Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs

Advances in Concept and Imaging of Interstitial Lung Disease

Although idiopathic pulmonary fibrosis (IPF) is a type of idiopathic interstitial pneumonia (IIP), it is different from other IIPs. IPF also differs from interstitial lung disease (ILD) with known causes, including connective tissue disease, exposure, cysts and/or airspace filling disease, and sarcoidosis. More than 90% of IPFs demonstrate progressive disease. Non-IPF ILD has been classified as progressive pulmonary fibrosis on the basis of disease behavior (progressive disease that gets worse over time) as opposed to classification based on cause and/or morphologic characteristics. Progressive fibrosis predictors in ILD include demographic characteristics, underlying connective tissue disease, more extensive disease at CT, honeycombing and usual interstitial pneumonia (UIP) pattern at CT, and greater impairment of lung function. Hypersensitivity pneumonitis (HP), a type of ILD, is separated into fibrotic and nonfibrotic types. Extensive peribronchiolar metaplasia supports the diagnosis of fibrotic HP over UIP, as does predominantly peribronchiolar disease with relative subpleural sparing at CT. Interstitial lung abnormality (ILA) is incidentally identified at CT; thus, ILA is under radiologist purview. Subpleural fibrotic ILA is a prognostic imaging biomarker, predictive of worse prognosis. Photon-counting CT can provide high spatial resolutions of up to 125 μm (in-plane) and 200 μm (through-plane) for improved evaluation of abnormalities.

Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction

OBJECTIVES

To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms.

METHODS

We retrospectively analyzed abdominal CT scans performed using a low-dose protocol. Three different image reconstruction algorithms-HIR, cDLR, and SR-DLR-were applied to the same raw image data. Objective evaluations included noise magnitude and contrast-to-noise ratio (CNR), as well as noise power spectrum (NPS) and edge rise slope (ERS). Subjective evaluations were performed by radiologists, who assessed image quality in terms of noise, artifacts, sharpness, and overall diagnostic utility.

RESULTS

Raw CT image data were obtained from 35 patients (mean CTDIvol 11.0 mGy; mean DLP 344.8 mGy/cm). cDLR yielded the lowest noise levels and highest CNR (p < 0.001). However, SR-DLR outperformed cDLR in terms of noise texture and resolution, achieving the lowest NPS peak and highest ERS (p < 0.001 and p = 0.005, respectively). Subjectively, SR-DLR was rated highest across all categories, including noise, artifacts, sharpness, and overall image quality, with statistically significant differences compared to cDLR and HIR (p < 0.001).

CONCLUSION

SR-DLR was the most effective reconstruction algorithm for low-dose abdominal CT imaging, offering superior image quality and noise reduction compared to cDLR and HIR. This suggests that SR-DLR can enhance the reliability and diagnostic accuracy of abdominal imaging, particularly in low-dose settings, making it a valuable tool in clinical practice.

Photon-Counting Detector CT Radiological-Histological Correlation in Cadaveric Human Lung Nodules and Airways

OBJECTIVES

The aim of this study was to compare the performances of photon-counting detector computed tomography (PCD-CT) and energy-integrating detector computed tomography (EID-CT) for visualizing nodules and airways in human cadaveric lungs.

MATERIALS AND METHODS

Previously obtained 20 cadaveric lungs were scanned, and images were prospectively acquired by EID-CT and PCD-CT at a radiation dose with a noise level equivalent to the diagnostic reference level. PCD-CT was scanned with ultra-high-resolution mode. The EID-CT images were reconstructed with a 512 matrix, 0.6-mm thickness, and a 350-mm field of view (FOV). The PCD-CT images were reconstructed at 3 settings: PCD-512: same as EID-CT; PCD-1024-FOV350: 1024 matrix, 0.2-mm thickness, 350-mm FOV; and PCD-1024-FOV50: 1024 matrix, 0.2-mm thickness, 50-mm FOV. Two specimens per lung were examined after hematoxylin and eosin staining. The CT images were evaluated for nodules on a 5-point scale and for airways on a 4-point scale to compare the histology. The Wilcoxon signed rank test with Bonferroni correction was performed for statistical analyses.

RESULTS

Sixty-seven nodules (1321 μm; interquartile range [IQR], 758-3105 μm) and 92 airways (851 μm; IQR, 514-1337 μm) were evaluated. For nodules and airways, scores decreased in order of PCD-1024-FOV50, PCD-1024-FOV350, PCD-512, and EID-CT. Significant differences were observed between series other than PCD-1024-FOV350 versus PCD-1024-FOV50 for nodules (PCD-1024-FOV350 vs PCD-1024-FOV50, P = 0.063; others P < 0.001) and between series other than EID-CT versus PCD-512 for airways (EID-CT vs PCD-512, P = 0.549; others P < 0.005). On PCD-1024-FOV50, the median size of barely detectable nodules was 604 μm (IQR, 469-756 μm) and that of barely detectable airways was 601 μm (IQR, 489-929 μm). On EID-CT, that of barely detectable nodules was 837 μm (IQR, 678-914 μm) and that of barely detectable airways was 1210 μm (IQR, 674-1435 μm).

CONCLUSIONS

PCD-CT visualized small nodules and airways better than EID-CT and improved with high spatial resolution and potentially can detect submillimeter nodules and airways.

High-resolution 0.25 mm Detector CT Has Limited Impact on Right Adrenal Vein Detectability in Preprocedural Contrast Enhanced CT for Adrenal Venous Sampling

OBJECTIVE

This study aims to identify factors associated with the detectability of the right adrenal vein (RAV) on preoperative contrast-enhanced CT scans of adrenal venous sampling (AVS) in the era of high-resolution CT (HRCT).

MATERIALS AND METHODS

In this retrospective study, 36 patients (15 men and 21 women; mean age, 56 y) who underwent preoperative contrast-enhanced CT [11 patients in HRCT with 0.25 mm detector matrix (Cannon Medical Systems) and 25 patients in conventional multidetector CT with 0.5 mm matrix] were included. A contrast agent dose of 600 mgI/kg was injected, and CT images were acquired at a fixed scan delay of 50 and 80 seconds. Adrenal venography and venous sampling were performed for the diagnosis of suspected primary hyperaldosteronism. The qualitative detectability of RAV on preoperative CT was assessed with adrenal venography as a reference. Clinical and imaging factors associated with a good detectability of RAV were analyzed via regression analysis. Optimal acquisition timing was assessed by analyzing the time-intensity curve and contrast enhancement pattern of the inferior vena cava using CT data from a separate cohort (n=5).

RESULTS

The qualitative detectability of RAV was deemed good in 15 patients and poor in 21 patients. Regression analysis revealed that only heterogeneous enhancement of inferior vena cava with bolus high attenuation, corresponding to an optimal acquisition timing from time-intensity curve analysis, was associated with a good detectability of RAV (odds ratio, 5.06). The use of HRCT was not statistically significant.

CONCLUSIONS

Optimal acquisition timing is a crucial factor for the detectability of RAV in preprocedural CT for AVS, while high-resolution 0.25 detector CT appears to have limited significance.

Recent trends in scientific research in chest radiology: What to do or not to do? That is the critical question in research

Hereby inviting young rising stars in chest radiology in Japan for contributing what they are working currently, we would like to show the potentials and directions of the near future research trends in the research field. I will provide a reflection on my own research topics. At the end, we also would like to discuss on how to choose the themes and topics of research: What to do or not to do? We strongly believe it will stimulate and help investigators in the field.

Unveiling the potential of ultrasound in brain imaging: Innovations, challenges, and prospects

Within medical imaging, ultrasound serves as a crucial tool, particularly in the realms of brain imaging and disease diagnosis. It offers superior safety, speed, and wider applicability compared to Magnetic Resonance Imaging (MRI) and X-ray Computed Tomography (CT). Nonetheless, conventional transcranial ultrasound applications in adult brain imaging face challenges stemming from the significant acoustic impedance contrast between the skull bone and soft tissues. Recent strides in ultrasound technology encompass a spectrum of advancements spanning tissue structural imaging, blood flow imaging, functional imaging, and image enhancement techniques. Structural imaging methods include traditional transcranial ultrasound techniques and ultrasound elastography. Transcranial ultrasound assesses the structure and function of the skull and brain, while ultrasound elastography evaluates the elasticity of brain tissue. Blood flow imaging includes traditional transcranial Doppler (TCD), ultrafast Doppler (UfD), contrast-enhanced ultrasound (CEUS), and ultrasound localization microscopy (ULM), which can be used to evaluate the velocity, direction, and perfusion of cerebral blood flow. Functional ultrasound imaging (fUS) detects changes in cerebral blood flow to create images of brain activity. Image enhancement techniques include full waveform inversion (FWI) and phase aberration correction techniques, focusing on more accurate localization and analysis of brain structures, achieving more precise and reliable brain imaging results. These methods have been extensively studied in clinical animal models, neonates, and adults, showing significant potential in brain tissue structural imaging, cerebral hemodynamics monitoring, and brain disease diagnosis. They represent current hotspots and focal points of ultrasound medical research. This review provides a comprehensive summary of recent developments in brain imaging technologies and methods, discussing their advantages, limitations, and future trends, offering insights into their prospects.

Cervical CT Angiography: The Advantage of Ultra-High-Resolution CT Versus Conventional HRCT

BACKGROUND/OBJECTIVES

Pre-treatment depiction of the cervical arteries is important for better intra-arterial infusion therapy of malignant head and neck tumors. There have not been any studies on the image quality of ultra-high-resolution computed tomography (U-HRCT) for cervical CT angiography (CTA). The aim of this study is to evaluate the advantages of U-HRCT over conventional HRCT for cervical CTA; Methods: Forty-one patients underwent cervical CTA prior to selective intra-arterial infusion chemotherapy for malignant head and neck tumors. Twenty-two patients were scanned on conventional HRCT, while the remaining nineteen on U-HRCT. U-HRCT super-high-resolution (SHR) mode was used in 8 patients, while high-resolution (HR) mode was used in 11 patients. On CTA, the visibility of 18 branches from bilateral external carotid arteries was evaluated using a 5-point scale by three radiologists in consensus. Prior to the patient study, a head-neck CT phantom study regarding mock arterial density and its visibility was performed; Results: Regarding the patient study, the mean score of the SHR mode for visibility was significantly higher than that of conventional HRCT in 17 of 18 arteries (p < 0.05). The mean score of the HR mode for visibility was significantly higher than that of conventional HRCT in all arteries (p < 0.05). Regarding the phantom study, the maximum density of the SHR mode was significantly higher than that of conventional HRCT for mock proximal and peripheral arteries (p < 0.01). In addition, the visual score of the SHR mode for mock arteries was significantly higher than that of conventional HRCT (p < 0.05); Conclusions: U-HRCT provides higher image quality in terms of visualization of the arteries than conventional HRCT in cervical CTA.

Photon-Counting Computed Tomography Angiography of Carotid Arteries: A Topical Narrative Review with Case Examples

Photon counting computed tomography (PCCT) represents a paradigm shift from conventional CT imaging, propelled by a new generation of X-ray detectors capable of counting individual photons and measuring their energy. The first part of this narrative review is focused on the technical aspects of PCCT and describes its key advancements and benefits compared to conventional CT but also its limitations. By synthesizing the existing literature, the second part of the review seeks to elucidate the potential of PCCT as a valuable tool for assessing carotid artery disease. Thanks to the enhanced spatial resolution and image quality, PCCT allows for an accurate evaluation of carotid luminal stenosis. With its ability to finely discriminate between different tissue types, PCCT allows for detailed characterization of plaque morphology and composition, which is crucial for assessing plaque vulnerability and the risk of cerebrovascular events.

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.

Technical principles, benefits, challenges, and applications of photon counting computed tomography in coronary imaging: a narrative review

Background and Objective

The introduction of photon-counting computed tomography (PCCT) represents the most recent groundbreaking advancement in clinical computed tomography (CT). PCCT has the potential to overcome the limitations of traditional CT and to provide new quantitative imaging information. This narrative review aims to summarize the technical principles, benefits, and challenges of PCCT and to provide a concise yet comprehensive summary of the applications of PCCT in the domain of coronary imaging.

Methods

A review of PubMed, Scopus, and Google Scholar was performed until October 2023 by using relevant keywords. Articles in English were considered.

Key Content and Findings

The main advantages of PCCT over traditional CT are enhanced spatial resolution, improved signal and contrast characteristics, diminished electronic noise and image artifacts, lower radiation exposure, and multi-energy capability with enhanced material discrimination. These key characteristics have made room for improved assessment of plaque volume and severity of stenosis, more precise assessment of coronary artery calcifications, also preserved in the case of a reduced radiation dose, improved assessment of plaque composition, possibility to provide details regarding the biological processes occurring within the plaque, enhanced quality and accuracy of coronary stent imaging, and improved radiomic analyses.

Conclusions

PCCT can significantly impact diagnostic and clinical pathways and improve the management of patients with coronary artery diseases (CADs).

Super-resolution of clinical CT: Revealing microarchitecture in whole bone clinical CT image data

Osteoporotic fractures, prevalent in the elderly, pose a significant health and economic burden. Current methods for predicting fracture risk, primarily relying on bone mineral density, provide only modest accuracy. If better spatial resolution of trabecular bone in a clinical scan were available, a more complete assessment of fracture risk would be obtained using microarchitectural measures of bone (i.e. trabecular thickness, trabecular spacing, bone volume fraction, etc.). However, increased resolution comes at the cost of increased radiation or can only be applied at small volumes of distal skeletal locations. This study explores super-resolution (SR) technology to enhance clinical CT scans of proximal femurs and better reveal the trabecular microarchitecture of bone. Using a deep-learning-based (i.e. subset of artificial intelligence) SR approach, low-resolution clinical CT images were upscaled to higher resolution and compared to corresponding MicroCT-derived images. SR-derived 2-dimensional microarchitectural measurements, such as degree of anisotropy, bone volume fraction, trabecular spacing, and trabecular thickness were within 16 % error compared to MicroCT data, whereas connectivity density exhibited larger error (as high as 1094 %). SR-derived 3-dimensional microarchitectural metrics exhibited errors <18 %. This work showcases the potential of SR technology to enhance clinical bone imaging and holds promise for improving fracture risk assessments and osteoporosis detection. Further research, including larger datasets and refined techniques, can advance SR's clinical utility, enabling comprehensive microstructural assessment across whole bones, thereby improving fracture risk predictions and patient-specific treatment strategies.

Deep Learning With Physics-Embedded Neural Network for Full Waveform Ultrasonic Brain Imaging

The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the successful application of traditional ultrasound for brain imaging. In this study, we propose a physics-embedded neural network with deep learning based full waveform inversion (PEN-FWI), which can achieve reliable quantitative imaging of brain tissues. The network consists of two fundamental components: forward convolutional neural network (FCNN) and inversion sub-neural network (ISNN). The FCNN explores the nonlinear mapping relationship between the brain model and the wavefield, replacing the tedious wavefield calculation process based on the finite difference method. The ISNN implements the mapping from the wavefield to the model. PEN-FWI includes three iterative steps, each embedding the F CNN into the ISNN, ultimately achieving tomography from wavefield to brain models. Simulation and laboratory tests indicate that PEN-FWI can produce high-quality imaging of the skull and soft tissues, even starting from a homogeneous water model. PEN-FWI can achieve excellent imaging of clot models with constant uniform distribution of velocity, randomly Gaussian distribution of velocity, and irregularly shaped randomly distributed velocity. Robust differentiation can also be achieved for brain slices of various tissues and skulls, resulting in high-quality imaging. The imaging time for a horizontal cross-sectional imag e of the brain is only 1.13 seconds. This algorithm can effectively promote ultrasound-based brain tomography and provide feasible solutions in other fields.

Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix

PURPOSE

To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT).

MATERIALS AND METHODS

For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant.

RESULTS

Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists.

CONCLUSION

Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.

In vivo depiction of cortical bone vascularization with ultra-high resolution-CT and deep learning algorithm reconstruction using osteoid osteoma as a model

PURPOSE

The purpose of this study was to evaluate the ability to depict in vivo bone vascularization using ultra-high-resolution (UHR) computed tomography (CT) with deep learning reconstruction (DLR) and hybrid iterative reconstruction algorithm, compared to simulated conventional CT, using osteoid osteoma as a model.

MATERIALS AND METHODS

Patients with histopathologically proven cortical osteoid osteoma who underwent UHR-CT between October 2019 and October 2022 were retrospectively included. Images were acquired with a 1024 × 1024 matrix and reconstructed with DLR and hybrid iterative reconstruction algorithm. To simulate conventional CT, images with a 512 × 512 matrix were also reconstructed. Two radiologists (R1, R2) independently evaluated the number of blood vessels entering the nidus and crossing the bone cortex, as well as vessel identification and image quality with a 5-point scale. Standard deviation (SD) of attenuation in the adjacent muscle and that of air were used as image noise and recorded.

RESULTS

Thirteen patients with 13 osteoid osteomas were included. There were 11 men and two women with a mean age of 21.8 ± 9.1 (SD) years. For both readers, UHR-CT with DLR depicted more nidus vessels (11.5 ± 4.3 [SD] (R1) and 11.9 ± 4.6 [SD] (R2)) and cortical vessels (4 ± 3.8 [SD] and 4.3 ± 4.1 [SD], respectively) than UHR-CT with hybrid iterative reconstruction (10.5 ± 4.3 [SD] and 10.4 ± 4.6 [SD], and 4.1 ± 3.8 [SD] and 4.3 ± 3.8 [SD], respectively) and simulated conventional CT (5.3 ± 2.2 [SD] and 6.4 ± 2.5 [SD], 2 ± 1.2 [SD] and 2.4 ± 1.6 [SD], respectively) (P < 0.05). UHR-CT with DLR provided less image noise than simulated conventional CT and UHR-CT with hybrid iterative reconstruction (P < 0.05). UHR-CT with DLR received the greatest score and simulated conventional CT the lowest score for vessel identification and image quality.

CONCLUSION

UHR-CT with DLR shows less noise than UHR-CT with hybrid iterative reconstruction and significantly improves cortical bone vascularization depiction compared to simulated conventional CT.

Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images

PURPOSE

Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases.

MATERIALS AND METHODS

Forty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume : mean ± standard deviation, 9.0 ± 1.8 mGy), RDCT (CTDIvol: 1.7 ± 0.2 mGy) and ULDCT (CTDIvol: 0.8 ± 0.1 mGy) at a HDCT. All CT data set were then reconstructed with 512 × 512 or 1024 × 1024 matrixes by means of hybrid-type IR and DLR. SNR of lung parenchyma and probabilities of all lung textures were assessed for each CT data set. SNR and detection performance of each lung texture reconstructed with DLR and hybrid-type IR were then compared by means of paired t tests and ROC analyses for all CT data at each section thickness.

RESULTS

Data for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05).

CONCLUSION

DLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

Cardiovascular Applications of Photon-Counting CT Technology: A Revolutionary New Diagnostic Step

Photon-counting computed tomography (PCCT) is an emerging technology that can potentially transform clinical CT imaging. After a brief description of the PCCT technology, this review summarizes its main advantages over conventional CT: improved spatial resolution, improved signal and contrast behavior, reduced electronic noise and artifacts, decreased radiation dose, and multi-energy capability with improved material discrimination. Moreover, by providing an overview of the existing literature, this review highlights how the PCCT benefits have been harnessed to enhance and broaden the diagnostic capabilities of CT for cardiovascular applications, including the detection of coronary artery calcifications, evaluation of coronary plaque extent and composition, evaluation of coronary stents, and assessment of myocardial tissue characteristics and perfusion.

Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography

Purpose

To investigate image noise and edge sharpness of coronary CT angiography (CCTA) with super-resolution deep learning reconstruction (SR-DLR) compared with conventional DLR (C-DLR) and to evaluate agreement in stenosis grading using CCTA with that from invasive coronary angiography (ICA) as the reference standard.

Materials and Methods

This retrospective study included 58 patients (mean age, 69.0 years ± 12.8 [SD]; 38 men, 20 women) who underwent CCTA using 320-row CT between April and September 2022. All images were reconstructed with two different algorithms: SR-DLR and C-DLR. Image noise, signal-to-noise ratio, edge sharpness, full width at half maximum (FWHM) of stent, and agreement in stenosis grading with that from ICA were compared. Stenosis was visually graded from 0 to 5, with 5 indicating occlusion.

Results

SR-DLR significantly decreased image noise by 31% compared with C-DLR (12.6 HU ± 2.3 vs 18.2 HU ± 1.9; P < .001). Signal-to-noise ratio and edge sharpness were significantly improved by SR-DLR compared with C-DLR (signal-to-noise ratio, 38.7 ± 8.3 vs 26.2 ± 4.6; P < .001; edge sharpness, 560 HU/mm ± 191 vs 463 HU/mm ± 164; P < .001). The FWHM of stent was significantly thinner on SR-DLR (0.72 mm ± 0.22) than on C-DLR (1.01 mm ± 0.21; P < .001). Agreement in stenosis grading between CCTA and ICA was improved on SR-DLR compared with C-DLR (weighted κ = 0.83 vs 0.77).

Conclusion

SR-DLR improved vessel sharpness, image noise, and accuracy of coronary stenosis grading compared with the C-DLR technique.Keywords: CT Angiography, Cardiac, Coronary Arteries Supplemental material is available for this article. © RSNA, 2023.

Ultra-high resolution CT imaging of interstitial lung disease: impact of photon-counting CT in 112 patients

OBJECTIVES

To compare lung parenchyma analysis on ultra-high resolution (UHR) images of a photon-counting CT (PCCT) scanner with that of high-resolution (HR) images of an energy-integrating detector CT (EID-CT).

METHODS

A total of 112 patients with stable interstitial lung disease (ILD) were investigated (a) at T0 with HRCT on a 3rd-generation dual-source CT scanner; (b) at T1 with UHR on a PCCT scanner; (c) with a comparison of 1-mm-thick lung images.

RESULTS

Despite a higher level of objective noise at T1 (74.1 ± 14.1 UH vs 38.1 ± 8.7 UH; p < 0.0001), higher qualitative scores were observed at T1 with (a) visualization of more distal bronchial divisions (median order; Q1-Q3) (T1: 10th division [9-10]; T0: 9th division [8-9]; p < 0.0001); (b) greater scores of sharpness of bronchial walls (p < 0.0001) and right major fissure (p < 0.0001). The scores of visualization of CT features of ILD were significantly superior at T1 (micronodules: p = 0.03; linear opacities, intralobular reticulation, bronchiectasis, bronchiolectasis, and honeycombing: p < 0.0001), leading to the reclassification of 4 patients with non-fibrotic ILD at T0, recognized with fibrotic ILD at T1. At T1, the mean (± SD) radiation dose (CTDI vol: 2.7 ± 0.5 mGy; DLP: 88.5 ± 21 mGy.cm) was significantly lower than that delivered at T0 (CTDI vol: 3.6 ± 0.9 mGy; DLP: 129.8 ± 31.7 mGy.cm) (p < 0.0001), corresponding to a mean reduction of 27% and 32% for the CTDIvol and DLP, respectively.

CONCLUSIONS

The UHR scanning mode of PCCT allowed a more precise depiction of CT features of ILDs and reclassification of ILD patterns with significant radiation dose reduction.

CLINICAL RELEVANCE STATEMENT

Evaluation of lung parenchymal structures with ultra-high-resolution makes subtle changes at the level of the secondary pulmonary lobules and lung microcirculation becoming visually accessible, opening new options for synergistic collaborations between highly-detailed morphology and artificial intelligence.

KEY POINTS

• Photon-counting CT (PCCT) provides a more precise analysis of lung parenchymal structures and CT features of interstitial lung diseases (ILDs). • The UHR mode ensures a more precise delineation of fine fibrotic abnormalities with the potential of modifying the categorization of ILD patterns. • Better image quality at a lower radiation dose with PCCT opens new horizons for further dose reduction in noncontrast UHR examinations.

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.

Evaluation of Trabecular Microstructure of Cancellous Bone Using Quarter-Detector Computed Tomography

Quarter-detector computed tomography (QDCT) is an ultra-high-spatial-resolution imaging technique. This study aimed to verify the validity of trabecular structure evaluation using a QDCT scanner in the diagnosis of osteoporosis. We used a cancellous bone specimen image of the second lumbar vertebrae of an adult male with moderate osteoporosis. To obtain QDCT images, we created a three-dimensional model from micro-CT images of the specimen. Statistical analysis was performed on the relationship between micro-CT and QDCT imaging modalities. The differences between micro-CT and QDCT were assessed based on their significance with respect to the calculated mean measurements using the Mann–Whitney test. Single regression analysis was performed using linear regression, with micro-CT and QDCT as the explanatory and objective variables, respectively, to determine the relationship of the measured values between the two modalities. By applying the necessary correction to the micro-CT measured values, it is possible to perform an analysis equivalent to micro-CT, which offers higher spatial resolution than QDCT. We found evidence that if QDCT can be used, trabecular structure evaluation may contribute to image diagnosis to evaluate practical bone fragility.

Effects of non-stationary blur on texture biomarkers of bone using Ultra-High Resolution CT

Purpose

To advance the development of radiomic models of bone quality using the recently introduced Ultra-High Resolution CT (UHR CT), we investigate inter-scan reproducibility of trabecular bone texture features to spatially-variant azimuthal and radial blurs associated with focal spot elongation and gantry rotation.

Methods

The UHR CT system features 250×250 μm detector pixels and an x-ray source with a 0.4×0.5 mm focal spot. Visualization of details down to ~150 μm has been reported for this device. A cadaveric femur was imaged on UHR CT at three radial locations within the field-of-view: 0 cm (isocenter), 9 cm from the isocenter, and 18 cm from the isocenter; we expect the non-stationary blurs to worsen with increasing radial displacement. Gray level cooccurrence (GLCM) and gray level run length (GLRLM) texture features were extracted from 237 trabecular regions of interest (ROIs, 5 cm diameter) placed at corresponding locations in the femoral head in scans obtained at the different shifts. We evaluated concordance correlation coefficient (CCC) between texture features at 0 cm (reference) and at 9 cm and 18 cm. We also investigated whether the spatially-variant blurs affect K-means clustering of trabecular bone ROIs based on their texture features.

Results

The average CCCs (against the 0 cm reference) for GLCM and GLRM features were ~0.7 at 9 cm. At 18 cm, the average CCCs were reduced to ~0.17 for GLCM and ~0.26 for GLRM. The non-stationary blurs are incorporated in radiomic features of cancellous bone, leading to inconsistencies in clustering of trabecular ROIs between different radial locations: an intersection-over-union overlap of corresponding (most similar) clusters between 0 cm and 9 cm shift was >70%, but dropped to <60% for the majority of corresponding clusters between 0 cm and 18 cm shift.

Conclusion

Non-stationary CT system blurs reduce inter-scan reproducibility of texture features of trabecular bone in UHR CT, especially for locations >15 cm from the isocenter. Radiomic models of bone quality derived from UHR CT measurements at isocenter might need to be revised before application in peripheral body sites such as the hips.

Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies

OBJECTIVES

To demonstrate the effect of an improved deep learning-based reconstruction (DLR) algorithm on Ultra-High-Resolution Computed Tomography (U-HRCT) scanners.

METHODS

Clinical and phantom studies were conducted. Thirty patients who underwent contrast-enhanced CT examination during the follow-up period were enrolled. Images were reconstructed using improved DLR [termed, New DLR, i.e., Advanced Intelligent Clear-IQ Engine (AiCE) Body Sharp] and conventional DLR (Conv DLR, AiCE Body) algorithms. Two radiologists assessed the overall image quality using a 5-point scale (5 = excellent; 1 = unacceptable). The noise power spectra (NPSs) were calculated to assess the frequency characteristics of the image noise, and the square root of area under the curve (√AUC NPS) between 0.05 and 0.50 cycle/mm was calculated as an indicator of the image noise. Dunnett's test was used for statistical analysis of the visual evaluation score, with statistical significance set at p < 0.05.

RESULTS

The overall image quality of New DLR was better than that of the Conv DLR (4.2 ± 0.4 and 3.3 ± 0.4, respectively; p < 0.0001). All New DLR images had an overall image quality score above the average or excellent. The √AUCNPS value of New DLR was lower than that of Conv DLR (13.8 and 14.2, respectively). The median values of reconstruction time required with New DLR and Conv DLR were 5.0 and 7.8 min, respectively.

CONCLUSIONS

The new DLR algorithm improved the image quality within a practical reconstruction time.

ADVANCES IN KNOWLEDGE

The new DLR enables us to choose whether to improve image quality or reduce the dose.

Effect of Ultra-High-Resolution CT on Pseudoenhancement in Renal Cysts: A Phantom Experiment and Clinical Study

BACKGROUND. Ultra-high-resolution CT (UHRCT) allows acquisition using a small detector element size, in turn allowing very high spatial resolutions. The high resolution may reduce partial-volume averaging and thereby renal cyst pseudoenhancement. OBJECTIVE. The purpose of this article was to assess the impact of UHRCT on renal cyst pseudoenhancement. METHODS. A phantom was constructed that contained 7-, 15-, and 25-mm simulated cysts within compartments simulating unenhanced and nephrographic phase renal parenchyma. The phantom underwent two UHRCT acquisitions using 0.25- and 0.5-mm detector elements, with reconstruction at varying matrices and slice thicknesses. A retrospective study was performed of 36 patients (24 men, 12 women; mean age, 75.7 ± 9.4 [SD] years) with 118 renal cysts who underwent renal-mass protocol CT using UHRCT and the 0.25-mm detector element, with reconstruction at varying matrices and slice thicknesses; detector element size could not be retrospectively adjusted. ROIs were placed to measure cysts' attenuation increase from unenhanced to nephrographic phases (to reflect pseudoenhancement) and SD of unenhanced phase attenuation (to reflect image noise). RESULTS. In the phantom, attenuation increase was lower for the 0.25- than 0.5-mm detector element for the 15-mm cyst (4.6 ± 2.7 HU vs 6.8 ± 2.9 HU, p = .03) and 25-mm cyst (2.3 ± 1.4 HU vs 3.8 ± 1.2 HU, p = .02), but not the 7-mm cyst (p = .72). Attenuation increase was not different between 512 × 512 and 1024 × 1024 matrices for any cyst size in the phantom or patients (p > .05). Attenuation increase was not associated with slice thickness for any cyst size in the phantom or in patients for cysts that were between 5 mm and less than 10 mm and those that were 10 mm and larger (p > .05). For cysts smaller than 5 mm in patients, attenuation increase showed decreases with thinner slices, though there was no significant difference between 0.5-mm and 0.25-mm (3-mm slice: 23.7 ± 22.5 HU; 2-mm slice: 20.2 ± 22.7 HU; 0.5-mm slice: 11.6 ± 17.5 HU; 0.25-mm slice: 12.6 ± 19.7 HU; p < .001). Smaller detector element size, increased matrix size, and thinner slices all increased image noise for cysts of all sizes in the phantom and patients (p < .05). CONCLUSION. UHRCT may reduce renal cyst pseudoenhancement through a smaller detector element size and, for cysts smaller than 5 mm, very thin slices; however, these adjustments result in increased noise. CLINICAL IMPACT. Although requiring further clinical evaluation, UHRCT may facilitate characterization of small cystic renal lesions, thereby reducing equivocal interpretations and follow-up recommendations.

Computer tomography and magnetic resonance for multimodal imaging of fossils and mummies

The study of fossils and mummies has largely benefited from the use of modern noninvasive and nondestructive imaging technologies and represents a fast developing area. In this review, we describe the emerging role of imaging based on Magnetic Resonance (MR) and Computer Tomography (CT) employed for the study of ancient remains and mummies. For each methodology, the state of the art in paleoradiology applications is described, by emphasizing new technologies developed in the field of both CT, such as micro- and nano-CT, dual-energy and multi-energy CT, and MR, with the description of novel dedicated sequences, radiofrequency coils and gradients. The complementarity of CT and MR in paleoradiology is also discussed, by pointing out what MR provides in addition to CT, with an overview on the state of the art of emerging strategies in the use of CT/MR combination for the study of a sample following a multimodal integrated approach.

Comparison of lung CT number and airway dimension evaluation capabilities of ultra-high-resolution CT, using different scan modes and reconstruction methods including deep learning reconstruction, with those of multi-detector CT in a QIBA phantom study

OBJECTIVE

Ultra-high-resolution CT (UHR-CT), which can be applied normal resolution (NR), high-resolution (HR), and super-high-resolution (SHR) modes, has become available as in conjunction with multi-detector CT (MDCT). Moreover, deep learning reconstruction (DLR) method, as well as filtered back projection (FBP), hybrid-type iterative reconstruction (IR), and model-based IR methods, has been clinically used. The purpose of this study was to directly compare lung CT number and airway dimension evaluation capabilities of UHR-CT using different scan modes with those of MDCT with different reconstruction methods as investigated in a lung density and airway phantom design recommended by QIBA.

MATERIALS AND METHODS

Lung CT number, inner diameter (ID), inner area (IA), and wall thickness (WT) were measured, and mean differences between measured CT number, ID, IA, WT, and standard reference were compared by means of Tukey's HSD test between all UHR-CT data and MDCT reconstructed with FBP as 1.0-mm section thickness.

RESULTS

For each reconstruction method, mean differences in lung CT numbers and all airway parameters on 0.5-mm and 1-mm section thickness CTs obtained with SHR and HR modes showed significant differences with those obtained with the NR mode on UHR-CT and MDCT (p < 0.05). Moreover, the mean differences on all UHR-CTs obtained with SHR, HR, or NR modes were significantly different from those of 1.0-mm section thickness MDCTs reconstructed with FBP (p < 0.05).

CONCLUSION

Scan modes and reconstruction methods used for UHR-CT were found to significantly affect lung CT number and airway dimension evaluations as did reconstruction methods used for MDCT.

KEY POINTS

• Scan and reconstruction methods used for UHR-CT showed significantly higher CT numbers and smaller airway dimension evaluations as did those for MDCT in a QIBA phantom study (p < 0.05). • Mean differences in lung CT number for 0.25-mm, 0.5-mm, and 1.0-mm section thickness CT images obtained with SHR and HR modes were significantly larger than those for CT images at 1.0-mm section thickness obtained with MDCT and reconstructed with FBP (p < 0.05). • Mean differences in inner diameter (ID), inner area (IA), and wall thickness (WT) measured with SHR and HR modes on 0.5- and 1.0-mm section thickness CT images were significantly smaller than those obtained with NR mode on UHR-CT and MDCT (p < 0.05).

Performance of Ultra-High-Resolution Computed Tomography in Super High-Resolution Mode at the Routine Radiation Dose: Phantom Study

OBJECTIVE

Using a chest phantom, we compared the image quality of ultra-high-resolution computed tomography (U-HRCT) images acquired in super high-resolution (SHR) and normal resolution (NR) mode and at the routine radiation dose. The detector size was 0.25 and 0.5 mm, respectively.

METHODS

A chest phantom was scanned on a U-HRCT scanner. The scan parameters were tube voltage 120 kV and volume CT dose index 13.0 mGy, the routine radiation dose for conventional scans. The rotation time was 0.5 s/rot, the number of matrices was 512 in NR and 1024 in SHR mode. For physical evaluation, the modulation transfer function was measured on the spherical simulated nodule, and the noise power spectrum on the cylindrical water phantom. A CT value profile curve was created using an in-house simulated bronchial phantom. For visual evaluation, 3 radiologists and 3 radiology technologists evaluated overall image quality using a 4-grade scale (grade 1, poor; and grade 4, excellent).

RESULTS

The 10% of modulation transfer function was 13.5 lp/cm in NR and 14.9 lp/cm in SHR mode (P<0.01). ƒpeak was 5.6 lp/cm in NR and 8.8 lp/cm in SHR mode (P<0.01), and the peak of noise power spectrum shifted. On the profile curves, the CT value at the edge changed in NR but not in SHR mode. The overall image quality was grade 3.0 ± 0.7 in SHR and grade 2.0 ± 0.7 in NR mode (P<0.01).

CONCLUSIONS

The image quality of SHR mode with U-HRCT was superior to that of NR mode at the routine radiation dose.

Influence of monitor display resolution and displayed image size on the spatial resolution of ultra-high-resolution CT images: a phantom study

To determine the optimal display conditions for ultra-high-resolution computed tomography (UHRCT) images in clinical practice, this study investigated the effects of liquid-crystal display (LCD) resolution and displayed image size on the spatial resolution of phantom images acquired using a UHRCT system. A phantom designed to evaluate the high-contrast resolution was scanned. The scan data were reconstructed into four types of UHRCT image series consisting of the following possible combinations: two types of reconstruction kernels on the filtered back-projection method (for the lung and mediastinum) and two types of matrix sizes (1024² and 2048²). These images were displayed under eight types of display conditions: three image sizes displayed on a 2-megapixel (MP) and 3-MP color LCD and two image sizes on an 8-MP color LCD. A total of 32 samples (four image series × eight display conditions) were evaluated by eight observers for high-contrast resolution. The high-contrast resolution of the displayed UHRCT images was significantly affected by the displayed image size, although the largest (full-screen) displayed image size did not necessarily show the maximum high-contrast resolution. When the images were displayed in the full-screen size, LCD resolution affected the high-contrast resolution of only the 2048²-matrix-size images reconstructed using the lung kernel. In conclusion, the spatial resolution of UHRCT images may be affected by LCD resolution and displayed image size. To optimize the clinical display conditions for UHRCT images, it is necessary to adopt an LCD with an adequate resolution for each viewing situation.

Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy

Simple Summary

Successful surgery in robot-assisted partial nephrectomy (RAPN), especially for highly complex tumors, relies on a detailed understanding of the anatomical relations of the tumor absolute and relative to the urinary tract and the vascular structures, including the renal pedicle. Intraoperative navigation with accurate information regarding tumor position relative to the surrounding urinary vascular structures undoubtedly assists the surgeon during RAPN. In this report, we performed RAPN with intraoperative navigation using a novel computed tomography scanner (UHR-CT) and compared its perioperative and short-term functional outcomes to those of area-detector CT (ADCT). We found that this novel navigation system using UHR-CT provided a shorter warm ischemia time and lower estimated blood loss than ADCT, and concluded this could be a useful tool for patients who undergo RAPN. This is the first report to evaluate the feasibility and usefulness of UHR-CT for intraoperative navigation during RAPN.

Abstract

To assess the perioperative and short-term functional outcomes of robot-assisted partial nephrectomy (RAPN) with intraoperative navigation using an ultra-high-resolution computed tomography (UHR-CT) scanner, we retrospectively analyzed 323 patients who underwent RAPN using an UHR-CT or area-detector CT (ADCT). Perioperative outcomes and the postoperative preservation ratio of estimated glomerular filtration rate (eGFR) were compared. After the propensity score matching, we evaluated 99 patients in each group. Although the median warm ischemia time (WIT) was less than 25 min in both groups, it was significantly shorter in the UHR-CT group than in the ADCT group (15 min vs. 17 min, p = 0.032). Moreover, the estimated blood loss (EBL) was significantly lower in the UHR-CT group than in the ADCT group (33 mL vs. 50 mL, p = 0.028). However, there were no significant intergroup differences in the postoperative preservation ratio of eGFR at 3 or 6 months of follow-up (ADCT 91.8% vs. UHR-CT 93.5%, p = 0.195; and ADCT 91.7% vs. UHR-CT 94.0%, p = 0.160, respectively). Although no differences in short-term renal function were observed in intraoperative navigation for RAPN in this propensity score–matched cohort, this study is the first to demonstrate that UHR-CT resulted in a shorter WIT and lower EBL than ADCT.

Efficacy of ultra-high-resolution computed tomographic angiography for postoperative evaluation of intracranial aneurysm after clipping surgery: A case report

Background:

Following clipping surgery for intracranial aneurysm, computed tomography angiography (CTA) is often used to confirm complete aneurysm obliteration. However, artifacts from the titanium clips usually degrade the images around them. The ultra-high-resolution computed tomography (UHR-CT) system recently became available in clinical practice. Here, we report a case in which CTA using the UHR-CT system successfully pointed out a small aneurysmal remnant after the clipping surgery, which was validated by digital subtraction angiography.

Case Description:

A patient underwent clipping surgery for an unruptured aneurysm using two titanium alloy clips. CTA using the UHR-CT system demonstrated a small remnant aneurysm. Digital subtraction angiography confirmed the minor remnant. The UHR-CTA images were comparable to three-dimensional reconstructed images from the rotational angiography.

Conclusion:

We propose that UHR-CTA is a reliable postoperative assessment method for intracranial clipping surgeries.

High-Resolution CT Imaging of the Temporal Bone: A Cadaveric Specimen Study

Objective  Super-high and ultra-high spatial resolution computed tomography (CT) imaging can be advantageous for detecting temporal bone pathology and guiding treatment strategies. Methods  Six temporal bone cadaveric specimens were used to evaluate the temporal bone microanatomic structures utilizing the following CT reconstruction modes: normal resolution (NR, 0.5-mm slice thickness, 512 ² matrix), high resolution (HR, 0.5-mm slice thickness, 1,024 ² matrix), super-high resolution (SHR, 0.25-mm slice thickness, 1,024 ² matrix), and ultra-high resolution (UHR, 0.25-mm slice thickness, 2,048 ² matrix). Noise and signal-to-noise ratio (SNR) for bone and air were measured at each reconstruction mode. Two observers assessed visualization of seven small anatomic structures using a 4-point scale at each reconstruction mode. Results  Noise was significantly higher and SNR significantly lower with increases in spatial resolution (NR, HR, and SHR). There was no statistical difference between SHR and UHR imaging with regard to noise and SNR. There was significantly improved visibility of all temporal bone osseous structures of interest with SHR and UHR imaging relative to NR imaging ( p  < 0.001) and most of the temporal bone osseous structures relative to HR imaging. There was no statistical difference in the subjective image quality between SHR and UHR imaging of the temporal bone ( p  ≥ 0.085). Conclusion  Super-high-resolution and ultra-high-resolution CT imaging results in significant improvement in image quality compared with normal-resolution and high-resolution CT imaging of the temporal bone. This preliminary study also demonstrates equivalency between super-high and ultra-high spatial resolution temporal bone CT imaging protocols for clinical use.

Quantitative measurements of emphysema in ultra-high resolution computed tomography using model-based iterative reconstruction in comparison to that using hybrid iterative reconstruction

The percentage of low attenuation volume ratio (LAVR), which is measured using computed tomography (CT), is an index of the severity of emphysema. For LAVR evaluation, ultra-high-resolution (U-HR) CT images are useful. To improve the image quality of U-HRCT, iterative reconstruction is used. There are two types of iterative reconstruction: hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR). In this study, we physically and clinically evaluated U-HR images reconstructed with HIR and MBIR, and demonstrated the usefulness of U-HR images with MBIR for quantitative measurements of emphysema. Both images were reconstructed with a slice thickness of 0.25 mm and an image matrix size of 1024 × 1024 pixels. For physical evaluation, the modulation transfer function (MTF) and noise power spectrum (NPS) of HIR and MBIR were compared. For clinical evaluation, LAVR calculated from HIR and MBIR were compared using the Wilcoxon matched-pairs signed-rank test. In addition, the correlation between LAVR and forced expiratory volume in one second (FEV₁%) was evaluated using the Spearman rank correlation test. The MTFs of HIR and MBIR were comparable. The NPS of MBIR was lower than that of HIR. The mean LAVR values calculated from HIR and MBIR were 19.5 ± 12.6% and 20.4 ± 11.7%, respectively (p = 0.84). The correlation coefficients between LAVR and FEV₁% that were taken from HIR and MBIR were 0.64 and 0.74, respectively (p < 0.01). MBIR is more useful than HIR for the quantitative measurements of emphysema with U-HR images.

Improved visualization of the chorda tympani nerve using ultra-high-resolution computed tomography

Background

Recognition of the anatomical course of the chorda tympani nerve (CTN) is important for preventing iatrogenic injuries during middle-ear surgery.

Purpose

This study aims to compare visualization of the CTN using two computed tomography (CT) methods: conventional high-resolution CT (C-HRCT) and ultra‐high-resolution CT (U-HRCT).

Materials and methods

We performed a retrospective visual assessment of 59 CTNs in normal temporal bones of 54 consecutive patients who underwent both C-HRCT and U-HRCT. After dividing CTN into three anatomical segments (posterior canaliculus, tympanic segment, and anterior canaliculus), two neuroradiologists scored the visualizations on a four-point scale.

Results

On C-HRCT, the visual scores of the posterior canaliculus, tympanic segment, and anterior canaliculus were 3.5 ± 0.7, 1.6 ± 0.6, and 3.1 ± 0.7, respectively. The respective values were significantly higher in all segments on U-HRCT: 3.9 ± 0.2, 2.4 ± 0.6, 3.5 ± 0.6 (p < 0.01). Although the difference in scores between methods was greatest for the tympanic segment, the visual score on U-HRCT was lower for the tympanic segment than for the anterior and posterior segments (p < 0.01).

Conclusion

Ultra‐high-resolution CT provides superior visualization of the CTN, especially the tympanic segment.

Comparison between ultra-high-resolution computed tomographic angiography and conventional computed tomographic angiography in the visualization of the subcallosal artery

Background:

The subcallosal artery (ScA) is a single dominant artery arising from the anterior communicating artery. Its injury causes amnesia and cognitive disturbance. The conventional computed tomographic angiography (C-CTA) is a common evaluation method of the intracranial artery. However, to image tinny perforating arteries such as the ScA is technically demanding for C-CTA. The purpose of this study is to investigate whether the ultra-high-resolution CTA (UHR-CTA) could image the ScA better than C-CTA. UHR-CTA became available in clinical practice in 2017. Its novel features are the improvement of the detector system and a small X-ray focus.

Methods:

Between April 2019 and May 2020, 77 and 49 patients who underwent intracranial UHR-CTA and C-CTA, respectively, were enrolled in this study. Two board-certified neurosurgeons participated as observers to identify the ScA based on UHR-CTA and C-CTA images.

Results:

UHR-CTA and C-CTA detected the ScA in 56–58% and 30–40% of the patients, respectively. In visualization of the ScA, UHR-CTA was better than C-CTA (P < 0.05, Fisher’s exact test). Between the two observers, the Cohen’s kappa coefficient was 0.77 for UHR-CTA and 0.78 for C-CTA.

Conclusions:

UHR-CTA is a simple and accessible method to evaluate intracranial vasculature. Visualization of the ScA with UHR-CTA was better than that with C-CTA. The high quality of UHR-CTA could provide useful information in the neurosurgery field.

COMPARISON OF RADIATION DOSE AND IMAGE QUALITY IN HEAD CT SCANS AMONG MULTIDETECTOR CT SCANNERS

The present study compares three different multidetector CT (MDCT) scanners for routine brain imaging in terms of image quality and radiation doses. The volume CT dose index (CTDIvol), dose-length product (DLP), and effective dose (E) were calculated. Subjective image assessment was obtained based on a scale ranging from 1 (unacceptable) to 5 (optimum). All images scored 3.5 or over, with the 160-slice MDCT images being favoured. For the 4-, 16- and 160-slice MDCT scanners, the respective median values for CTDIvol were 57 mGy, 41 mGy, and 28 mGy; DLP values were 901 mGy.cm, 680 mGy.cm, and 551 mGy.cm; and effective doses were 2 mSv, 1.5 mSv, and 1 mSv, respectively. Compared to the 160-slice MDCT, the dose values for the 4- and 16-slice units were significantly greater. In practice, the CT modality used must be carefully selected to avoid elevated radiation doses and maintain image quality.

Carotid computed tomography angiography after cobalt-based alloy carotid artery stenting using ultra-high-resolution computed tomography with model-based iterative reconstruction

In conventional carotid computed tomographic angiography, the artifacts of the stent vary depending on the structure and characteristics of the alloy type. Cobalt-based alloy stents have been reported to exhibit high artifacts, and accurate evaluation of the internal lumen can be difficult. Recently, ultra-high-resolution computed tomography scanner systems have become available for clinical practice. The primary features of this computed tomography scanner are a 0.25-mm detector row width and a 1024 × 1024 matrix. We report a case-series of carotid artery stenting using a cobalt-based alloy stent scanned by an ultra-high-resolution computed tomography scanner system and model-based iterative reconstruction. We also report that the combination of the ultra-high-resolution computed tomography scanner system with model-based iterative reconstruction would be useful to evaluate vessel patency after placement of a cobalt-based alloy stent.

A Pilot Study to Estimate the Impact of High Matrix Image Reconstruction on Chest Computed Tomography

Objectives:

The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction.

Material and Methods:

This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed.

Results:

Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001).

Conclusion:

High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

Ultra-high resolution computed tomography of joints: practical recommendations for acquisition protocol optimization

Background

To assess the influence on the spatial resolution of various Ultra-high-resolution computed tomography (CT) parameters and provide practical recommendations for acquisition protocol optimization in musculoskeletal imaging.

Methods

All acquisitions were performed with an Ultra-high resolution scanner, and variations of the following parameters were evaluated: field-of-view (150-300 mm), potential (80-140 KVp), current (25-250 mAs), focal spot size (0.4×0.5 to 0.8×1.3 mm²), slice thickness (0.25-0.5 mm), reconstruction matrix (512×512 to 2048×2048), and iso-centering (up to 85 mm off-center). Two different image reconstruction algorithms were evaluated: hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR). CATPHAN 600 phantom images were analyzed to calculate the number of visible line pairs per centimeter (lp/cm). Task transfer function (TTF) curves were calculated to quantitatively evaluate spatial resolution. Cadaveric knee acquisitions were also performed.

Results

Under the conditions studied, the factor that most intensely influenced spatial resolution was the matrix size (additional visualization of up to 8 lp/cm). Increasing the matrix from 512×512 to 2048×2048 led to a 28.2% increase in TTF10% values with a high-dose protocol and a 5.6% increase with a low-dose protocol with no change in the number of visually distinguishable line pairs. The second most important factor affecting spatial resolution was the tube output (29.6% TTF10% gain and 5 additional lp/cm visualized), followed by the reconstruction algorithm choice and lateral displacement (both with a 4 lp/cm gain). Decreasing the slice thickness from 0.5 to 0.25 mm, led to an increase of 3 lp/cm (from 17 to 20 lp/cm) and a 17.3% increase in TTF10% values with no change in the "in-plane" spatial resolution.

Conclusions

This study provides practical recommendations for spatial resolution optimization using Ultra-high-resolution CT.

Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study

OBJECTIVES

This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner.

METHODS

The mandible tooth phantom with and without the lesion was scanned using super-high-resolution, high-resolution (HR), and normal-resolution (NR) modes. Images were reconstructed with deep learning-based reconstruction (DLR) and hybrid iterative reconstruction (HIR) using the MAR algorithm. Two dental radiologists independently graded the degree of metal artefact (1, very severe; 5, minimum) and lesion shape reproducibility (1, slight; 5, almost perfect). The signal-to-artefact ratio (SAR), accuracy of the CT number of the lesion, and image noise were calculated quantitatively. The Tukey-Kramer method with a p-value of less than 0.05 was used to determine statistical significance.

RESULTS

The HR_DLR visual score was better than the NR_HIR score in terms of degree of metal artefact (4.6 ± 0.5 and 2.6 ± 0.5, p < 0.0001) and lesion shape reproducibility (4.5 ± 0.5 and 2.9 ± 1.1, p = 0.0005). The SAR of HR_DLR was significantly better than that of NR_HIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HR_DLR was lower than that in NR_HIR (0.8% in HR_DLR and 23.8% in NR_IR). The image noise of HR_DLR was lower than that of NR_HIR (15.7 ± 1.4 and 51.6 ± 15.3, p < 0.0001).

CONCLUSIONS

Our study demonstrated that the combination of HR mode and DLR in UHRCT scanner improved the impact of the MAR algorithm in the oral cavity.

Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction

OBJECTIVE

The aim of the study was to compare computed tomography (CT) angiography (CTA) imaging of deep inferior epigastric artery perforator (DIEP) using the ultrahigh-resolution CT (UHRCT) and conventional multidetector CT (MDCT).

METHODS

This retrospective study enrolled 20 patients who underwent CTA of DIEP flap with UHRCT and MDCT. Computed tomography values were measured at 4 large vessels (thoracic aorta, abdominal aorta, common iliac artery, and external iliac artery) and 5 peripheral vessels (proximal and distal internal thoracic artery, proximal and distal deep inferior epigastric artery, and DIEP).

RESULTS

There were no significant differences in mean CT values of the major vessel between UHRCT and MDCT. Ultrahigh-resolution CT shows higher CT values of the peripheral vessels than MDCT (P < 0.05 for all). The median CT values of the DIEP in UHRCT were approximately 3 times higher than those in MDCT (P < 0.001).

CONCLUSIONS

Ultrahigh-resolution CT provides higher-quality CTA of DIEP compared with MDCT.

A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset

PURPOSE

To improve the image quality of inflated fixed cadaveric human lungs by utilizing ultra-high-resolution computed tomography (U-HRCT) as a training dataset for super-resolution processing using deep learning (SR-DL).

MATERIALS AND METHODS

Image data of nine cadaveric human lungs were acquired using U-HRCT. Three different matrix images of U-HRCT images were obtained with two acquisition modes: normal mode (512-matrix image) and super-high-resolution mode (1024- and 2048-matrix image). SR-DL used 512- and 1024-matrix images as training data for deep learning. The virtual 2048-matrix images were acquired by applying SR-DL to the 1024-matrix images. Three independent observers scored normal anatomical structures and abnormal computed tomography (CT) findings of both types of 2048-matrix images on a 3-point scale compared to 1024-matrix images. The image noise values were quantitatively calculated. Moreover, the edge rise distance (ERD) and edge rise slope (ERS) were also calculated using the CT attenuation profile to evaluate margin sharpness.

RESULTS

The virtual 2048-matrix images significantly improved visualization of normal anatomical structures and abnormal CT findings, except for consolidation and nodules, compared with the conventional 2048-matrix images (p < 0.01). Quantitative noise values were significantly lower in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.001). ERD was significantly shorter in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01). ERS was significantly higher in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01).

CONCLUSION

SR-DL using original U-HRCT images as a training dataset might be a promising tool for image enhancement in terms of margin sharpness and image noise reduction. By applying trained SR-DL to U-HRCT SHR mode images, virtual ultra-high-resolution images were obtained which surpassed the image quality of unmodified SHR mode images.

Detectability of pulmonary ossifications in fibrotic lung on ultra-high-resolution CT using 2048 matrix size and 0.25-mm slice thickness

To investigate the prevalence of nodular pulmonary ossifications (POs) in patients with honeycombing on ultra-high-resolution CT (UHRCT) and to compare the detectability of nodular POs between images reconstructed using the ultra-high-resolution setting (UHR-setting) and those using the conventional setting (C-setting) on UHRCT. Twenty patients with honeycombing in the lung were evaluated retrospectively. All patients underwent non-contrast-enhanced UHRCT. Images were reconstructed with UHR-setting (matrix, 2048 × 2048; slice thickness, 0.25 mm) and with C-setting (matrix size, 512 × 512; slice thickness, 0.5 mm). Two chest radiologists independently recorded the number of nodular POs (< 4 mm diameter) in each lung lobes. Each lobe was classified as one of the following five categories according to the number of POs: C0, none; C1, 1-4 POs; C2, 5-9 POs; C3, 10-49 POs; and C4, ≥ 50 POs. The maximum CT values of the POs were measured and compared between the two settings. PO categories were significantly higher with UHR-setting than with C-setting (p < 0.001). Maximum CT values were significantly higher with UHR-setting than with C-setting (p < 0.001). Nodular POs were seen in 80% or more of patients with honeycombing and more easily detected in images reconstructed with UHR-setting than in those with C-setting.

Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment

Photon counting x-ray detectors (PCDs) with spectral capabilities have the potential to revolutionize computed tomography (CT) for medical imaging. The ideal PCD provides accurate energy information for each incident x-ray, and at high spatial resolution. This information enables material-specific imaging, enhanced radiation dose efficiency, and improved spatial resolution in CT images. In practice, PCDs are affected by non-idealities, including limited energy resolution, pulse pileup, and cross talk due to charge sharing, K-fluorescence, and Compton scattering. In order to maximize their performance, PCDs must be carefully designed to reduce these effects and then later account for them during correction and post-acquisition steps. This review article examines algorithms for using PCDs in spectral CT applications, including how non-idealities impact image quality. Performance assessment metrics that account for spatial resolution and noise such as the detective quantum efficiency (DQE) can be used to compare different PCD designs, as well as compare PCDs with conventional energy integrating detectors (EIDs). These methods play an important role in enhancing spectral CT images and assessing the overall performance of PCDs.

Application of Multi-Slice Spiral CT in the Evaluation of Diffuse Lung Diseases

This article analyzes the manifestations, characteristics, and significance of multi-slice spiral CT for diffuse lung disease, and evaluates the diagnostic value of multi-slice CT multi-directional reconstruction for diffuse lung disease. After performing multi-slice spiral CT examination on the patient and collecting relevant data, the characteristic multi-slice CT imaging findings of diffuse lung disease were determined by statistical analysis. Diffuse lung disease is representative in multi-slice spiral CT image imaging manifestations of the disease include multiple disseminated small nodules, multiple voids, ground glass shadows, and lung consolidation. And analyze the correlation of image performance, and then use statistical methods to analyze and evaluate the value of multi-slice spiral CT characteristic images in the diagnosis of diffuse lung disease, and analyze the characteristics of these characteristic multi-slice CT image appearances. The use of high-resolution CT to screen the characteristic CT imaging findings of the same research object, and then to perform a statistical analysis of the diagnostic differences with multi-slice spiral CT, further confirmed the importance of multi-slice CT for diffuse lung disease Diagnostic value. Studies have shown that multi-slice CT imaging technology is of great significance in the evaluation of diffuse lung diseases.

Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study

Purpose

In an ultrahigh‐resolution CT (U‐HRCT), deep learning‐based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol.

Methods

For the normal‐sized abdominal models, a Catphan 600 was scanned by U‐HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model‐based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms.

Results

Deep learning‐based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the 100% and 50% radiation doses (100%, 50%, DLR: 15.4, 16.9 vs MBIR: 10.2, 15.6 Hounsfield units: HU). However, at the 25% radiation dose, the image noise in DLR was lower than that in MBIR (16.7 vs. 26.6 HU). The spatial frequency at 10% of the modulation transfer function (MTF) in DLR was 1.0 cycles/mm, slightly lower than that in MBIR (1.05 cycles/mm) at the 100% radiation dose. Even when the radiation dose decreased, the spatial frequency at 10% of the MTF of DLR did not change significantly (50% and 25% doses, 0.98 and 0.99 cycles/mm, respectively).

Conclusion

Deep learning‐based reconstruction performs more consistently at decreasing dose in abdominal ultrahigh‐resolution CT compared to all other commercially available reconstruction algorithms evaluated.

Quantitative volumetry of ground-glass nodules on high-spatial-resolution CT with 0.25-mm section thickness and 1024 matrix: Phantom and clinical studies

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High-spatial-resolution CT provided more accurate volume of a −800-HU nodule in a phantom than conventional settings.

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The maximum CT attenuation values were significantly higher in high-resolution setting than conventional setting.

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The high-resolution setting might allow earlier detection of solid components in GGNs during follow-up.

Objectives

To compare high-resolution (HR) and conventional (C) settings of high-spatial-resolution computed tomography (CT) for software volumetry of ground-glass nodules (GGNs) in phantoms and patients.

Methods

We placed −800 and −630 HU spherical GGN-mimic nodules in 28 different positions in phantoms and scanned them individually. Additionally, 60 GGNs in 45 patients were assessed retrospectively. Images were reconstructed using the HR-setting (matrix size, 1024; slice thickness, 0.25 mm) and C-setting (matrix size, 512; slice thickness, 0.5 mm). We measured the GGN volume and mass using software. In the phantom study, the absolute percentage error (APE) was calculated as the absolute difference between Vernier caliper measurement-based and software-based volumes. In patients, we measured the density (mean, maximum, and minimum) and classified GGNs into low- and high-attenuation GGNs.

Results

In images of the −800 HU, but not −630 HU, phantom nodules, the volumes and masses differed significantly between the two settings (both p < 0.01). The APE was significantly lower in the HR-setting than in the C-setting (p < 0.01). In patients, volumes did not differ significantly between settings (p = 0.59). Although the mean attenuation was not significantly different, the maximum and minimum values were significantly increased and decreased, respectively, in the HR-setting (both p < 0.01). The volumes of both low-attenuation and high-attenuation GGNs were not significantly different between settings (p = 0.78 and 0.39, respectively).

Conclusion

The HR-setting might yield a more accurate volume for phantom GGN of −800 HU and influence the detection of maximum and minimum CT attenuation.

Visualization of small visceral arteries on abdominal CT angiography using ultra-high-resolution CT scanner

PURPOSE

To evaluate the image quality and ability to delineate the small visceral arteries of high-resolution (HR) abdominal CT angiography (CTA) using an ultra-high-resolution computed tomography (UHR CT) scanner.

MATERIALS AND METHODS

Thirty-seven patients were enrolled who underwent abdominal CTA using a UHR CT scanner. The images were reconstructed with a matrix of 1024 × 1024 and 0.25 mm thickness for HR CTA and with a matrix of 512 × 512 and 0.5 mm thickness for normal resolution (NR) CTA. Maximum CT value, image quality, and delineation of the small arteries were compared between HR CTA and NR CTA.

RESULTS

HR CTA showed significantly higher maximum CT value, higher image quality, and better delineation of the small arteries than did NR CTA (P < .005).

CONCLUSION

HR CTA using a UHR CT scanner showed higher image quality than NR CTA and enhanced the delineation of visceral arteries.

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study

PURPOSE

To compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR).

METHOD

Totally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging.

RESULTS

Groups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001).

CONCLUSIONS

Reduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.

Photon-counting x-ray detectors for CT

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT

OBJECTIVES

We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value.

METHODS

Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed.

RESULTS

With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images.

CONCLUSIONS

DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose.

KEY POINTS

• Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT. • Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality. • Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.