Size-specific dose estimates for adult patients at CT of the torso

Quantification of water and lipid composition of perivascular adipose tissue using coronary CT angiography: a simulation study

Early detection of vascular inflammation via perivascular adipose tissue (PVAT) compositional changes (e.g., increased water content) could improve cardiovascular risk stratification. However, CT-based measurements face variability due to tube voltage and patient size. This study aims to quantify perivascular adipose tissue (PVAT) composition (water, lipid, protein) using coronary CT angiography and assess impacts of tube voltage, patient size, and positional variability on measurements. A 320-slice CT simulation generated anthropomorphic thorax phantoms (small, medium, large) with fat rings mimicking different patient sizes. Ten randomized water-lipid-protein inserts were placed within the thorax phantom. Three-material decomposition was applied using medium phantoms with different tube voltages and different patient sizes at 120 kV. PVAT CT number (HU) increased with higher tube voltages and larger patient sizes. The root-mean-squared errors (RMSE) for water volumetric fraction measurements were 0.26%, 0.64%, 0.01%, and 0.15% for 80, 100, 120, and 135 kV, respectively, and 0.19%, 0.35%, and 0.61% for small, medium, and large size phantoms at 120 kV, respectively. The root-mean-squared deviations (RMSD) were 3.52%, 2.94%, 4.96%, and 6.00% for 80, 100, 120, and 135 kV, respectively, and 3.82%, 3.74%, and 6.05% for small, medium, and large size phantoms at 120 kV, respectively. Clinically relevant water fractions spanned 17-37%, with inflammation expected to alter values by approximately 5%. The findings of this study indicate that, after accounting for the effects of tube voltage and patient size, perivascular adipose tissue CT number can be quantitatively represented in terms of its water composition. This decomposition method has the potential to enable quantification of water composition and facilitate early detection of coronary artery inflammation.

Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study

OBJECTIVES

In this work, it was aimed to employ machine learning (ML) algorithms to accurately forecast the radiation doses for phantoms while accounting for the most popular CT protocols.

METHODS

A cloud-based software was utilized to calculate the effective doses from different CT protocols. To simulate a range of adult patients with different weights, eight entire body mesh-based computational phantom sets were used. The head, neck, and chest-abdomen-pelvis CT scan characteristics were combined to create a dataset with 33 rows for each phantom and 792 rows total. At the ML stage, linear (LR), random forest (RF) and support vector regression (SVR) were used. Mean absolute error, mean squared error and accuracy were used to evaluate the performances.

RESULTS

The female phantoms received higher doses (7.8 %) than males. Furthermore, an average of 11 % more dose was taken to the normal weight phantom than to the overweight, the overweight in comparison to the obese I, and the obese I in comparison to the obese II. Among the ML algorithms, the LR showed 0 error rate and 100 % accuracy in predicting CT doses.

CONCLUSIONS

The LR was shown to be the best approach out of those used in the ML estimation of CT-induced doses.

Size-specific dose estimates calculated using patient size measurements from scanned projection radiograph in high-resolution chest computed tomography

INTRODUCTION

Size-specific dose estimates (SSDE) are used to assess patient-specific radiation exposure in Computed Tomography (CT), complementing the volume CT dose index (CTDIvol). This study compared SSDE calculated using patient's lateral size from scan projection radiograph (SPR) with SSDE calculated using water equivalent diameter (Dw) from tomographic images in adult chest high-resolution CT (HRCT).

METHODS

In a single-centre study, the CTDIvol and dose-length product (DLP) were recorded from HRCT dose reports of adult patients. Lateral width (SLat), at the centre of the scan range, from the SPR was measured and the SSDE (SSDER) was calculated using conversion factors related to SLat. Average CT number, area of the slice, and lateral size of the patient (AxLat) were measured on the middle slice. The Dw and SSDE from Dw (SSDEW) were calculated. SSDER and SSDEW were compared using Wilcoxon signed rank test. Correlation between patient size and dosimetry parameters were investigated using Spearman Correlation test with statistical significance at P < 0.05. Bland-Altman plot was also used to test agreement between the two SSDE values.

RESULTS

Median CTDIvol, DLP, SSDER and SSDEW were 11.0 mGy, 372 mGy.cm, 11.6 mGy and 12.9 mGy, respectively. Small but statistically significant differences (P < 0.03) were found between SLat and AxLat as well as between SSDER and SSDEW. Bland-Altman analysis resulted in borderline agreement between SSDE values. Moderate correlations were observed between dosimetry quantities and patient size measurements (ρ > 0.640; P < 0.001). SSDEw showed statistically significant correlation (ρ = 0.587 and P < 0.001) with SSDER.

CONCLUSION

SSDER may be used to assess patients' absorbed radiation dose, before the scan, in adult chest HRCT. The median value of SSDER was about 10% lower than the median value SSDEW. However, the SSDEW should be used after the scan to establish effective dose and radiation risk to the patient.

Evolved size-specific dose estimates for patient-specific organ doses from chest CT scans based on hybrid patient size vectors

This study aims to develop a neural network-based method for predicting patient-specific organ doses from chest CT scans, utilizing hybrid patient size vectors for enhanced computational efficiency, accuracy, and generality. A dataset of 705 chest CT scans was retrospectively analyzed to construct predictive models for organ dose estimation. The proposed approach employs high dimensional hybrid vectors to represent patient size, combining muti-slice parameters regarding lateral dimension, anteroposterior dimension, and water-equivalent diameter (Dw). These vectors are used to train fully-connected neural networks, which are designed to correlate high-dimensional patient size features with reference organ doses obtained from Monte Carlo simulations. The performance of the neural networks was evaluated using separate test cohorts, with metrics such as mean absolute percentage error (MAPE) and coefficient of determination (R²) to evaluate predictive accuracy and generality. For the left lung, right lung, heart, and spinal cord, the trained neural networks respectively achieve MAPE values of 2.94%, 2.79%, 7.04%, and 6.76%, and R² values of 0.98, 0.99, 0.93, and 0.91. The maximal discrepancy between reference and predicted values is less than 10% for the left and right lungs, and less than 20% for the heart and spinal cord. With 5-fold cross-validation, the maximal perturbation does not exceed 1% in MAPE and 0.05 in R². By incorporating hybrid patient size vectors, the neural network models achieve superior accuracy in organ dose estimation compared with traditional size specific dose estimates, paving the way for online swift organ dose screening in clinical practice.

Non-contrast spectral CT vs chemical-shift MRI in discriminating lipid-poor adrenal lesions

OBJECTIVES

To compare the diagnostic performance of conventional non-contrast CT, dual-energy spectral CT, and chemical-shift MRI (CS-MRI) in discriminating lipid-poor adenomas (> 10-HU on non-contrast CT) from non-adenomas.

METHODS

A total of 110 patients (69 men; 41 women; mean age 66.5 ± 13.4 years) with 80 lipid-poor adenomas and 30 non-adenomas who underwent non-contrast dual-layer spectral CT and CS-MRI were retrospectively identified. For each lesion, non-contrast attenuation on conventional 120-kVp images, ΔHU-index ([attenuation difference between virtual monoenergetic 140-keV and 40-keV images]/conventional attenuation × 100), and signal intensity index (SI-index) were quantified. Each parameter was compared between adenomas and non-adenomas using the Mann-Whitney U-test. The area under the receiver operating characteristic curve (AUC) and sensitivity to achieve > 95% specificity for adenoma diagnosis were determined.

RESULTS

Conventional non-contrast attenuation was lower in adenomas than in non-adenomas (22.4 ± 8.6 HU vs 32.8 ± 48.5 HU), whereas ΔHU-index (148.0 ± 103.2 vs 19.4 ± 25.8) and SI-index (41.6 ± 19.6 vs 4.2 ± 10.2) were higher in adenomas (all, p < 0.001). ΔHU-index showed superior performance to conventional non-contrast attenuation (AUC: 0.919 [95% CI: 0.852-0.963] vs 0.791 [95% CI: 0.703-0.863]; sensitivity: 75.0% [60/80] vs 27.5% [22/80], both p < 0.001), and near equivalent to SI-index (AUC: 0.952 [95% CI: 0.894-0.984], sensitivity 85.0% [68/80], both p > 0.05). Both the ΔHU-index and SI-index provided a sensitivity of 96.0% (48/50) for hypoattenuating adenomas (≤ 25 HU). For hyperattenuating (> 25 HU) adenomas, SI-index showed higher sensitivity than ΔHU-index (66.7% [20/30] vs 40.0% [12/30], p = 0.022).

CONCLUSIONS

Non-contrast spectral CT and CS-MRI outperformed conventional non-contrast CT in distinguishing lipid-poor adenomas from non-adenomas. While CS-MRI demonstrated superior sensitivity for adenomas measuring > 25 HU, non-contrast spectral CT provided high discriminative values for adenomas measuring ≤ 25 HU.

CLINICAL RELEVANCE STATEMENT

Spectral attenuation analysis improves the diagnostic performance of non-contrast CT in discriminating lipid-poor adrenal adenomas, potentially serving as an alternative to CS-MRI and obviating the necessity for additional diagnostic workup in indeterminate adrenal incidentalomas, particularly for lesions measuring ≤ 25 HU.

KEY POINTS

Incidental adrenal lesion detection has increased as abdominal CT use has become more frequent. Non-contrast spectral CT and CS-MRI differentiated lipid-poor adenomas from non-adenomas better than conventional non-contrast CT. For lesions measuring ≤ 25 HU, spectral CT may obviate the need for additional evaluation.

[Accuracy of Effective Diameter and Water Equivalent Diameter Using Phantoms in Various CT Systems]

PURPOSE

The effects of scanning parameters such as CT system performance, CT bed geometry, and upper limb position on effective diameter (ED) and water equivalent diameter (WED) have not been assessed. The purpose of this study was to compare both ED and WED obtained with various CT systems with theoretical values and to assess their accuracy.

METHODS

Jaszczak cylindrical phantom (Data Spectrum, Durham, NC, USA), NEMA IEC body phantom (AcroBio, Tokyo), and thoracic bone phantom were used in this study with and without upper limb phantom. The ED, WED, and size-specific dose estimate (SSDE) obtained using 8 types of CT systems were computed using radiation dose control software.

RESULTS

The EDs had <5% error for all systems, but the error increased as the aspect ratio of the phantom increased. The accuracy of WED varied depending on the CT systems, with a maximum difference of 3.57 cm between systems. The influence of the upper limb depended on the shape of the bed of the CT systems, which affected the correlation between ED as well as WED and SSDE.

CONCLUSION

Although the ED did not show any dependence on the CT system, the accuracy of WED for fusion CT was low. We found that there are issues in the management of scanning data, including the upper limb.

Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography

PURPOSE

Currently, precise patient body weight (BW) at the time of diagnostic imaging cannot always be used for radiation dose management. Various methods have been explored to address this issue, including the application of deep learning to medical imaging and BW estimation using scan parameters. This study develops and evaluates machine learning-based BW prediction models using 11 features related to radiation dose obtained from computed tomography (CT) scans.

METHODS

A dataset was obtained from 3996 patients who underwent positron emission tomography CT scans, and training and test sets were established. Dose metrics and descriptive data were automatically calculated from the CT images or obtained from Digital Imaging and Communications in Medicine metadata. Seven machine-learning models and three simple regression models were employed to predict BW using features such as effective diameter (ED), water equivalent diameter (WED), and mean milliampere-seconds. The mean absolute error (MAE) and correlation coefficient between the estimated BW and the actual BW obtained from each BW prediction model were calculated.

RESULTS

Our results found that the highest accuracy was obtained using a light gradient-boosting machine model, which had an MAE of 1.99 kg and a strong positive correlation between estimated and actual BW (ρ = 0.972). The model demonstrated significant predictive power, with 73% of patients falling within a ±5% error range. WED emerged as the most relevant dose metric for BW estimation, followed by ED and sex.

CONCLUSIONS

The proposed machine-learning approach is superior to existing methods, with high accuracy and applicability to radiation dose management. The model's reliance on universal dose metrics that are accessible through radiation dose management software enhances its practicality. In conclusion, this study presents a robust approach for BW estimation based on CT imaging that can potentially improve radiation dose management practices in clinical settings.

Assessing the inter-observer and intra-observer reliability of radiographic measurements for size-specific dose estimates

BACKGROUND

Calculating size-specific dose estimates (SSDEs) requires measurement of the patient's anteroposterior (AP) and lateral thickness based on computed tomography (CT) images. However, these measurements can be subject to variation due to inter-observer and intra-observer differences. This study aimed to investigate the impact of these variations on the accuracy of the calculated SSDE.

METHODS

Four radiographers with 1-10 years of experience were invited to measure the AP and lateral thickness on 30 chest, abdomen, and pelvic CT images. The images were sourced from an internet-based database and anonymized for analysis. The observers were trained to perform the measurements using MicroDicom software and asked to repeat the measurements 1 week later. The study was approved by the institutional review board at Taibah University, and written informed consent was obtained from the observers. Statistical analyses were performed using Python libraries Pingouin (version 0.5.3), Seaborn (version 0.12.2), and Matplotlib (version 3.7.1).

RESULTS

The study revealed excellent inter-observer agreement for the calculated effective diameter and AP thickness measurements, with Intraclass correlation coefficients (ICC) values of 0.95 and 0.96, respectively. The agreement for lateral thickness measurements was lower, with an ICC value of 0.89. The second round of measurements yielded nearly the same levels of inter-observer agreement, with ICC values of 0.97 for the effective diameter, 1.0 for AP thickness, and 0.88 for lateral thickness. When the consistency of the observer was examined, excellent consistency was found for the calculated effective diameter, with ICC values ranging from 0.91 to 1.0 for all observers. This was observed despite the lower consistency in the lateral thickness measurements, which had ICC values ranging from 0.78 to 1.0.

CONCLUSIONS

The study's findings suggest that the measurements required for calculating SSDEs are robust to inter-observer and intra-observer differences. This is important for the clinical use of SSDEs to set diagnostic reference levels for CT scans.

Introducing fitting models for estimating age-specific dose and effective dose in paediatric patients undergoing head, chest and abdomen-pelvis imaging protocols: a patient study

INTRODUCTION

Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient-specific variables. The aim of this study is to present fitting models for the estimation of age-specific dose estimates (ASDE), in the same direction of size-specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen-pelvis imaging.

METHODS

A total of 583 paediatric patients were included in the study. Radiometric data were gathered from DICOM files. The patients were categorised into five distinct groups (under 15 years of age), and the effective dose, organ dose and ASDE were computed for the CT examinations involving the head, chest and abdomen-pelvis. Finally, the best fitting models were presented for estimation of ASDE and effective doses based on patient age, gender and the type of examination.

RESULTS

The ASDE in head, chest, and abdomen-pelvis CT examinations increases with increasing age. As age increases, the effective dose in head and abdomen-pelvis CT scans decreased. However, for chest scans, the effective dose initially showed a decreasing trend until the first year of life; after that, it increases in correlation with age.

CONCLUSIONS

Based on the presented fitting model for the ASDE, these CT scan quantities depend on factors such as patient age and the type of CT examination. For the effective dose, the gender was also included in the fitting model. By utilising the information about the scan type, region and age, it becomes feasible to estimate the ASDE and effective dose using the models provided in this study.

Diagnostic Reference Levels of Radiographic and CT Examinations in Jordan: A Systematic Review

ABSTRACT

A comprehensive search was performed to examine the literature on diagnostic reference levels (DRL) for computed tomography (CT) and radiography examinations that are performed routinely in Jordan. EBSCO, Scopus, and Web of Science were used for the search. The acronym "DRL" and the additional phrase "dose reference levels" were used to search for articles in literature. Seven papers that reported DRL values for radiography and CT scans in Jordan were identified. One study reported DRLs for conventional radiography, two studies reported CT DRLs in pediatrics, and the remaining four studies provided DRL values for adult CT scans. The most popular techniques for determining the DRLs were the entrance surface dose, volume CT dose index (CTDIvol), and dose-length product (DLP) values. Variations in Jordanian DRL values were noted across both modalities. Lower radiation doses and less variation in DRL values may be achieved by educating and training radiographers to better understand dose reduction strategies. To limit dose variance and enable dosage comparison, CT DRLs must be standardized in accordance with the guidelines of the International Commission on Radiological Protection (ICRP).

Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window

OBJECTIVES

To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma.

METHODS

Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction.

RESULTS

The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05).

CONCLUSION

LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR.

CLINICAL RELEVANCE STATEMENT

The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications.

KEY POINTS

• DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Dual-energy CT of the pancreas: comparison between virtual non-contrast images and true non-contrast images in the detection of pancreatic lesion

PURPOSE

To evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT).

METHODS

One hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase.

RESULTS

A total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587-0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase.

CONCLUSION

VNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

Relationships of Radiation Dose Indices with Body Size Indices in Adult Body Computed Tomography

We investigated the relationships between radiation dose indices and body size indices in adult body computed tomography (CT). A total of 3200 CT scans of the thoracic, abdominal, abdominopelvic, or thoraco-abdominopelvic regions performed using one of four CT scanners were analyzed. Volume CT dose index (CTDIvol) and dose length product (DLP) were compared with various body size indices derived from CT images (water-equivalent diameter, WED; effective diameter, ED) and physical measurements (weight, weight/height, body mass index, and body surface area). CTDIvol showed excellent positive linear correlations with WED and ED. CTDIvol also showed high linear correlations with physical measurement-based indices, whereas the correlation coefficients were lower than for WED and ED. Among the physical measurement-based indices, weight/height showed the strongest correlations, followed by weight. Compared to CTDIvol, the correlation coefficients with DLP tended to be lower for WED, ED, and weight/height and higher for weight. The standard CTDIvol values at 60 kg and dose increase ratios with increasing weight, estimated using the regression equations, differed among scanners. Radiation dose indices closely correlated with body size indices such as WED, ED, weight/height, and weight. The relationships between dose and body size differed among scanners, indicating the significance of dose management considering body size.

Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma

Deep learning-based spectral CT imaging (DL-SCTI) is a novel type of fast kilovolt-switching dual-energy CT equipped with a cascaded deep-learning reconstruction which completes the views missing in the sinogram space and improves the image quality in the image space because it uses deep convolutional neural networks trained on fully sampled dual-energy data acquired via dual kV rotations. We investigated the clinical utility of iodine maps generated from DL-SCTI scans for assessing hepatocellular carcinoma (HCC). In the clinical study, dynamic DL-SCTI scans (tube voltage 135 and 80 kV) were acquired in 52 patients with hypervascular HCCs whose vascularity was confirmed by CT during hepatic arteriography. Virtual monochromatic 70 keV images served as the reference images. Iodine maps were reconstructed using three-material decomposition (fat, healthy liver tissue, iodine). A radiologist calculated the contrast-to-noise ratio (CNR) during the hepatic arterial phase (CNRa) and the equilibrium phase (CNRe). In the phantom study, DL-SCTI scans (tube voltage 135 and 80 kV) were acquired to assess the accuracy of iodine maps; the iodine concentration was known. The CNRa was significantly higher on the iodine maps than on 70 keV images (p < 0.01). The CNRe was significantly higher on 70 keV images than on iodine maps (p < 0.01). The estimated iodine concentration derived from DL-SCTI scans in the phantom study was highly correlated with the known iodine concentration. It was underestimated in small-diameter modules and in large-diameter modules with an iodine concentration of less than 2.0 mgI/ml. Iodine maps generated from DL-SCTI scans can improve the CNR for HCCs during hepatic arterial phase but not during equilibrium phase in comparison with virtual monochromatic 70 keV images. Also, when the lesion is small or the iodine concentration is low, iodine quantification may result in underestimation.

Thermoluminescence Dosimetry in Abdominal CT for Urinary Stone Detection: Effective Radiation Dose Reduction With Tin Prefiltration at 100 kVp

OBJECTIVES

Spectral shaping via tin prefiltration has gained recognition for dose saving in high-contrast imaging tasks. The aim of this phantom dosimetry study was to investigate whether the use of tin filters can also reduce the effective radiation dose in 100 kVp abdominal computed tomography (CT) compared with standard low-dose scans for suspected urolithiasis.

METHODS

Using a third-generation dual-source CT scanner, 4 scan protocols each were used on a standard (P1-P4) and a modified obese Alderson-Rando phantom (P5-P8), in which 11 urinary stones of different compositions were placed. Hereby 1 scan protocol represented standard low-dose settings (P1/P5: 110 kVp/120 kVp), whereas 3 experimental protocols used low-kilovoltage spectral shaping (P2/P3/P4 and P6/P7/P8: 100 kVp with tin prefiltration). Radiation dose was recorded by thermoluminescent dosimeters at 24 measurement sites. For objective assessment of image quality, dose-weighted contrast-to-noise ratios were calculated and compared between scan protocols. Additional subjective image quality analysis was performed by 2 radiologists using equidistant 5-point scales for estimation image noise, artifacts, kidney stone detectability, and delineation of bone and soft tissue.

RESULTS

Both conventional low-dose protocols without tin prefiltration were associated with the highest individual equivalent doses and the highest effective radiation dose in the experimental setup (P1: 0.29-6.43 mGy, 1.45-1.83 mSv; P5: 0.50-9.35 mGy, 2.33-2.79 mSv). With no false-positive diagnoses, both readers correctly detected each of the 11 urinary calculi irrespective of scan protocol and phantom configuration. Protocols using spectral shaping via tin prefiltration allowed for effective radiation dose reduction of up to 38% on the standard phantom and 18% on the modified obese phantom, while maintaining overall diagnostic image quality. Effective dose was approximately 10% lower in a male versus female anatomy and could be reduced by another 10% if gonadal protection was used ( P < 0.001).

CONCLUSIONS

Spectral shaping via tin prefiltration at 100 kVp is a suitable means to reduce the effective radiation dose in abdominal CT imaging of patients with suspected urolithiasis. The dose reduction potential is slightly less pronounced in a modified phantom emulating an obese body composition compared with a standard phantom.

Development of size-specific dose estimates for common computed tomography examinations: a study in Ghana

This study determined the size-specific dose estimate (SSDE) of computed tomography (CT) examinations and derived mathematical expressions for dose output estimation and optimization in a teaching hospital in Ghana. Demographic and scanner output indices, including CT dose index (CTDI_vol) and dose length product for adult head, chest and abdominopelvic (ABP) CT examinations carried out at the hospital from 2018 to 2020, were retrieved from the picture archiving and communication system of the CT scanner machine. Other indices such as the antero-posterior diameter (D_AP), lateral diameter (D_L) and diagonal diameter (D_dia) of the patients' bodies were measured on the mid-slice axial image using a digital caliper. The effective diameter (D_eff) was then calculated as the square root of the product of theD_APandD_L. The SSDEs were calculated as the product of the CTDI_voland the size-specific conversion factors obtained from Report 204 of the American Association of Physicists in Medicine. Regression analyses were performed to find the relationship between SSDE and the various parameters to derive mathematical equations for the dose estimations. There were more female samples (n= 468, 56.3%) than male samples (n= 364, 43.7%) for each CT procedure. The SSDEs and size-specific diagnostic reference levels (SSDRLs) were: head (83.9 mGy; 86.9 mGy), chest (8.1 mGy; 8.7 mGy) and ABP (8.4 mGy; 9.2 mGy). The variations between CTDI_voland SSDEs for head (2.50%), chest (25.9%), and ABP (26.2%) showed an underestimation of radiation dose to patients, especially in chest and ABP examinations, if CTDI_volis used to report patient doses. The SSDEs of the chest and ABP CT examinations showed linear correlations with the CTDI_vol. The estimated values could be used to optimize radiation doses in the CT facility. The SSDE and SSDRLs for head, chest and ABP CT examinations have been developed at a teaching hospital in Ghana. The SSDEs of chest and ABP examinations showed linear correlations with the CTDI_voland hence can be calculated using the mathematically derived equations in the study.

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study

OBJECTIVES

To investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to that of other reconstruction algorithms in a phantom experiment and an abdominal clinical study.

METHODS

An elliptical phantom with five different iodine concentrations (1-12 mgI/mL) was imaged five times with fast-kilovoltage-switching DECT for three target volume CT dose indexes. All images were reconstructed using filtered back-projection, iterative reconstruction (two levels), and DLIR algorithms. Measured and nominal iodine concentrations were compared among the algorithms. Contrast-enhanced CT of the abdomen with the same scanner was acquired in clinical patients. In arterial and portal venous phase images, iodine concentration, image noise, and coefficients of variation for four locations were retrospectively compared among the algorithms. One-way repeated-measures analyses of variance were used to evaluate differences in the iodine concentrations, standard deviations, coefficients of variation, and percentages of error among the algorithms.

RESULTS

In the phantom study, the measured iodine concentrations were equivalent among the algorithms: within ± 8% of the nominal values, with root-mean-square deviations of 0.08-0.36 mgI/mL, regardless of radiation dose. In the clinical study (50 patients; 35 men; mean age, 68 ± 11 years), iodine concentrations were equivalent among the algorithms for each location (all p > .99). Image noise and coefficients of variation were lower with DLIR than with the other algorithms (all p < .01).

CONCLUSIONS

The DLIR algorithm reduced image noise and variability of iodine concentration values compared with other reconstruction algorithms in the fast-kilovoltage-switching dual-energy CT.

KEY POINTS

• In the phantom study, standard deviations and coefficients of variation in iodine quantification were lower on images with the deep learning image reconstruction algorithm than on those with other algorithms. • In the clinical study, iodine concentrations of measurement location in the upper abdomen were consistent across four reconstruction algorithms, while image noise and variability of iodine concentrations were lower on images with the deep learning image reconstruction algorithm.

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.

DIAGNOSTIC REFERENCE LEVELS OF RADIOGRAPHIC AND CT EXAMINATIONS IN SAUDI ARABIA: A SYSTEMATIC REVIEW

A systematic search was conducted to explore the literature on the existing diagnostic reference level (DRL) of radiographic and computed tomography (CT) examinations in Saudi Arabia. The search was performed using Web of Science, Scopus and EBSCO. The search identified 19 studies that reported DRL values for radiographic and CT examinations in Saudi Arabia. Six of those studies reported DRL values for projection radiography, and the remaining studies reported DRL values for CT examinations (n = 13). The entrance surface dose, volume CT dose index (CTDIvol) and dose-length product (DLP) were the most common methods used for establishing the DRLs. Variations were observed in the Saudi DRL values, and this is consistent with the DRL values reported in the literature. Educating and training radiographers to better understand dose minimizing techniques may result in lower patient doses and lower variances in DRL values.

ESTIMATION OF PEDIATRIC DOSE DESCRIPTORS ADAPTED TO INDIVIDUAL SPECIFIC SIZE FROM CT EXAMINATIONS

Clinical challenges in pediatrics dose estimation by the displayed computed tomography (CT) dose indices may lead to inaccuracy, and thus size-specific dose estimate (SSDE) is introduced for better-personalized dose estimation. This study aims to estimate pediatric dose adapted to specific size. This retrospective study involved pediatric population aged 0-12 y. SSDE was derived from scanner reported volume CT dose index (CTDIvol), based on individual effective diameter (Deff) with corresponding size correction factors. The correlations of Deff with other associated factors such as age, exposure setting, CTDIvol and SSDE were also studied. The average Deff of Malaysian pediatric was smaller than reference phantom size (confidence interval, CI = 0.28, mean = 14.79) and (CI = 0.51, mean = 16.33) for head and abdomen, respectively. These have led to underestimation of pediatric dose as SSDE was higher than displayed CTDIvol. The percentage differences were statistically significant (p < .001) ranged from 0 to 17% and 37 to 60% for head and abdominal CT, respectively. In conclusion, the clinical implementation of SSDE in pediatric CT imaging is highly relevant to reduce radiation risk.

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease

OBJECTIVES

To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).

METHODS

Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated.

RESULTS

The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01).

CONCLUSION

Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR.

KEY POINTS

• Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results. • HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer. • Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease.

Image Quality and Radiation Dose of Contrast-Enhanced Chest-CT Acquired on a Clinical Photon-Counting Detector CT vs. Second-Generation Dual-Source CT in an Oncologic Cohort: Preliminary Results

Our aim was to compare the image quality and patient dose of contrast-enhanced oncologic chest-CT of a first-generation photon-counting detector (PCD-CT) and a second-generation dual-source dual-energy CT (DSCT). For this reason, one hundred consecutive oncologic patients (63 male, 65 ± 11 years, BMI: 16−42 kg/m2) were prospectively enrolled and evaluated. Clinically indicated contrast-enhanced chest-CT were obtained with PCD-CT and compared to previously obtained chest-DSCT in the same individuals. The median time interval between the scans was three months. The same contrast media protocol was used for both scans. PCD-CT was performed in QuantumPlus mode (obtaining full spectral information) at 120 kVp. DSCT was performed using 100 kV for Tube A and 140 kV for Tube B. “T3D” PCD-CT images were evaluated, which emulate conventional 120 keV polychromatic images. For DSCT, the convolution algorithm was set at I31f with class 1 iterative reconstruction, whereas comparable Br40 kernel and iterative reconstruction strengths (Q1 and Q3) were applied for PCD-CT. Two radiologists assessed image quality using a five-point Likert scale and performed measurements of vessels and lung parenchyma for signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and in the case of pulmonary metastases tumor-to-lung parenchyma contrast ratio. PCD-CT CNRvessel was significantly higher than DSCT CNRvessel (all, p < 0.05). Readers rated image contrast of mediastinum, vessels, and lung parenchyma significantly higher in PCD-CT than DSCT images (p < 0.001). Q3 PCD-CT CNRlung_parenchyma was significantly higher than DSCT CNRlung_parenchyma and Q1 PCD-CT CNRlung_parenchyma (p < 0.01). The tumor-to-lung parenchyma contrast ratio was significantly higher on PCD-CT than DSCT images (0.08 ± 0.04 vs. 0.03 ± 0.02, p < 0.001). CTDI, DLP, SSDE mean values for PCD-CT and DSCT were 4.17 ± 1.29 mGy vs. 7.21 ± 0.49 mGy, 151.01 ± 48.56 mGy * cm vs. 288.64 ± 31.17 mGy * cm and 4.23 ± 0.97 vs. 7.48 ± 1.09, respectively. PCD-CT enables oncologic chest-CT with a significantly reduced dose while maintaining image quality similar to a second-generation DSCT for comparable protocol settings.

OPTIMIZATION OF LUNG CT PROTOCOL FOR THE DIAGNOSTIC EVALUATION OF COVID-19 LUNG DISEASE

This study intends to evaluate the different lung CT scan protocols used for the diagnostic evaluation of COVID-19-induced lung disease in Iranian imaging centers in terms of radiation dose and image quality. After data collecting, subjective image quality, radiation dose and objective image quality such as noise, SNR and CNR were assessed. Statistically significant differences in effective dose and image quality were evident among different lung CT protocols. Lowest and highest effective dose was1.31 ± 0.53 mSv related to a protocol with activated AEC (reference mAs = 20) and 6.15 ± 0.57 mSv related to a protocol with Fixed mAs (mAs = 100), respectively. A protocol with enabled tube current modulation with 70 mAs as a reference mAs, and protocol with 20 mAs and enabled AEC had the best and lowest image quality, respectively. To optimize the scan parameters, AEC must be used, and a range of tube currents (between 20 and 50 mAs) can produce acceptable images in terms of diagnostic quality and radiation dose for the diagnosis of COVID-19-induced lung disease.

Potential increase in radiation-induced DNA double-strand breaks with higher doses of iodine contrast during coronary CT angiography

PURPOSE

To investigate the contrast media iodine dose dependency of radiation-induced DNA double-strand breaks (DSBs) during a coronary computed tomography angiography (CCTA) scan.

METHODS

This prospective patient study was approved by the ethical committee. Between November 2018 and July 2019, 50 patients (31 males and 19 females, mean age 64 years) were included in the study, 45 CCTA and five noncontrast-enhanced (NCE) cardiac computed tomography (CT) patients. A single-heartbeat scan protocol with a patient-tailored contrast media injection protocol was used, administering a patient-specific iodine dose. DNA double-strand breaks were quantified using a γH2AX foci assay on peripheral blood lymphocytes. The net amount of γH2AX/cell was normalized to the individual patient CT dose by the size-specific dose estimate (SSDE). Correlation between the administered and blood-iodine dose and the SSDE normalized amount of DNA DSBs was investigated using a Pearson correlation test.

RESULTS

CCTA patients were scanned with a mean CTDIvol of 10.6 ± 5.6 mGy, corresponding to a mean SSDE of 11.3 ± 5.3 mGy while the NCE cardiac CT patients were scanned with a mean CTDIvol of 6.00 ± 1.8 mGy, corresponding to a mean SSDE of 6.6 ± 2.7 mGy. The administered iodine dose ranged from 16.5 to 34.0 gI in the CCTA patients, resulting in a blood-iodine dose range from 5.1 to 15.0 gI in the exposed blood volume. A significant linear relationship (r = 0.79, p-value < 0.001) was observed between the blood iodine dose and SSDE normalized radiation-induced DNA DSBs. A similar significant linear relationship (r = 0.62, p-value < 0.001) was observed between the administered iodine dose and SSDE normalized radiation-induced DNA DSBs.

CONCLUSIONS

This study shows that contrast media iodine dose increases the level of radiation-induced DNA DSBs in peripheral blood lymphocytes in a linear dose-dependent manner with CCTA. Importantly, the level of DNA DSBs can be reduced by lowering the administered iodine dose.

Prospective Study of Low- and Standard-dose Chest CT for Pulmonary Nodule Detection: A Comparison of Image Quality, Size Measurements and Radiation Exposure

OBJECTIVE

To comprehensively and accurately analyze the out-performance of low-dose chest CT (LDCT) vs. standard-dose CT (SDCT).

METHODS

The image quality, size measurements and radiation exposure for LDCT and SDCT protocols were evaluated. A total of 117 patients with extra-thoracic malignancies were prospectively enrolled for non-enhanced CT scanning using LDCT and SDCT protocols. Three experienced radiologists evaluated subjective image quality independently using a 5-point score system. Nodule detection efficiency was compared between LDCT and SDCT based on nodule characteristics (size and volume). Radiation metrics and organ doses were analyzed using Radimetrics.

RESULTS

The images acquired with the LDCT protocol yielded comparable quality to those acquired with the SDCT protocol. The sensitivity of LDCT for the detection of pulmonary nodules (n=650) was lower than that of SDCT (n=660). There was no significant difference in the diameter and volume of pulmonary nodules between LDCT and SDCT (for BMI <22 kg/m², 4.37 vs. 4.46 mm, and 43.66 vs. 46.36 mm³; for BMI ≥22 kg/m², 4.3 vs. 4.41 mm, and 41.66 vs. 44.86 mm³) (P>0.05). The individualized volume CT dose index (CTDI_vol), the size specific dose estimate and effective dose were significantly reduced in the LDCT group compared with the SDCT group (all P<0.0001). This was especially true for dose-sensitive organs such as the lung (for BMI <22 kg/m², 2.62 vs. 12.54 mSV, and for BMI ≥22 kg/m², 1.62 vs. 9.79 mSV) and the breast (for BMI <22 kg/m², 2.52 vs. 10.93 mSV, and for BMI ≥22 kg/m², 1.53 vs. 9.01 mSV) (P<0.0001).

CONCLUSION

These results suggest that with the increases in image noise, LDCT and SDCT exhibited a comparable image quality and sensitivity. The LDCT protocol for chest scans may reduce radiation exposure by about 80% compared to the SDCT protocol.

Size-specific dose estimates of adult, chest computed tomography examinations: Comparison of Chinese and updated 2017 American College of Radiology diagnostic reference levels based on the water-equivalent diameter

Rationale and objectives

This study aimed to compare the volume computed tomography dose index (CTDIvol), dose length product (DLP), and size-specific dose estimate (SSDE), with the China and updated 2017 American College of Radiology (ACR) diagnostic reference levels (DRLs) in chest CT examinations of adults based on the water-equivalent diameter (Dw).

Materials and methods

All chest CT examinations conducted without contrast administration from January 2020 to July 2020 were retrospectively included in this study. The Dw and SSDE of all examinations were calculated automatically by “teamplay”. The CTDIvol and DLP were displayed on the DICOM-structured dose report in the console based on a 32cm phantom.The differences in patient CTDIvol, DLP, and SSDE values between groups were examined by the one-way ANOVA. The differences in patient CTDIvol, DLP, and SSDE values between the updated 2017 ACR and the China DRLs were examined with one sample t-tests.

Results

In total 14666 chest examinations were conducted in our study. Patients were divided into four groups based on Dw:270 (1.84%) in 15–20 cm group, 10287 (70.14%) in the 21–25 cm group, 4097 (27.94%) in the 26–30 cm group, and 12 (0.08%) patients had sizes larger than 30 cm. CTDIvol, DLP, and SSDE increased as a function of Dw (p<0.05). CTDIvol was smaller than SSDE among groups (p<0.05). The mean CTDIvol and DLP values were lower than the 25^th, 50^th, and 75^th percentile of the China DRLs (p <0.05). The CTDIvol, DLP, and SSDE were lower than the 50^th and 75^th percentiles of the updated 2017 ACR DRLs (p <0.05) among groups.

Conclusions

SSDE takes into account the influence of the scanning parameters, patient size, and X-ray attenuation on the radiation dose, which can give a more realistic estimate of radiation exposure dose for patients undergoing CT examinations. Establishing hospital’s own DRL according to CTDIvol and SSDE is very important even though the radiation dose is lower than the national DRLs.

Comparison of Radiation Dose and Image Quality Between Split-Filter Twin Beam Dual-Energy Images and Single-Energy Images in Single-Source Contrast-Enhanced Chest Computed Tomography

OBJECTIVE

To compare image quality and radiation dose of split-filter TwinBeam dual-energy (SF-TBDE) with those of single-energy images (SECT) in the contrast-enhanced chest computed tomography (CT).

METHODS

Two hundred patients who underwent SF-TBDE (n = 100) and SECT (n = 100) contrast-enhanced chest scanning were retrospectively analyzed. The contrast-to-noise ratio (CNR) and figure of merit (FOM)-CNR of 5 structures (lung, aorta, pulmonary artery, thyroid, and erector spinae) were calculated and subjectively evaluated by 2 independent radiologists. Radiation dose was compared using volume CT dose index and size-specific dose estimate.

RESULTS

The CNR and FOM-CNR of lung and erector spinae in SF-TBDE were higher than those of SECT (P < 0.001). The differences in the subjective image quality between the 2 groups were not significant (P = 0.244). Volume CT dose index and size-specific dose estimate of SF-TBDE were lower than those of SECT (6.60 ± 1.56 vs 7.81 ± 3.02 mGy, P = 0.001; 9.25 ± 1.60 vs. 10.55 ± 3.54; P = 0.001).

CONCLUSIONS

The SF-TBDE CT can provide similar image quality at a lower radiation dose compared with SECT.

Unenhanced Dual-Layer Spectral-Detector CT for Characterizing Indeterminate Adrenal Lesions

Background Unenhanced dual-layer spectral-detector CT may facilitate adrenal lesion characterization; however, no studies have evaluated its incremental diagnostic yield for indeterminate lesions (unenhanced attenuation >10 HU) in comparison to that with conventional unenhanced CT. Purpose To determine whether spectral attenuation analysis improves characterization of lipid-poor adrenal adenomas from nonadenomas compared to that with mean attenuation and histogram analysis of conventional CT images. Materials and Methods This retrospective study included patients with indeterminate adrenal lesions who underwent unenhanced dual-layer spectral-detector CT between March 2018 and June 2020. Mean attenuation on conventional 120-kVp images (HU_conv), histogram-based percentage negative pixels (proportion of all pixels <0 HU) on conventional 120-kVp images, and mean attenuation on virtual monoenergetic images (VMIs) at 40-140 keV were measured for each lesion. The attenuation difference between virtual monoenergetic 140- and 40-keV images (ΔHU; ie, Hounsfield unit at 140 keV - Hounsfield unit at 40 keV) and ΔHU indexed with HU_conv (ΔHU index; ie, ΔHU/HU_conv × 100) were calculated. Conventional and virtual monoenergetic imaging parameters were compared between lipid-poor adenomas and nonadenomas by using the Mann-Whitney U test. Receiver operating characteristic analysis was performed to determine the sensitivity for attaining at least 95% specificity in characterizing adenomas from nonadenomas; sensitivity was compared by using the McNemar test. Results A total of 232 patients (mean age ± standard deviation, 67 years ± 11; 145 men) with 129 lipid-poor adenomas and 103 nonadenomas were evaluated. HU_conv and mean attenuation on VMIs at 40-140 keV were lower and the percentage negative pixels, ΔHU, and ΔHU index higher in lipid-poor adenomas than in nonadenomas (P < .001 for all). Attenuation differences between adenomas and nonadenomas on VMIs were maximal at 40 keV (23 HU at 40 keV vs 5 HU at 140 keV). The highest sensitivities for differentiating adenomas and nonadenomas were achieved for virtual monoenergetic ΔHU index (77% [99 of 129 adenomas]), attenuation on 40-keV images (71% [91 of 129 adenomas]), and ΔHU (67% [87 of 129 adenomas]) compared to HU_conv (35% [45 of 129 adenomas]) and percentage negative pixels (30% [39 of 129 adenomas]) (P < .001 for all; specificity, 95% [98 of 103 nonadenomas]). Conclusion Spectral attenuation analysis enabled differentiation of lipid-poor adenomas from nonadenomas with higher sensitivity than mean attenuation or histogram analysis of conventional CT images. © RSNA, 2021 Online supplemental material is available for this article.

Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction

PURPOSE

To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.

METHOD

Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent).

RESULTS

The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT).

CONCLUSIONS

Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.

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.

DEVELOPMENT OF A SPECIALISED TAPE MEASURE TO ESTIMATE THE SIZE-SPECIFIC DOSE ESTIMATE

The risk in computed tomography (CT) examinations is radiation exposure. We aimed to develop a specialised tape measure for determining the size-specific dose estimate (SSDE) for patients undergoing CT scans. The scanning parameters used were those of the abdominal protocol in our institute. With this method, the SSDE220 and standard deviations obtained from CT images for the liver, pelvic and lung areas, corresponded closely to the SSDEtape and standard deviations obtained using the tape measure. We thus devised a new idea that allows the estimation of the SSDE220 using a specialised tape measure before the CT examination, allowing for an informed explanation of the radiation dose to the patient. Although the tape measure developed in this study is specific to one particular CT instrument, the method could be adapted to a wide range of radiography applications.

Peripheral bronchial luminal conspicuity on dynamic-ventilation computed tomography: association with radiation doses and temporal resolution by using an ex vivo porcine lung phantom

BACKGROUND

It is still unclear which image reconstruction algorithm is appropriate for peripheral bronchial luminal conspicuity (PBLC) on dynamic-ventilation computed tomography (DVCT).

PURPOSE

To assess the influence of radiation doses and temporal resolution (TR) on the association between movement velocity (MV) and PBLC on DVCT.

MATERIAL AND METHODS

An ex vivo porcine lung phantom with simulated respiratory movement was scanned by 320-row CT at 240 mA and 10 mA. Peak and dip CT density and luminal area adjusted by values at end-inspiration (CTD_peak and CTD_dip, luminal area ratio [LAR]) for PBLC and MVs were measured and visual scores (VS) were obtained at 12 measurement points on 13 frame images obtained at half and full reconstructions (TR 340 and 190 ms) during expiration. Size-specific dose estimate (SSDE) was applied to presume radiation dose. VS, CTD_peak, CTD_dip, LAR, and their cross-correlation coefficients with MV (CCC) were compared among four methods with combinations of two reconstruction algorithms and two doses.

RESULTS

The dose at 10 mA was presumed as 26 mA by SSDE for standard proportion adults. VS, CTD_dip, CTD_peak, and LAR with half reconstruction at 10 mA (2.52 ± 0.59, 1.016 ± 0.221, 0.948 ± 0.103, and 0.990 ± 0.527) were similar to those at 240 mA except for VS, and different from those with full reconstruction at both doses (2.24 ± 0.85, 0.830 ± 0.209, 0.986 ± 0.065, and 1.012 ± 0.438 at 240 mA) (P < 0.05). CCC for CTD_dip with half reconstruction (-0.024 ± 0.552) at 10 mA was higher compared with full reconstruction (-0.503 ± 0.291) (P < 0.05).

CONCLUSION

PBLC with half reconstruction at 10 mA was comparable to that at 240 mA and better than those with full reconstruction on DVCT.

Automated size-specific dose estimates using deep learning image processing

An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose management can operate on incomplete DICOM header metadata by retrieving necessary information from the dose report image by using optical character recognition. For the determination of the patient's effective diameter and water equivalent diameter, a convolutional neural network is employed for the semantic segmentation of the body area in axial CT slices. Validation experiments for the assessment of the SSDE determination and subsequent stages of our methodology involved a total of 335 CT series (60 352 images) from both public databases and our clinical data. We obtained the mean body area segmentation accuracy of 0.9955 and Jaccard index of 0.9752, yielding a slice-wise mean absolute error of effective diameter below 2 mm and water equivalent diameter at 1 mm, both below 1%. Three modes of the SSDE determination approach were investigated and compared to the results provided by the commercial system GE DoseWatch in three different body region categories: head, chest, and abdomen. Statistical analysis was employed to point out some significant remarks, especially in the head category.

Independent role of low-density lipoprotein cholesterol in subclinical coronary atherosclerosis in the absence of traditional cardiovascular risk factors

AIMS

Individuals without traditional cardiovascular risk factors (CVRFs) still experience adverse events in clinical practice. This study evaluated the predictors of subclinical coronary atherosclerosis in individuals without traditional CVRFs.

METHODS AND RESULTS

A total of 1250 (52.8 ± 6.5 years, 46.9% male) asymptomatic individuals without CVRFs who underwent coronary computed tomographic angiography for a general health examination were analysed. The following were considered as traditional CVRFs: systolic/diastolic blood pressure ≥140/90 mmHg; fasting glucose ≥126 mg/dL; total cholesterol ≥240 mg/dL; low-density lipoprotein cholesterol (LDL-C) ≥160 mg/dL; high-density lipoprotein cholesterol <40 mg/dL; body mass index ≥25.0 kg/m2; current smoking status; and previous medical history of hypertension, diabetes, and dyslipidaemia. Subclinical atherosclerosis, defined as the presence of any coronary plaque, was present in 20.6% cases; the incidences of non-calcified, calcified, and mixed plaque were 9.6%, 12.6%, and 2.6%, respectively. Multivariate regression analysis showed that LDL-C level [odds ratio (OR): 1.008; 95% confidence interval (CI): 1.001-1.015], together with age (OR: 1.101; 95% CI: 1.075-1.128) and male sex (OR: 5.574; 95% CI: 3.310-9.388), was associated with the presence of subclinical atherosclerosis (All P < 0.05). LDL-C level was significantly associated with an increased risk of calcified plaques rather than non-calcified or mixed plaques.

CONCLUSION

LDL-C, even at levels currently considered within normal range, is independently associated with the presence of subclinical coronary atherosclerosis in individuals without traditional CVRFs. Our results suggest that a stricter control of LDL-C levels may be necessary for primary prevention in individuals who are conventionally considered healthy.

Deep learning reconstruction of equilibrium phase CT images in obese patients

PURPOSE

To compare abdominal equilibrium phase (EP) CT images of obese and non-obese patients to identify the reconstruction method that preserves the diagnostic value of images obtained in obese patients.

METHODS

We compared EP images of 50 obese patients whose body mass index (BMI) exceeded 25 (group 1) with EP images of 50 non-obese patients (BMI < 25, group 2). Group 1 images were subjected to deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), and model-based IR (MBIR), group 2 images to hybrid-IR; group 2 hybrid-IR images served as the reference standard. A 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 confidence scale ranging from 1 (unacceptable) to 5 (excellent). Non-inferiority and potential superiority were assessed.

RESULTS

With respect to the image noise, group 1 DLR- were superior to group 2 hybrid-IR images; group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images. The quality scores of only DLR images in group 1 were superior to hybrid-IR images of group 2 while the quality scores of group 1 hybrid-IR- and MBIR images were neither superior nor non-inferior to group 2 hybrid-IR images.

CONCLUSIONS

DLR preserved the quality of EP images obtained in obese patients.

Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography

PURPOSE

Deep learning reconstruction (DLR) introduces deep convolutional neural networks into the reconstruction flow. We examined the clinical applicability of drip-infusion cholangiography (DIC) acquired on an ultra-high-resolution CT (U-HRCT) scanner reconstructed with DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).

METHODS

This retrospective, single-institution study included 30 patients seen between January 2018 and November 2019. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) in the common bile duct. The overall visual image quality of the bile duct on thick-slab maximum intensity projections was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (not delineated) to 5 (clearly delineated). The difference among hybrid-IR, MBIR, and DLR images was compared.

RESULTS

The image noise was significantly lower on DLR than hybrid-IR and MBIR images and the CNR and the overall visual image quality of the bile duct were significantly higher on DLR than on hybrid-IR and MBIR images (all: p < 0.001).

CONCLUSION

DLR resulted in significant quantitative and qualitative improvement of DIC acquired with U-HRCT.

Radiation doses in CT examinations from the West China Hospital, Sichuan University and setting local diagnostic references levels

BACKGROUND

Our study aims to summarize the data of radiation doses collected from consecutive CT examinations by using the Radiometrics software and contributing to the establishment of the region's diagnostic reference levels (DRLs).

METHODS

The radiation doses in 158,463 CT examinations performed on 106,275 adults between April 2017 and April 2019 were retrospectively analyzed. The median value and interquartile range (IQR) of volumetric CT dose index (CTDIvol), dose-length product (DLP), effective dose (ED), and size-specific dose estimate (SSDE) were calculated according to the scanning region.

RESULTS

The median CTDIvol (mGy) for each scanning region was 42.3 (head), 6.2 (chest), and 9.0 (abdomen). The median DLPs (mGy.cm) for single-phase, multi-phase, and all examinations were as follows: 607, 794, and 641 for the head; 220, 393, and 237 for the chest; 298, 1,141, and 570 for the abdomen. The median EDs (mSv) for single-phase, multi-phase, and all examinations are as follows: 1.6, 2.6, and 1.8 for the head; 5.1, 8.1, and 5.3 for the chest; 5.8, 20.3, and 10.4 for the abdomen.

CONCLUSIONS

Our study's results could provide a basis for the evaluation of CT scanning radiation dosage and supply evidence for the establishment of local DRLs in China's Sichuan Province.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

OBJECTIVE

Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).

MATERIALS AND METHODS

This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.

RESULTS

Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).

CONCLUSION

DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Patient size matters: Effect of tube current modulation on size-specific dose estimates (SSDE) and image quality in low-dose lung cancer screening CT

Purpose

We compare the effect of tube current modulation (TCM) and fixed tube current (FTC) on size‐specific dose estimates (SSDE) and image quality in lung cancer screening with low‐dose CT (LDCT) for patients of all sizes.

Methods

Initially, 107 lung screening examinations were performed using FTC, which satisfied the Centers for Medicare & Medicaid Services' volumetric CT dose index (CTDI_vol) limit of 3.0 mGy for standard‐sized patients. Following protocol modification, 287 examinations were performed using TCM. Patient size and examination parameters were collected and water‐equivalent diameter (D_w) and SSDE were determined for each patient. Regression models were used to correlate CTDI_vol and SSDE with D_w. Objective and subjective image quality were measured in 20 patients who had consecutive annual screenings with both FTC and TCM.

Results

CTDI_vol was 2.3 mGy for all FTC scans and increased exponentially with D_w (range = 0.96–4.50 mGy, R² = 0.73) for TCM scans. As patient D_w increased, SSDE decreased for FTC examinations (R² = 1) and increased for TCM examinations (R² = 0.54). Image quality measurements were superior with FTC for smaller sized patients and with TCM for larger sized patients (R² > 0.5, P < 0.005). Radiologist graded all images acceptable for diagnostic evaluation of lung cancer screening.

Conclusion

Although FTC protocol offered a consistently low CTDI_vol for all patients, it yielded unnecessarily high SSDE for small patients and increased image noise for large patients. Lung cancer screening with LDCT using TCM produces radiation doses that are appropriately reduced for small patients and increased for large patients with diagnostic image quality for all patients.

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging

PURPOSE

To develop size-specific institutional diagnostic reference levels (DRLs) for computed tomography (CT) protocols used in neck CT imaging (cervical spine CT, cervical CT angiography (CTA) and cervical staging CT) and to compare institutional to national DRLs.

MATERIALS AND METHODS

Cervical CT examinations (spine, n = 609; CTA, n = 505 and staging CT, n = 184) performed between 01/2016 and 06/2017 were included in this retrospective study. For each region and examination, the volumetric CT dose index (CTDI_vol) and dose-length product (DLP) were determined and binned into size bins according to patient water-equivalent diameter (d_w). Linear regression analysis was performed to calculate size-specific institutional DRLs for CTDI_vol and DLP, applying the 75th percentile as the upper limit for institutional DRLs. The mean institutional CTDI_vol and DLP were compared to national DRLs (CTDI_vol 20 mGy for cervical spine CT (DLP 300 mGycm) and cervical CTA (DLP 600 mGycm), and CTDI_vol 15 mGy for cervical staging CT (DLP 330 mGycm)).

RESULTS

The mean CTDI_vol and DLP (±standard deviation) were 15.2 ± 4.1 mGy and 181.5 ± 88.3 mGycm for cervical spine CT; 8.1 ± 4.3 mGy and 280.2 ± 164.3 mGycm for cervical CTA; 8.6 ± 1.9 mGy and 162.8 ± 85.0 mGycm for cervical staging CT. For all CT protocols, there was a linear increase in CTDI_vol and DLP with increasing d_w. For the CTDI_vol, size-specific institutional DRLs increased with d_w from 14 to 29 mGy for cervical spine CT, from 5 to 17 mGy for cervical CTA and from 8 to 13 mGy for cervical staging CT. For the DLP, size-specific institutional DRLs increased with d_w from 130 to 510 mGycm for cervical spine CT, from 140 to 640 mGycm for cervical CTA and from 140 to 320 mGycm for cervical staging CT. Institutional DRLs were lower than national DRLs by 81% and 67% for cervical spine CT (d_w = 17.8 cm), 43% and 51% for cervical CTA (d_w = 19.5 cm) and 59% and 53% for cervical staging CT (d_w = 18.8 cm) for CTDI_vol and DLP, respectively.

CONCLUSION

Size-specific institutional DRLs were generated for neck CT examinations. The mean institutional CTDI_vol and DLP values were well below national DRLs.

Size specific dose estimate (SSDE) for estimating patient dose from CT used in myocardial perfusion SPECT/CT

Objectives

Size specific dose estimate (SSDE) is a new parameter that includes patient size factor in its calculation. Recent studies have produced mixed results on the utility of SSDE, especially when automatic exposure control (AEC) was used. The objective of the study was to find out if there is a relationship between patient size and each of the parameters, SSDE and CTDIvol, when AEC is used.

Methods

CT data of consecutively selected 111 patients were included for analysis. CTDIvol values of the CT scans were extracted for each patient. Effective diameter of each patient was calculated as geometric mean of anteroposterior and lateral diameters measured on axial CT images. Corresponding conversion factors for effective diameters were obtained from American Association of Physicists in Medicine (AAPM) report 204. SSDE was obtained as the product of CTDIvol and conversion factor values. Linear regression model was used to evaluate the relationship between patient size and the parameters SSDE and CTDIvol.

Results

Mean weight was 62 (11.5) and range was 34 - 103 kg. Median CTDIvol (mGy) on AEC mode was 7.27(IQ range 7.27, 7.65) and mean effective diameter was 26.2 cm (2.4). Mean SSDE (mGy) was 10.6 (0.84). Good positive correlation was obtained between CTDIvol and effective diameter (r=0.536; p<0.0005). Strong inverse correlation was noted between SSDE and effective diameter (r=-0.777; p<0.0005). Linear regression model for establishing relationship between CTDIvol and effective diameter showed slope of 0.314mGy/cm (R=0.561; R2=0.314; P<0.0005) whereas between effective diameter and SSDE slope was -0.23mGy/cm (R=0.676; R2=0.457; P< 0.0005).

Conclusion

The study shows that CTDIvol and SSDE vary but divergently, with patient size. SSDE is a better estimate of patient radiation dose from CT of MPI SPECT/CT than CTDIvol in systems that use automated exposure control.

Triglyceride glucose index is a useful marker for predicting subclinical coronary artery disease in the absence of traditional risk factors

BACKGROUND

Atherosclerotic cardiovascular (CV) events commonly occur in individuals with a low CV risk burden. This study evaluated the ability of the triglyceride glucose (TyG) index to predict subclinical coronary artery disease (CAD) in asymptomatic subjects without traditional CV risk factors (CVRFs).

METHODS

This retrospective, cross-sectional, and observational study evaluated the association of TyG index with CAD in 1250 (52.8 ± 6.5 years, 46.9% male) asymptomatic individuals without traditional CVRFs (defined as systolic/diastolic blood pressure ≥ 140/90 mmHg; fasting glucose ≥126 mg/dL; total cholesterol ≥240 mg/dL; low-density lipoprotein cholesterol ≥160 mg/dL; high-density lipoprotein cholesterol < 40 mg/dL; body mass index ≥25.0 kg/m²; current smoking; and previous medical history of hypertension, diabetes, or dyslipidemia). CAD was defined as the presence of any coronary plaque on coronary computed tomographic angiography. The participants were divided into three groups based on TyG index tertiles.

RESULTS

The prevalence of CAD increased with elevating TyG index tertiles (group I: 14.8% vs. group II: 19.3% vs. group III: 27.6%; P < 0.001). Multivariate logistic regression models showed that TyG index was associated with an increased risk of CAD (odds ratio [OR] 1.473, 95% confidence interval [CI] 1.026-2.166); especially non-calcified (OR 1.581, 95% CI 1.002-2.493) and mixed plaques (OR 2.419, 95% CI 1.051-5.569) (all P < 0.05). The optimal TyG index cut-off for predicting CAD was 8.44 (sensitivity 47.9%; specificity 68.5%; area under the curve 0.600; P < 0.001). The predictive value of this cut-off improved after considering the non-modifiable factors of old age and male sex.

CONCLUSIONS

TyG index is an independent marker for predicting subclinical CAD in individuals conventionally considered healthy.

Size-Specific Dose Estimates Based on Water-Equivalent Diameter and Effective Diameter in Computed Tomography Coronary Angiography

BACKGROUND To determine the difference in size-specific dose estimates (SSDEs), separately based on effective diameter (deff) and water equivalent diameter (dw) of the central slice of the scan range in computed tomography coronary angiography (CTCA). MATERIAL AND METHODS There were 134 patients who underwent CTCA examination, were electronically retrieved. SSDEs (SSDEdeff and SSDEdw) were calculated using 2 approaches: deff and dw. The median SSDEs and mean absolute relative difference of SSDEs were calculated. Linear regression model was used to assess the absolute relative difference of SSDEs based on the ratio of deff to dw. RESULTS The median values of SSDEdeff and SSDEdw were 18.26 mGy and 20.56 mGy, respectively (P<0.01). The former was about 10.08% smaller than the latter. The mean absolute relative difference of SSDEs was 10.48%, ranging from 0.33% to 24.16%. A considerably positive correlation was found between the absolute relative difference of SSDEs and the ratio of deff to dw (R²=0.9561, r=0.979, P<0.01). CONCLUSIONS The value of SSDEdeff was smaller by an average of about 10.08% than SSDEdw in CTCA, and the absolute relative difference increased linearly with the ratio of effective diameter to water equivalent diameter.

A SIMPLIFIED METHOD FOR THE WATER-EQUIVALENT DIAMETER CALCULATION TO ESTIMATE PATIENT DOSE IN CT EXAMINATIONS

We proposed and evaluated a water-equivalent diameter calculation without using a region of interest (ROI), (Dw,t) and compared it with the results of using a ROI fitted to the patient border (Dw,f). Evaluations were carried out on thoracic and head CT images. We found that the difference between Dw,t and Dw,f was within 5% for all images in the head region, and most images were within 5% (27 of the 30 patients, 90%) in the thoracic region. We also proposed a method to automatically detect and eliminate the patient table (or head support) from images and evaluated the water-equivalent diameter values after the table had been removed (Dw,nt). This method was able to recognize and remove the patient table from all images used. By removing the table, the water-equivalent diameter (Dw,nt) became more accurate and the difference from Dw,f was within 5% for all images (head and thoracic images).

Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.

Radiation dose and image quality of high-pitch emergency abdominal CT in obese patients using third-generation dual-source CT (DSCT)

In this third-generation dual-source CT (DSCT) study, we retrospectively investigated radiation dose and image quality of portal-venous high-pitch emergency CT in 60 patients (28 female, mean age 56 years) with a body mass index (BMI) ≥ 30 kg/m2. Patients were dichotomized in groups A (median BMI 31.5 kg/m2; n = 33) and B (36.8 kg/m2; n = 27). Volumetric CT dose index (CTDIvol), size-specific dose estimate (SSDE), dose length product (DLP) and effective dose (ED) were assessed. Contrast-to-noise ratio (CNR) and dose-independent figure-of-merit (FOM) CNR were calculated. Subjective image quality was assessed using a five-point scale. Mean values of CTDIvol, SSDE as well as normalized DLP and ED were 7.6 ± 1.8 mGy, 8.0 ± 1.8 mGy, 304 ± 74 mGy * cm and 5.2 ± 1.3 mSv for group A, and 12.6 ± 3.7 mGy, 11.0 ± 2.6 mGy, 521 ± 157 mGy * cm and 8.9 ± 2.7 mSv for group B (p < 0.001). CNR of the liver and spleen as well as each calculated FOM CNR were significantly higher in group A (p < 0.001). Subjective image quality was good in both groups. In conclusion, third-generation abdominal high-pitch emergency DSCT yields good image quality in obese patients. Radiation dose increases in patients with a BMI > 36.8 kg/m2.

High HDL-C levels reduce the risk of obstructive coronary artery disease in asymptomatic diabetics who achieved optimal glycemic control

The benefit of a high level of high-density lipoprotein cholesterol (HDL-C) against coronary atherosclerosis risk after achieving optimal glycemic control (OGC) in diabetics remains uncertain. We aimed to evaluate the association between HDL-C and obstructive coronary artery disease (CAD) according to OGC status in diabetics. We analyzed 1,114 asymptomatic diabetics who underwent coronary computed tomographic angiography in a health examination. OGC was defined as hemoglobin A1C <7.0%. Obstructive CAD was defined as the presence of plaques with ≥50% stenosis. Patients with a high HDL-C level (≥40 mg/dL and ≥50 mg/dL in males and females, respectively) showed a lower prevalence of obstructive CAD than those with a low HDL-C level in the OGC group (8.9% vs. 14.4%; p = 0.046), but not in the non-OGC group (22.3% vs. 23.2%, p = 0.850). Multiple logistic regression models showed that the risk for obstructive CAD was lower in patients with a high HDL-C level than in those with a low HDL-C level in the OGC group (odds ratio: 0.584, 95% confidence interval: 0.343-0.995; p = 0.048), but not in the non-OGC group. In conclusion, it may be necessary to maintain a high HDL-C level to reduce the risk of obstructive CAD in asymptomatic diabetics after OGC is achieved.

Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases

Purpose

To evaluate the effect of a deep learning-based reconstruction (DLR) method on the conspicuity of hypovascular hepatic metastases on abdominal CT images.

Materials and Methods

This retrospective study with institutional review board approval included 58 patients with hypovascular hepatic metastases. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and the contrast-to-noise ratio (CNR). CNR was calculated as region of interest ([ROI]_L - ROI_T)/N, where ROI_L is the mean liver parenchyma attenuation, ROI_T, the mean tumor attenuation, and N, the noise. Two other radiologists graded the conspicuity of the liver lesion on a five-point scale where 1 is unidentifiable and 5 is detected without diagnostic compromise. Only the smallest liver lesion in each patient, classified as smaller or larger than 10 mm, was evaluated. The difference between hybrid iterative reconstruction (IR) and DLR images was determined by using a two-sided Wilcoxon signed-rank test.

Results

The image noise was significantly lower, and the CNR was significantly higher on DLR images than hybrid IR images (median image noise: 19.2 vs 12.8 HU, P < .001; median CNR: tumors < 10 mm: 1.9 vs 2.5; tumors > 10 mm: 1.7 vs 2.2, both P < .001). The scores for liver lesions were significantly higher for DLR images than hybrid IR images (P < .01 for both in tumors smaller or larger than 10 mm).

Conclusion

DLR improved the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.© RSNA, 2019Supplemental material is available for this article.