auditing the behavior of DL denoising models. We created a paired set of structure-embedded and pure noise images and trained AntiHalluciNet to predict spurious structures in the structure-embedded noise images. The performance of AntiHalluciNet was evaluated by using a newly devised residual structure index (RSI), which represents the prediction confidence based on the presence of structural components in the residual noise image. We also evaluated whether AntiHalluciNet could assess the image fidelity of a denoised image by using only a noise component instead of measuring the SSIM, which requires both reference and test images. Then, we explored the potential of AntiHalluciNet for auditing the behavior of DL denoising models. AntiHalluciNet was applied to three DL denoising models (two pre-trained models, RED-CNN and CTformer, and a commercial software, ClariCT.AI [version 1.2.3]), and whether AntiHalluciNet could discriminate between the noise purity performances of DL denoising models was assessed. AntiHalluciNet demonstrated an excellent performance in predicting the presence of structural components. The RSI values for the structural-embedded and pure noise images measured using the 50% low-dose dataset were 0.57 ± 31 and 0.02 ± 0.02, respectively, showing a substantial difference with a p-value < 0.0001. The AntiHalluciNet-derived RSI could differentiate between the quality of the degraded denoised images, with measurement values of 0.27, 0.41, 0.48, and 0.52 for the 25%, 50%, 75%, and 100% mixing rates of the degradation component, which showed a higher differentiation potential compared with the SSIM values of 0.9603, 0.9579, 0.9490, and 0.9333. The RSI measurements from the residual images of the three DL denoising models showed a distinct distribution, being 0.28 ± 0.06, 0.21 ± 0.06, and 0.15 ± 0.03 for RED-CNN, CTformer, and ClariCT.AI, respectively. AntiHalluciNet has the potential to predict the structural components embedded in the residual noise from DL denoising models in low-dose CT. With AntiHalluciNet, it is feasible to audit the performance and behavior of DL denoising models in clinical environments where only residual noise images are available.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD).Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score.Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic.Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study

PURPOSE

This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR).

METHODS

This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10 mGy; low: 3.9 mGy; ultralow: 2.0 mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR.

RESULT

The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP-a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses.

CONCLUSION

DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.

Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image

This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPSR=P. NPSsR=256, 128, 64, 32, 16, and 8 were obtained and compared to assess their dependency on ROI size. In the proposed method, the true NPS was numerically modeled as an NPSmodel, with adjustable parameters, and a noise image with the property of the NPSmodel was generated. From the generated noise image, the NPS was measured using the conventional method with a P × P pixel ROI size; the obtained NPS was referred to as NPS'R=P. The adjustable parameters of the NPSmodel were optimized such that NPS'R=P was most similar to NPSR=P. When NPS'R=P was almost equivalent to NPSR=P, the NPSmodel was considered the true NPS. NPSsR=256, 128, 64, 32, 16, and 8 obtained using the conventional method were dependent on the ROI size. Conversely, the NPSs (optimized NPSsmodel) measured using the proposed method were not dependent on the ROI size, even when a much smaller ROI (P = 16 or 8) was used. The proposed method for NPS measurement was confirmed to be precise, independent of the ROI size, and useful for measuring local NPSs using a small ROI.

Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography

OBJECTIVES

To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA).

METHODS

Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF).

RESULTS

SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects.

CONCLUSION

SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms.

CLINICAL RELEVANCE STATEMENT

The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability.

KEY POINTS

• SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.

Exploration of the pulse pileup effects in a clinical CdTe-based photon-counting computed tomography

BACKGROUND

High tube current generates a high flux of x-rays to photon counting detectors (PCDs) that can potentially result in the piling up of pulses formed by concurrent photons, which can cause count loss and energy resolution degradation.

PURPOSE

To evaluate the performance of clinical photon-counting CT (PCCT) systems in high flux, potentially influenced by pulse pileup effects, in terms of task-generic image quality metrics.

METHODS

A clinical phantom was scanned on a commercial PCCT scanner (NAEOTOM Alpha, Siemens) at 120 kV under fourteen different tube current levels (40-1000 mA) with a rotation time of 0.25 s and a pitch of 1. The dose levels corresponded to CTDIvol (32 cm phantom) of 0.79-19.8 mGy. CT sinograms were reconstructed using QIR-off mode (noniterative reconstruction algorithm), Br44 kernel, and a voxel size of0.4102 × 0.4102 × 3 mm 3 $0.4102 \times 0.4102 \times 3{\mathrm{\ mm}}^3$ . imQuest, an open-source MATLAB-based software package was used to calculate noise power spectrum (NPS), task transfer function (TTF), contrast-to-noise ratio (CNR), and CT number according to AAPM Task Group 233 metrology.

RESULTS

The 50% cut-off frequency of TTF (f50 ) remained mostly constant across all higher tube currents for all inserts, namely polyethylene, bone, air, and acrylic. Using the lowest two data points (40 and 80 mA), the expected relationship between noise magnitude and tube current was determined to be noise∝ $ \propto \ $ mA-0.47 . The measured noise magnitude were up to 11.1% higher than the expected value at the highest tube current. The average frequency of NPS (fav ) decreased from 0.32 to 0.29 mm-1 as tube current increased from 40 to 1000 mA. No considerable effects were observed in CT number measurement of any insert; however, CT numbers for air and bone changed almost monotonically as tube current increased. Absolute CNR increased monotonically for all inserts; however, the difference between measured and expected CNRs were approximately -6% to 12% across all tube currents.

CONCLUSIONS

Increasing tube currents did not affect the spatial resolution, but slightly affected the CT number and noise measurements of the clinical PCCT system. However, the effects were only considerable at clinically irrelevant tube currents used on a small 20-cm phantom. In general clinical practices, automatic exposure control techniques are used to decrease the variation of flux on the detector, which alleviates the chances of detector saturation due to high count rates. The observed effects could be due to pulse pileup, signal-dependent filtration of the system, or nonlinearities in the reconstruction algorithm. In conclusion, either the deadtime of the detector used in the photon-counting CT system is shorter such that count losses due to pulse pileup are negligible, or pulse pileup has inconsiderable effects on the image quality of clinical photon-counting CT systems in routine clinical practice due to possible corrections applied on the system.

Spectral performance of two split-filter dual-energy CT systems: A phantom study

BACKGROUND

Recently, a second generation of split filter dual-energy CT (SFCT) platform has been developed. The thicknesses of the gold and tin filters used to obtain both low- and high-energy spectra have been changed. These differences in filter thickness may affect the spectral separation between the two spectra and thus the quality of spectral images.

PURPOSE

To compare the spectral performance of two Split-Filter Dual-Energy CT systems (SFCT-1st and SFCT-2nd ) on virtual monoenergetic images (VMIs) and iodine map.

METHODS

A Multi-Energy CT phantom was scanned on two SFCT with a tube voltage of 120 kVp for both systems (SFCT-1st -120 and SFCT-2nd -120) and 140 kVp only for the second generation (SFCT-2nd -140). Acquisitions were performed on the phantom with a CTDIvol close to 11 mGy. Noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated on VMIs from 40 to 70 keV. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions on VMIs. Hounsfield Unit (HU) accuracy was assessed on VMIs and the accuracy of iodine concentration was assessed on iodine maps.

RESULTS

For all keV, noise magnitude values were lower with the SFCT-2nd -120 than with the SFCT-1st -120 (on average: -22.5 ± 2.9%) and higher with the SFCT-2nd -140 than with the SFCT-2nd -120 (on average: 25.0 ± 6.2%). Average NPS spatial frequencies (fav ) were lower with the SFCT-1st -120 than with the SFCT-2nd -120 (-6.0 ± 0.5%) and the SFCT-2nd -140 (-3.6 ± 1.6%). Similar TTF50% values were found for both systems and both kVp for blood and iodine inserts at 2 mg/mL (0.29 ± 0.01 mm-1 ) and at 4 mg/mL (0.31 ± 0.01 mm-1 ). d' values peaked at 40 keV for the SFCT-2nd and at 70 keV for the SFCT-1st . Highest d' values were found for the SFCT-2nd -120 for both simulated lesions. Accuracy of HU values and iodine concentration was higher with the SFCT-2nd than with the SFCT 1st .

CONCLUSION

Compared to the SFCT-1st , with similar spatial resolution and noise texture values, the SFCT-2nd -120 exhibited the lowest values for noise magnitude, the highest detectability index values, and more accurate HU values and iodine concentrations.