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.

Low Radiation Dose and High Image Quality of 320-Row Coronary Computed Tomography Angiography Using a Small Dose of Contrast Medium and Refined Scan Timing Prediction.

OBJECTIVE: The aim of the study was to investigate the feasibility of coronary computed tomography (CT) angiography with a low kilovoltage peak scan and a refined scan timing prediction using a small contrast medium (CM) dose. METHODS: In protocol A, 120-kVp scanning and a standard CM dose were used. The scan timing was fixed. In protocol B, 80 kVp and a 60% CM dose were used. The scan timing was determined according to the interval from the CM arrival to the peak time in the ascending aorta. We measured the CT number and recorded the radiation dose. RESULTS: Higher CT numbers were observed in the left circumflex (proximal, P = 0.0235; middle, P = 0.0007; distal, P < 0.0001) in protocol B compared with protocol A. The radiation dose in protocol B was significantly lower than in protocol A (2.2 ± 0.9 vs 4.3 ± 1.7 mSv). CONCLUSIONS: Low-contrast, low-radiation dose, high-image quality coronary CT angiography can be performed with low kilovoltage peak scanning and a refined scan timing prediction.

[Improvement of Image Quality in the Axial Section Using High-resolution Scan Mode and Hybrid Iterative Reconstruction in Ultra-high-resolution Computed Tomography].

The purpose of this study is to compare the physical characteristics and visibility of high-resolution and conventional images acquired with the same X-ray dose, and to investigate the superiority of super high-resolution imaging. A Catphan phantom was scanned in the normal resolution (NR), high-resolution (HR), and super high-resolution (SHR) modes of ultra-high-resolution computed tomography at 120 kV and 75 mAs. All images were reconstructed into a 5-mm thick image slices with filtered back-projection (FBP) and hybrid image reconstruction (HIR), which included normal and enhanced adaptive iterative dose reduction 3D (AIDR and eAIDR, respectively). The modulation transfer function (MTF) and noise power spectrum (NPS) were measured using the circular edge method and radial frequency method, respectively. The signal-to-noise ratio (SNR) was then calculated. High-contrast resolution and low-contrast detectability were evaluated visually by five radiological technologists. The MTFs of HReAIDR and HRFBP images were higher than those of NRFBP images. However, the NPSs of HReAIDR and HRFBP images were larger than those of NRFBP images. The SNR of HReAIDR images was higher than that of NRFBP and HRFBP images. The scores of high-contrast resolution of HReAIDR, NRFBP, and HRFBP images were 13, 8, and 13 cycles/cm, respectively, and the scores of low-contrast detectability were 5, 5, and 6 mm, respectively. Hence, an improvement in high-contrast resolution of signal more than 400 HU in the axial section can be achieved without increasing the radiation dose and decreasing low-contrast detectability with 10 HU using the HR mode and eAIDR.

Enantioselective Detection of Gaseous Odorants with Peptide-Graphene Sensors Operating in Humid Environments.

Replicating the sense of smell presents an ongoing challenge in the development of biomimetic devices. Olfactory receptors exhibit remarkable discriminatory abilities, including the enantioselective detection of individual odorant molecules. Graphene has emerged as a promising material for biomimetic electronic devices due to its unique electrical properties and exceptional sensitivity. However, the efficient detection of nonpolar odor molecules using transistor-based graphene sensors in a gas phase in environmental conditions remains challenging due to high sensitivity to water vapor. This limitation has impeded the practical development of gas-phase graphene odor sensors capable of selective detection, particularly in humid environments. In this study, we address this challenge by introducing peptide-functionalized graphene sensors that effectively mitigate undesired responses to changes in humidity. Additionally, we demonstrate the significant role of humidity in facilitating the selective detection of odorant molecules by the peptides. These peptides, designed to mimic a fruit fly olfactory receptor, spontaneously assemble into a monomolecular layer on graphene, enabling precise and specific odorant detection. The developed sensors exhibit notable enantioselectivity, achieving a remarkable 35-fold signal contrast between d- and l-limonene. Furthermore, these sensors display distinct responses to various other biogenic volatile organic compounds, demonstrating their versatility as robust tools for odor detection. By acting as both a bioprobe and an electrical signal amplifier, the peptide layer represents a novel and effective strategy to achieve selective odorant detection under normal atmospheric conditions using graphene sensors. This study offers valuable insights into the development of practical odor-sensing technologies with potential applications in diverse fields.

A deep learning model based on fusion images of chest radiography and X-ray sponge images supports human visual characteristics of retained surgical items detection.

PURPOSE: Although a novel deep learning software was proposed using post-processed images obtained by the fusion between X-ray images of normal post-operative radiography and surgical sponge, the association of the retained surgical item detectability with human visual evaluation has not been sufficiently examined. In this study, we investigated the association of retained surgical item detectability between deep learning and human subjective evaluation. METHODS: A deep learning model was constructed from 2987 training images and 1298 validation images, which were obtained from post-processing of the image fusion between X-ray images of normal post-operative radiography and surgical sponge. Then, another 800 images were used, i.e., 400 with and 400 without surgical sponge. The detection characteristics of retained sponges between the model and a general observer with 10-year clinical experience were analyzed using the receiver operator characteristics. RESULTS: The following values from the deep learning model and observer were, respectively, derived: Cutoff values of probability were 0.37 and 0.45; areas under the curves were 0.87 and 0.76; sensitivity values were 85% and 61%; and specificity values were 73% and 92%. CONCLUSION: For the detection of surgical sponges, we concluded that the deep learning model has higher sensitivity, while the human observer has higher specificity. These characteristics indicate that the deep learning system that is complementary to humans could support the clinical workflow in operation rooms for prevention of retained surgical items.

Importance of the heart rate in ultra-high-resolution coronary CT angiography with 0.35 s gantry rotation time.

PURPOSE: We investigated the effects of the heart rate (HR) on the motion artifact in coronary computed tomography angiography (CCTA) with ultra-high-resolution-CT (U-HRCT), and we clarified the upper limit of optimal HR in CCTA with U-HRCT in a comparison with conventional-resolution-CT (CRCT) on a cardiac phantom and in patients with CCTA. MATERIALS AND METHODS: A pulsating cardiac phantom equipped with coronary models was scanned at static and HR simulations of 40-90 beats/min (bpm) at 10-bpm intervals using U-HRCT and CRCT, respectively. The sharpness and lumen diameter of the coronary model were quantitatively compared between U-HRCT and CRCT stratified by HR in the phantom study. We also assessed the visual inspections of clinical images in CCTA with U-HRCT. RESULTS: At the HRs ≤ 60 bpm, the error of the lumen diameter of the U-HRCT tended to be smaller than that of the CRCT. However, at the HRs > 60 bpm, the inverse was shown. For the image sharpness, the U-HRCT was significantly superior to the CRCT (p < 0.05). In the visual assessment, the scores were negatively correlated with HRs in patients (Spearman r = - 0.71, p < 0.01). A receiver-operating characteristic analysis revealed the HR of 61 bpm as the optimal cutoff of the non-diagnostic image quality, with an area under the curve of 0.87, 95% sensitivity, and 71% specificity. CONCLUSION: At HRs ≤ 60 bpm, U-HRCT was more accurate in the imaging of coronary arteries than CRCT. The upper limit of the optimal HR in CCTA with U-HRCT was approx. 60 bpm.

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 HRDLR visual score was better than the NRHIR 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 HRDLR was significantly better than that of NRHIR (4.9 ± 0.4 and 2.1 ± 0.2, p < 0.0001), and the absolute percentage error of the CT number in HRDLR was lower than that in NRHIR (0.8% in HRDLR and 23.8% in NRIR). The image noise of HRDLR was lower than that of NRHIR (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.

Improvement of Image Quality Using Hybrid Iterative Reconstruction with Noise Power Spectrum Model in Computed Tomography During Hepatic Arteriography.

OBJECTIVES: In CT during hepatic arteriography (CTHA), the addition of a noise power spectrum (NPS) model to conventional hybrid iterative reconstruction (HIR) may improve spatial resolution and reduce image noise. This study aims at assessing the image quality provided by HIR with a NPS model at CTHA. METHODS: This institutional review board-approved retrospective analysis included 26 patients with hepatocellular carcinomas (HCCs) who underwent CTHA. In all acquisitions, images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR), and AIDR enhanced (eAIDR) with the NPS model. Four radiologists analyzed the signal-to-noise ratio (SNR) of HCC nodules and its associated feeding arteries. The radiologists used a semiquantitative scale (-3 to +3) to rate the subjective image quality comparing both the FBP and eAIDR images with the AIDR images. RESULTS: The feeding arteries' attenuation was significantly higher in eAIDR compared to AIDR [514.3 ± 121.4 and 448.3 ± 107.3 Hounsfield units (HU), p < 0.05]. The image noise of eAIDR was significantly lower than that of FBP (15.2 ± 2.2 and 28.5 ± 4.8 HU, p < 0.05) and comparable to that of AIDR. The SNR of feeding arteries on eAIDR was significantly higher than on AIDR (34.1 ± 7.9 and 27.4 ± 6.3, p < 0.05). Subjective assessment scores showed that eAIDR provided better visibility of feeding arteries and overall image quality compared to AIDR (p < 0.05). The HCC nodule visibility was not significantly different among the three reconstructions. CONCLUSION: In CTHA, eAIDR improved the visibility of feeding arteries associated with HCC nodules without compromising nodule detection.

X-ray dose reduction using additional copper filtration for dental cone beam CT.

This study aims to quantitatively evaluate the effect of additional copper-filters (Cu-filters) on the radiation dose and contrast-to-noise ratio (CNR) in a dental cone beam computed tomography (CBCT). The Cu-filter thickness and tube voltage of the CBCT unit were varied in the range of 0.00-0.20 mm and 70-90 kV, respectively. The CBCT images of a phantom with homogeneous materials of aluminum, air, and bone equivalent material (BEM) were acquired. The CNRs were calculated from the voxel values of each homogeneous material. The CTDIvol was measured using standard polymethyl methacrylate CTDI test objects. We evaluated and analyzed the effects of tube current and various radiation qualities on the CNRs and CTDIvol. We observed a tendency for higher CNR at increasing tube voltage and tube current in all the homogeneous materials. On the other hand, the CNR reduced at increasing Cu-filter thickness. The tube voltage of 90 kV showed a clear advantage in the tube current-CNR curves in all the homogeneous materials. The CTDIvol increased as the tube voltage and tube current increased and decreased with the increase in the Cu-filter thickness. When the CNR was fixed at 9.23 of BEM at an exposure setting of 90 kV/5 mA without a Cu-filter, the CTDIvol at 90 kV with Cu-filters was 8.7% lower compared with that at 90 kV without a Cu-filter. The results from this study demonstrate the potential of adding a Cu-filter for patient dose reduction while ensuring the image quality.

Optimal scan timing for artery-vein separation at whole-brain CT angiography using a 320-row MDCT volume scanner.

OBJECTIVE: A 320-row multidetector CT (MDCT) is expected for a good artery-vein separation in terms of temporal resolution. However, a shortened scan duration may lead to insufficient vascular enhancement. We assessed the optimal scan timing for the artery-vein separation at whole-brain CT angiography (CTA) when bolus tracking was used at 320-row MDCT. METHODS: We analyzed 60 patients, who underwent whole-brain four-dimensional CTA. Difference in CT attenuation between the internal carotid artery (ICA) and the superior sagittal sinus (Datt) was calculated in each phase. Using a visual evaluation score for the depiction of arteries and veins, we calculated the difference between the mean score for the intracranial arteries and the mean score for the veins (Dscore). We assessed the time at which the maximum Datt and Dscore were simultaneously observed. RESULTS: The maximum Datt was observed at 6.0 s and 8.0 s in the arterial-dominant phase and at 16.0 s and 18.0 s in the venous-dominant phase after the contrast media arrival time at the ICA (Taa). The maximum Dscore was observed at 6.0 s and 8.0 s in the arterial-dominant phase and at 16.0 s in the venous-dominant phase after the Taa. There were no statistically significant differences in Datt (p = 0.375) or Dscore (p = 0.139) between these scan timings. CONCLUSION: The optimal scan timing for artery-vein separation at whole-brain CTA was 6.0 s or 8.0 s for the arteries and 16.0 s for the veins after the Taa. Advances in knowledge: Optimal scan timing allowed us to visualize intracranial arteries or veins with minimal superimposition.

Agreement in depression determination among four self-rating depression scales applied to Japanese community-dwelling elderly.

OBJECTIVE: In this study, we examined the characteristics of depression determination using four representative self-rating depression scales (Geriatric Depression Scale, GDS; Self-rating Depression Scale, SDS; Center for Epidermiologic Studies Depression Scale, CES-D; and Carroll Rating Scale, CRS) applied to Japanese community-dwelling elderly. METHODS: Subjects were 563 community-dwelling independent elderly living in twelve prefectures (330 males, 68,9±6.3 yr; 233 females, 68.1±5.8 yr). RESULTS: Depression rates determined using SDS (45.8%) and CES-D (68.6%) were higher than those determined using GDS (5.7%) and CRS (14.7%). Although correlations of depression scale scores among the four scales were significant and comparable (r: 0.61 (GDS vs. SDS, p<0.01) to 0.78 (SDS vs. CES-D, p<0.01)), the agreement in depression determination varied among scales (kappa coefficients: 0.05 (GDS vs. CES-D, p>0.05) to 0.46 (SDS vs. CES-D, p<0.01)). CONCLUSIONS: Similarities in depression determination were found between GDS and CRS, and between CES-D and SDS. Depression rates determined on the basis of cut-off point for each scale were higher for CES-D and SDS than for GDS and CRS. Depression determination using a four-point rating scale may overestimate a slightly depressive symptom, compared with that using a two-point scale.

A new diagnostic method for chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma based on serum metallothionein, copper, and zinc levels.

Serum metal levels and their ratios are frequently reported to be good signals for diagnosing various diseases. These parameters are not always specific to the disease, however, it is necessary to use other serum parameters for an exact diagnosis. We examined whether the monitoring of these serum parameters such as metallothionein, copper, and zinc levels are useful in diagnosing hepatic disorders. Metallothionein levels of patients with liver cirrhosis and hepatocellular carcinoma were found to be significantly lower than those of patients with chronic hepatitis and those of controls. In contrast, copper levels of the patients with liver cirrhosis and hepatocellular carcinoma were significantly higher than those with chronic hepatitis and controls. Zinc levels of the patients with chronic hepatitis and hepatocellular carcinoma were lower than those of controls. Using these three parameters, we are introducing a new parameter, (Cu/Zn)/MT, by which we can discriminate between patients in the [control+miscellaneous diseases+chronic hepatitis] group and those in the [liver cirrhosis+hepatocellular carcinomal group. The new parameter does not, however, allow us to clearly distinguish between the liver cirrhosis and hepatocellular carcinoma groups. Multivariate discriminant analysis was found to be very useful, with combinations of two discriminant functions having been designed to discriminate both between chronic hepatitis and liver cirrhosis and between liver cirrhosis and hepatocellular carcinoma. This method recognizes the differences between hepatic disorder, including chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma groups. On the basis of these results, we propose here that the diagnosis of hepatic disorders should be made based on a combination of three serum levels such as those of metallothionein, copper, and zinc.