IMAGE QUALITY AND POTENTIAL DOSE REDUCTION USING ADVANCED MODELED ITERATIVE RECONSTRUCTION (ADMIRE) IN ABDOMINAL CT - A REVIEW

Evaluating the impact of dose monitoring software alerts on radiation dose reduction in computed tomography: A systematic review

INTRODUCTION

Radiation Dose Monitoring Software (DMS) tools have been developed to monitor doses and alert computed tomography (CT) users of high radiation exposure. However, the causal factors for alerts and the impact of DMS in dose optimisation are poorly understood.

AIM

This review aims to identify high-dose CT examinations triggering alerts and their determinants, and to assess if the alerts from DMS help to reduce CT dose levels.

METHODS

To identify relevant articles published to December 2023, an electronic search of Medline, Scopus, CINAHL, Embase, and the Web of Science was undertaken. Reference lists of published articles were also assessed to identify further articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to evaluate articles for relevance. Articles were included if they used the DMS tool to detect high-dose events that issued alerts in CT and were published in English.

RESULTS

The search resulted in 83 articles, of which, nine were included after a thorough screening process. High dose alerts most often occurred in CT chest, CT head/brain, CT Chest/abdomen/pelvis, and CT abdomen/pelvis with alert percentages ranging from 1.45% to 10.21%, 1.54% to 4.18%, 4.48% to 6.60%, and 4.47% respectively. Alerts were mainly triggered by overweight patients, scan repetition, miscentering of the patients, extra CT study added, orthopaedic hardware in the scanning area and scanning beyond the desire anatomy. Most of the studies reviewed show that DMS tools played a role in reducing the number of high-dose events that trigger alerts.

CONCLUSION

DMS tools are valuable in automatically identifying high-dose CT protocols, enabling quick investigation and dose optimisation. The high-dose events occurred due to patient and technical factors, which can be mitigated through proper monitoring and investment in both technological resources and staff training.

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study

To evaluate the effect of a vendor-agnostic deep learning denoising (DLD) algorithm on diagnostic image quality of non-contrast cranial computed tomography (ncCT) across five CT scanners.This retrospective single-center study included ncCT data of 150 consecutive patients (30 for each of the five scanners) who had undergone routine imaging after minor head trauma. The images were reconstructed using filtered back projection (FBP) and a vendor-agnostic DLD method. Using a 4-point Likert scale, three readers performed a subjective evaluation assessing the following quality criteria: overall diagnostic image quality, image noise, gray matter-white matter differentiation (GM-WM), artifacts, sharpness, and diagnostic confidence. Objective analysis included evaluation of noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and an artifact index for the posterior fossa.In subjective image quality assessment, DLD showed constantly superior results compared to FBP in all categories and for all scanners (p<0.05) across all readers. The objective image quality analysis showed significant improvement in noise, SNR, and CNR as well as for the artifact index using DLD for all scanners (p<0.001).The vendor-agnostic deep learning denoising algorithm provided significantly superior results in the subjective as well as in the objective analysis of ncCT images of patients with minor head trauma concerning all parameters compared to the FBP reconstruction. This effect has been observed in all five included scanners. · Significant improvement of image quality for 5 scanners due to the vendor-agnostic DLD. · Subjects were patients with routine imaging after minor head trauma. · Reduction of artifacts in the posterior fossa due to the DLD. · Access to improved image quality even for older scanners from different vendors. · Kapper C, Müller L, Kronfeld A et al. Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study. Fortschr Röntgenstr 2024; DOI 10.1055/a-2290-4781.

CT radiation dose reduction with tin filter for localisation/characterisation level image quality in PET-CT: a phantom study

BACKGROUND

The tin filter has allowed radiation dose reduction in some standalone diagnostic computed tomography (CT) applications. Yet, 'low-dose' CT scans are commonly used in positron emission tomography (PET)-CT for lesion localisation/characterisation (L/C), with higher noise tolerated. Thus, dose reductions permissible with the tin filter at this image quality level may differ. The aim was to determine the level of CT dose reduction permitted with the tin filter in PET-CT, for comparable image quality to the clinical reference standard (CRS) L/C CT images acquired with standard filtration.

MATERIALS AND METHODS

A whole-body CT phantom was scanned with standard filtration in CRS protocols, using 120 kV with 20mAs-ref for bone L/C (used in 18F-Sodium Fluoride (NaF) PET-CT) and 40mAs-ref for soft tissue L/C (used in 18F-Fluorodeoxyglucose (FDG) PET-CT), followed by tin filter scans at 100 kV (Sn100kV) and 140 kV (Sn140kV) with a range of mAs settings. For each scan, effective dose (ED) in an equivalent-sized patient was calculated, and image quality determined in 5 different tissues through quantitative (contrast-to-noise ratio) and qualitative (visual) analyses. The relative dose reductions which could be achieved with the tin filter for comparable image quality to CRS images were calculated.

RESULTS

Quantitative analysis demonstrated dose savings of 50-76% in bone, 27-51% in lung and 8-61% in soft tissue with use of the tin filter at Sn100kV. Qualitative analysis demonstrated dose reductions using Sn100kV in general agreement with the dose reductions indicated by quantitative analysis. Overall, CT dose reductions of around 85% were indicated for NaF bone PET-CT, allowing whole-body CT at just 0.2mSv ED, and a 30-40% CT dose reduction for FDG PET-CT using Sn100kV (1.7-2.0mSv), providing comparable image quality to current CRS images with standard filtration. Sn140kV demonstrated limited value in CT dose reduction.

CONCLUSIONS

Large CT dose reductions can be made using the tin filter at Sn100kV, when imaging bone, lung and soft tissue at L/C level CT image quality in PET-CT. As well as reducing the risk of inducing a cancer in later life, such dose reductions may also impact PET-CT practice, such as justifying cross-sectional over planar imaging or justifying PET-CT in younger patients.

[Deep Learning Reconstruction Algorithm Combined With Smart Metal Artifact Reduction Technique Improves Image Quality of Upper Abdominal CT in Critically Ill Patients]

Objective

To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction (DLMAR) on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electrocardiographic (ECG) monitoring.

Methods

A total of 102 patients were retrospectively enrolled. All subjects were critically ill patients who were unable to raise their arms and required ECG monitoring. Images were reconstructed using 6 algorithms, including filtered back projection (FBP), iterative reconstruction (IR), deep learning (DL), FBP combined with smart metal artifact reduction (FBPMAR), adaptive statistical iterative reconstruction-V combined with smart metal artifact reduction (IRMAR), and DLMAR. A quantitative analysis of CT values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) was conducted in regions without metal artifacts and regions with metal artifacts in the liver, as well as the tissues, including those from the liver, spleen, pancreas, and aorta, between the two arms. Qualitative analysis of electrode metal artifacts, the visualization of the structures between the two arms, and image noise was performed with a 5-point scoring system (1=worst and 5=best).

Results

In the regions of the liver with metal artifacts, there was a significant difference between the CT values of the DLMAR group ([98.5±9.8] Hounsfield units [HU]) and those of the FBP group ([73.7±5.6] HU), the IR group ([75.3±7.5] HU), and the DL group ([66.3±11.4] HU) (P<0.01). There was no significant difference between the CT values of the DLMAR group and those of the FBPMAR group ([99.8±4.8] HU) and the IRMAR group ([99.6±3.4] HU) (P>0.05). The noise of the DLMAR group was found to be significantly lower than that of the other groups (P<0.01). Furthermore, the SNR and CNR of the DLMAR group were also found to be higher than those of the other groups (P<0.01). In the tissue region between the two arms, the differences in CT values among the six groups were not statistically significant (P>0.05). The noise of the DLMAR group was lower than those of the other groups (P<0.01), and the SNR and CNR of the DLMAR group were higher than those of the other groups (P<0.01). In terms of the removal of metal artifacts, the scores of the FBPMAR, IRMAR, and DLMAR groups (4.27±0.32, 4.44±0.34, and 4.61±0.28, respectively) were higher than those of the FBP, IR, and DL groups (1.36±0.54, 1.32±0.45, and 1.24±0.46, respectively) (P<0.01). The DLMAR group also had a higher score of 4.62±0.37 in the visualization of structures between the two arms and 4.53±0.39 in the noise reduction of images, both of which were higher than those of the other groups (P<0.01).

Conclusion

DLMAR reduces artifacts, decreases noise, and improves the quality of abdominal CT imaging in critically ill patients who are unable to raise their arms and require ECG monitoring.

Performance of Iterative Reconstruction in Image Space Algorithm in Combination with Automatic Tube Current Modulation Compared to Filtered Back Projection in Brain CT Scan

Background

High-quality images with minimum radiation dose are considered a challenge in Computed Tomography (CT) scans.

Objective

The current study aimed to assess the efficacy of the Iterative Reconstruction in Image Space (IRIS) algorithm combined with Automatic Tube Current Modulation (ATCM) compared to Filtered Back Projection (FBP) in brain CT scans.

Material and Methods

In this cross-sectional study, 200 patients underwent to brain CT scan, and images were then reconstructed using both FBP and IRIS. The CT Number (CTN), noise, and Signal-to-Noise Ratio (SNR) were computed for different tissues from CT images. The performance of two algorithms under different exposure conditions was evaluated using a water phantom. Two experienced radiologists assessed the image quality. Volume CT Dose Index (CTDIvol) and Dose Length Product (DLP) were recorded for each scan.

Results

FBP reconstruction exhibited higher noise and lower SNR compared to IRIS, both with and without ATCM. Noise levels significantly increased for FBP combined with ATCM. Subjective analysis showed higher performance for IRIS without ATCM compared to other approaches. The mean CTDIvol with and without ATCM was 20.04±3.33 and 36.37±4.65 mGy, respectively. In the phantom study, the noise with IRIS remained lower than that with FBP even with a 42% dose reduction.

Conclusion

IRIS algorithm can preserve the image quality when radiation dose is significantly reduced by ATCM in brain CT scan. Implementation of IRIS combined with ATCM is recommended for brain CT examinations.

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.

Low-dose versus conventional CT urography using dual-source CT with different time-current product values and the same tube voltage: image quality and diagnostic performance in various diagnoses

OBJECTIVES

To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT.

METHODS

This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated.

RESULTS

Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P < .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P < .001). Patient's body weight was positively correlated with noise and effective radiation dose (P < .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols.

CONCLUSIONS

Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract.

ADVANCES IN KNOWLEDGE

Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases.

Optimization of chest CT protocols based on pixel image matrix, kernels and iterative reconstruction levels - A phantom study

INTRODUCTION

This study investigated the impact of high matrix image reconstruction in combination with different reconstruction kernels and levels of iterative reconstructions on image quality in chest CT.

METHODS

An anthropomorphic chest phantom (Kyoto Kagaku Co., Ltd., Kyoto, Japan), and a Catphan® 600 (The Phantom Laboratory, Greenwich, NY, USA) phantom were scanned using a dual source scanner. Standard institutional protocol with 512 × 512 matrix was used as a reference. Reconstructions were performed for 768 × 768 and 1024 × 1024 matrices and all possible combinations of three different kernels and five levels of iterative reconstructions were included. Signal difference to noise ratio (SdNR) and line pairs per cm (lp/cm) were manually measured. A Linear regression model was applied for objective image analysis (SdNR) and inter-and intra-reader agreement was given as Cohen's kappa for the visual image assessment.

RESULTS

Matrix size did not have a significant impact on SdNR (p = 0.595). Kernel (p = 0.014) and ADMIRE level (p = 0.001) had a statistically significant impact on SdNR. The spatial resolution ranged from 7 lp/cm to 9 lp/cm. The highest spatial resolution was achieved using kernel Br64 and ADMIRE 1, 2 and 3 in both 768- and 1024-matrices, and with Br59 with ADMIRE 2 and 4 and 768-matrix, all visualizing 9 lp/cm. Both readers scored kernel Br59 highest, and the scoring increased with increasing levels of Iterative Reconstruction.

CONCLUSION

Matrix size did not influence image quality, however, the choice of kernel and degree of IR had an impact on objective and visual image quality in 768 - and 1024-matrices, suggesting that increased degree of IR may improve diagnostic image quality in chest CT.

IMPLICATIONS FOR PRACTICE

Image quality in CT of the lung may be improved by increasing the level of IR.

Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography

Objectives

Images reconstructed with higher strengths of iterative reconstruction algorithms may impair radiologists' subjective perception and diagnostic performance due to changes in the amplitude of different spatial frequencies of noise. The aim of the present study was to ascertain if radiologists can learn to adapt to the unusual appearance of images produced by higher strengths of Advanced modeled iterative reconstruction algorithm (ADMIRE).

Methods

Two previously published studies evaluated the performance of ADMIRE in non-contrast and contrast-enhanced abdominal CT. Images from 25 (first material) and 50 (second material) patients, were reconstructed with ADMIRE strengths 3, 5 (AD3, AD5) and filtered back projection (FBP). Radiologists assessed the images using image criteria from the European guidelines for quality criteria in CT. To ascertain if there was a learning effect, new analyses of data from the two studies was performed by introducing a time variable in the mixed-effects ordinal logistic regression model.

Results

In both materials, a significant negative attitude to ADMIRE 5 at the beginning of the viewing was strengthened during the progress of the reviews for both liver parenchyma (first material: -0.70, p < 0.01, second material: -0.96, p < 0.001) and overall image quality (first material:-0.59, p < 0.05, second material::-1.26, p < 0.001). For ADMIRE 3, an early positive attitude for the algorithm was noted, with no significant change over time for all criteria except one (overall image quality), where a significant negative trend over time (-1.08, p < 0.001) was seen in the second material.

Conclusions

With progression of reviews in both materials, an increasing dislike for ADMIRE 5 images was apparent for two image criteria. In this time perspective (weeks or months), no learning effect towards accepting the algorithm could be demonstrated.

Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort

RATIONAL AND OBJECTIVES

Comparison of radiation dose and image quality in routine abdominal and pelvic contrast-enhanced computed tomography (CECT) between a photon-counting detector CT (PCD-CT) and a dual energy dual source CT (DSCT).

MATERIALS AND METHODS

70 oncologic patients (mean age 66 ± 12 years, 29 females) were prospectively enrolled between November 2021 and February 2022. Abdominal CECT were clinically indicated and performed first on a 2nd-generation DSCT and at follow-up on a 1st-generation dual-source PCD-CT. The same contrast media (Imeron 350, Bracco imaging) and pump protocol was used for both scans. For both scanners, polychromatic images were reconstructed with 3mm slice thickness and comparable kernel (I30f[DSCT] and Br40f[PCD-CT]); for PCD-CT data from all counted events above the lowest energy threshold at 20 keV ("T3D") were used. Results were compared in terms of radiation dose metrics of CT dose index (CTDIvol), dose length product (DLP) and size-specific dose estimation (SSDE), objective and subjective measurements of image quality were scored by two emergency radiologists including lesion conspicuity.

RESULTS

Median time interval between the scans was 4 months (IQR: 3-6). CNRvessel and SNRvessel of T3D reconstructions from PCD-CT were significantly higher than those of DSCT (all, p < 0.05). Qualitative image noise analysis from PCD-CT and DSCT yielded a mean of 4 each. Lesion conspicuity was rated significantly higher in PCD-CT (Q3 strength) compared to DSCT images. CTDI, DLP and SSDE mean values for PCD-CT and DSCT were 7.98 ± 2.56 mGy vs. 14.11 ± 2.92 mGy, 393.13 ± 153.55 mGy*cm vs. 693.61 ± 185.76 mGy*cm and 9.98 ± 2.41 vs. 14.63 ± 1.63, respectively, translating to a dose reduction of around 32% (SSDE).

CONCLUSION

PCD-CT enables oncologic abdominal CT with a significantly reduced dose while keeping image quality similar to 2nd-generation DSCT.

"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose4: a phantom study at different dose levels"

PURPOSE

To characterize the performance of the Precise Image (PI) deep learning reconstruction (DLR) algorithm for abdominal Computed Tomography (CT) imaging.

METHODS

CT images of the Catphan-600 phantom (equipped with an external annulus) were acquired using an abdominal protocol at four dose levels and reconstructed using FBP, iDose⁴ (levels 2,5) and PI ('Soft Tissue' definition, levels 'Sharper','Sharp','Standard','Smooth','Smoother'). Image noise, image non-uniformity, noise power spectrum (NPS), target transfer function (TTF), detectability index (d'), CT numbers accuracy and image histograms were analyzed.

RESULTS

The behavior of the PI algorithm depended strongly on the selected level of reconstruction. The phantom analysis suggested that the PI image noise decreased linearly by varying the level of reconstruction from Sharper to Smoother, expressing a noise reduction up to 80% with respect to FBP. Additionally, the non-uniformity decreased, the histograms became narrower, and d' values increased as PI reconstruction levels changed from Sharper to Smoother. PI had no significant impact on the average CT number of different contrast objects. The conventional FBP NPS was deeply altered only by Smooth and Smoother levels of reconstruction. Furthermore, spatial resolution was found to be dose- and contrast-dependent, but in each analyzed condition it was greater than or comparable to FBP and iDose⁴ TTFs.

CONCLUSIONS

The PI algorithm can reduce image noise with respect to FBP and iDose⁴; spatial resolution, CT numbers and image uniformity are generally preserved by the algorithm but changes in NPS for the Smooth and Smoother levels need to be considered in protocols implementation.

Automated patient centering of computed tomography images and its implementation to evaluate clinical practices in three hospitals in Indonesia

Purpose: This study aims to develop a software tool for investigating patient centering profiles of axial CT images and to implement it to evaluate practices in three hospitals in Indonesia.

Methods: The evaluation of patient centering accuracy was conducted by comparing the center coordinate of the patient’s image to the center coordinates of the axial CT image. This process was iterated for all slices to yield an average patient mis-centering in both the x- and y-axis. We implemented the software to evaluate the profile of centering on 268 patient images from the head, thorax, and abdomen examinations taken from three hospitals.

Results: We found that 82% of patients were mis-centered in the y-axis (i.e., placed more than 5 mm from the iso-center), with 49% of patients placed 10–35 mm from the iso-center. Most of the patients had a tendency to be placed below the iso-centers. In head examinations, patients were more precisely positioned than in the other examinations. We did not find any significant difference in mis-centering between males and females. We found that there was a slight difference between mis-centering in adult and pediatric patients.

Conclusion: Software for automated patient centering was successfully developed. Patients in three hospitals in Indonesia had a tendency to be placed under the iso-center of the gantry.

Application of Different Levels of Advanced Modeling Iterative Reconstruction in Brain CT Scanning

BACKGROUND

Advanced Modeling Iterative Reconstruction (ADMIRE) algorithm has five intensity levels; it is important to study which algorithm is better for brain CT scanning.

OBJECTIVE

The aim of the study is to compare the influence of different strength levels of ADMIRE and traditional Filtered Back Projection (FBP) on image quality in brain CT scanning.

METHODS

60 patients were retrospectively selected, and the data from each of these patients' brains were reconstructed by four different reconstruction methods (FBP, ADMIRE1, ADMIRE3, and ADMIRE5). A five-point Likert Scale was implemented to evaluate the subjective image quality. Image noise, CT value of brain tissue , signal-to-noise ratio (SNR) of gray white matter, contrast-to-noise ratio (CNR), and beam hardening artifact index (AI) of the posterior fossa, were measured for evaluating the objective image quality. Finally, the differences between the subjective and objective evaluations were compared.

RESULTS

There were no statistical differences observed in CT values of gray matter and white matter between the four groups (all P >0.05). The image noise gradually decreased with the increase of ADMIRE algorithm level. The AI exhibited no statistical difference between the four groups (F =0.793, P =0.499), but it tended to decrease slightly with the increase of ADMIRE algorithm level. Compared to other groups (all p <0.001), the ADMIRE5 group demonstrated the best objective image quality. Nevertheless, the highest subjective score was observed in the ADMIRE3 group, which exhibited significant differences with other images (all P <0.001).

CONCLUSION

ADMIRE algorithm can clearly improve image quality, but it cannot significantly improve the linear sclerosis artifacts in the posterior cranial fossa. Based on the subjective evaluation of image quality, ADMIRE3 algorithm is recommended in brain CT scanning.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study

Objective. While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach. The upper abdomen of a live 4 year old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of variation was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results. A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion. DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features.

GaN Heterostructures as Innovative X-ray Imaging Sensors—Change of Paradigm

Direct conversion of X-ray irradiation using a semiconductor material is an emerging technology in medical and material sciences. Existing technologies face problems, such as sensitivity or resilience. Here, we describe a novel class of X-ray sensors based on GaN thin film and GaN/AlGaN high-electron-mobility transistors (HEMTs), a promising enabling technology in the modern world of GaN devices for high power, high temperature, high frequency, optoelectronic, and military/space applications. The GaN/AlGaN HEMT-based X-ray sensors offer superior performance, as evidenced by higher sensitivity due to intensification of electrons in the two-dimensional electron gas (2DEG), by ionizing radiation. This increase in detector sensitivity, by a factor of 104 compared to GaN thin film, now offers the opportunity to reduce health risks associated with the steady increase in CT scans in today’s medicine, and the associated increase in exposure to harmful ionizing radiation, by introducing GaN/AlGaN sensors into X-ray imaging devices, for the benefit of the patient.