Which should be the routine cross-sectional reconstruction mode in spectral CT imaging: monochromatic or polychromatic?

Utility of dual energy CT angiography in the evaluation of acute non-variceal gastrointestinal hemorrhage: comparison with digital subtraction angiography

PURPOSE

To evaluate the utility of dual energy CT angiography (DECTA) in acute non-variceal gastrointestinal hemorrhage (ANVGIH) compared to digital subtraction angiography (DSA) as gold standard.

MATERIALS AND METHODS

111 Patients (mean age: 39.2 years; 94 males) of ANVGIH who underwent both DECTA and DSA between January 2016 and September 2021 were included. Virtual monochromatic (VM) images at 10 keV increments from 40 to 70 keV and blended (120kVp equivalent) images of arterial phase of DECTA were evaluated independently by two readers blinded to DSA information. Quantitative analysis included measurement of attenuation in the major arteries (abdominal aorta, celiac artery, superior mesenteric artery), suspected vascular lesion, and lesion feeding artery to calculate contrast-to-noise ratios (CNRs) and signal-to-noise ratios (SNRs). Qualitative analysis assessed the image quality of each data set using a 3-point Likert scale. Findings on DSA were evaluated by a third reader and both DECTA and DSA were compared.

RESULTS

On linear blended images, vascular lesion was identified by reader 1 in 88 (79.3%) and by reader 2 in 87 (78.4%) patients and DSA showed lesion in 92 (82.9%) patients. The sensitivity and specificity of blended images and VM images of DECTA for lesion detection were not significantly different from each other. The CNR and SNR of arteries, vascular lesion and feeding artery were significantly higher at 70 keV (p < 0.005) compared to blended and other VM images. Although subjective scores for image quality were higher for 60 keV images by both readers, the difference was not statistically significant (p = 0.3). The interobserver agreement was mostly good.

CONCLUSION

In the assessment of ANVGIH, the 60 keV and 70 keV VM images improved the image quality and contrast, respectively, but there was no increase in diagnostic accuracy of VM image datasets compared to linearly blended images. Hence, the diagnostic utility of DECTA in ANVGIH is still uncertain.

Deep learning-based reconstruction of virtual monoenergetic images of kVp-switching dual energy CT for evaluation of hypervascular liver lesions: Comparison with standard reconstruction technique

OBJECTIVE

To investigate clinical applicability of deep learning(DL)-based reconstruction of virtual monoenergetic images(VMIs) of arterial phase liver CT obtained by rapid kVp-switching dual-energy CT for evaluation of hypervascular liver lesions.

MATERIALS AND METHODS

We retrospectively included 109 patients who had available late arterial phase liver CT images of the liver obtained with a rapid switching kVp DECT scanner for suspicious intra-abdominal malignancies. Two VMIs of 70 keV and 40 keV were reconstructed using adaptive statistical iterative reconstruction (ASiR-V) for arterial phase scans. VMIs at 40 keV were additionally reconstructed with a vendor-agnostic DL-based reconstruction technique (ClariCT.AI, ClariPi, DL 40 keV). Qualitative, quantitative image quality and subjective diagnostic acceptability were compared according to reconstruction techniques.

RESULTS

In qualitative analysis, DL 40 keV images showed less image noise (4.55 vs 3.11 vs 3.95, p < 0.001), better image sharpness (4.75 vs 4.16 vs 4.3, p < 0.001), better image contrast (4.98 vs 4.72 vs 4.19, p < 0.017), better lesion conspicuity (4.61 vs 4.23 vs 3.4, p < 0.001) and diagnostic acceptability (4.59 vs 3.88 vs 4.09, p < 0.001) compared with ASiR-V 40 keV or 70 keV image sets. In quantitative analysis, DL 40 keV significantly reduced image noise relative to ASiR-V 40 keV images (49.9%, p < 0.001) and ASiR-V 70 keV images (85.2%, p = 0.012). DL 40 keV images showed significantly higher CNR_{lesion to the liver} and SNR_liver than ASiR-V 40 keV image and 70 keV images (p < 0.001).

CONCLUSION

DL-based reconstruction of 40 keV images using vendor-agnostic software showed greater noise reduction, better lesion conspicuity, image contrast, image sharpness, and higher overall image diagnostic acceptability than ASiR for 40 keV or 70 keV images in patients with hypervascular liver lesions.

Optimal Conspicuity of Liver Metastases in Virtual Monochromatic Imaging Reconstructions on a Novel Photon-Counting Detector CT—Effect of keV Settings and BMI

In dual-energy CT datasets, the conspicuity of liver metastases can be enhanced by virtual monoenergetic imaging (VMI) reconstructions at low keV levels. Our study investigated whether this effect can be reproduced in photon-counting detector CT (PCD-CT) datasets. We analyzed 100 patients with liver metastases who had undergone contrast-enhanced CT of the abdomen on a PCD-CT (n = 50) or energy-integrating detector CT (EID-CT, single-energy mode, n = 50). PCD-VMI-reconstructions were performed at various keV levels. Identical regions of interest were positioned in metastases, normal liver, and other defined locations assessing image noise, tumor-to-liver ratio (TLR), and contrast-to-noise ratio (CNR). Patients were compared inter-individually. Subgroup analyses were performed according to BMI. On the PCD-CT, noise and CNR peaked at the low end of the keV spectrum. In comparison with the EID-CT, PCD-VMI-reconstructions exhibited lower image noise (at 70 keV) but higher CNR (for ≤70 keV), despite similar CTDIs. Comparing high- and low-BMI patients, CTDI-upregulation was more modest for the PCD-CT but still resulted in similar noise levels and preserved CNR, unlike the EID-CT. In conclusion, PCD-CT VMIs in oncologic patients demonstrated reduced image noise–compared to a standard EID-CT–and improved conspicuity of hypovascularized liver metastases at low keV values. Patients with higher BMIs especially benefited from constant image noise and preservation of lesion conspicuity, despite a more moderate upregulation of CTDI.

Dual-energy spectral CT imaging of pulmonary embolism with Mycoplasma pneumoniae pneumonia in children

BACKGROUND

Pulmonary embolism (PE) associated with Mycoplasma pneumoniae pneumonia (MPP) in children has already attracted more attention. CT pulmonary angiography (CTPA) has been the preferred method for diagnosing PE, but it has some limitations, especially for children. Dual-energy spectral CT has been used in diagnosing PE in adults.

PURPOSE

To evaluate the application of dual-energy spectral CT in diagnosing PE in children with MPP.

MATERIALS AND METHODS

Eighty-three children with MPP and highly suspected PE, underwent CTPA with spectral imaging mode, 25 children were diagnosis with PE. Noise, clot-to-artery contrast-to-noise ratio, image quality and diagnosis confidence were calculated and assessed on nine monochromatic image sets (40 to 80 keV). CTPA images were observed for the presence, localization and embolic degrees of PE. Clots were divided into intra- and extra-consolidation clots. For extra-consolidation clots, iodine concentration (IC) of perfusion defects and normal lung, perfusion defects of four children before and after the treatment were measured and compared. For intra-consolidation clots, IC of consolidation areas with clots and consolidation areas without clot were measured and compared.

RESULTS

The optimal energy level for detecting PE in children was 55 keV. 116 clots (29 extra-consolidations) were found, IC of defect regions was 0.69 ± 0.28 mg/mL (extra-consolidations) and 0.90 ± 0.23 mg/mL (intra-consolidations), both significantly lower than the 2.76 ± 0.45 mg/mL in normal lungs and 10.25 ± 1.76 mg/mL in consolidations without clots (P < 0.001). Significant difference was found in the presence or absence of perfusion defects between occlusive clots and nonocclusive clots (P < 0.001). IC of the perfusion defects significantly increased after treatment (P < 0.001).

CONCLUSIONS

In dual-energy spectral CTPA, 55 keV images optimize PE detection for children, and MD images quantify pulmonary blood flow of PE, and may help to detect small clots and quantify embolic degrees.

Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions

BACKGROUND

Multiparametric dual-energy computed tomography (mpDECT) is widely used to differentiate various kinds of tumors; however, the data regarding its diagnostic performance with machine learning to diagnose breast tumors is limited. We evaluated univariate analysis and machine learning performance with mpDECT to distinguish between benign and malignant breast lesions.

METHODS

In total, 172 patients with 214 breast lesions (55 benign and 159 malignant) who underwent preoperative dual-phase contrast-enhanced DECT were included in this retrospective study. Twelve quantitative features were extracted for each lesion, including CT attenuation (precontrast, arterial, and venous phases), the arterial-venous phase difference in normalized effective atomic number (nZ_eff), normalized iodine concentration (NIC), and slope of the spectral Hounsfield unit (HU) curve (λ_Hu). Predictive models were developed using univariate analysis and eight machine learning methods [logistic regression, extreme gradient boosting (XGBoost), stochastic gradient descent (SGD), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), random forest (RF), decision tree, and linear support vector machine (SVM)]. Classification performances were assessed based on the area under the receiver operating characteristic curve (AUROC). The best performances of the conventional univariate analysis and machine learning methods were compared using the Delong test.

RESULTS

The univariate analysis showed that the venous phase λ_Hu had the highest AUROC (0.88). Machine learning with mpDECT achieved an excellent and stable diagnostic performance, as shown by the mean classification performances in the training dataset (AUROC, 0.88-0.99) and testing (AUROC, 0.83-0.96) datasets. The performance of the AdaBoost model based on mpDECT was more stable than the other machine learning models and superior to the univariate analysis (AUROC, 0.96 vs. 0.88; P<0.001).

CONCLUSIONS

The performance of the AdaBoost classifier based on mpDECT data achieved the highest mean accuracy compared to the other machine learning models and univariate analysis in differentiating between benign and malignant breast lesions.

Comparison of low kVp CT and dual-energy CT for the evaluation of hypervascular hepatocellular carcinoma

PURPOSE

To compare lesion conspicuity and image quality of arterial phase images obtained from low kVp (90-kVp) and dual-energy (DE) scans for the evaluation of hypervascular hepatocellular carcinoma (HCC).

METHODS

This retrospective study included 229 patients with HCC who underwent either 90 kVp (n = 106) or DE scan (80- and 150-kVp with a tin filter) (n = 123) during the arterial phase. DE scans were reconstructed into a linearly blended image with a mixed ratio of 0.6 (60% 80kVp and 40% 150 kVp) and post-processed for 40 keV and 50 keV images. The contrast-to-noise ratio (CNR) of HCC to the liver and image noise was measured. Lesion conspicuity, liver parenchymal image quality, and overall image preference were assessed qualitatively by three independent radiologists.

RESULTS

DE 40 keV images had the highest CNR of HCC, and DE blended images had the lowest image noise among four image sets (p = 0.01 and p < 0.001, respectively). There was no significant difference in mean volume CT dose index and dose-length product between DE and low kVp scan (ps > 0.05). For qualitative analyses, DE blended images had the highest scores for image quality and overall image preference (ps < 0.001).

CONCLUSION

At an equal radiation dose, DE 40 keV showed higher CNR of HCC and DE blended image showed higher image quality and image preference compared with low kVp CT.

Development of a dual-energy spectral CT based nomogram for the preoperative discrimination of mutated and wild-type KRAS in patients with colorectal cancer

PURPOSE

To develop a dual-energy spectral CT (DESCT) nomogram for the preoperative identification of KRAS mutation in patients with colorectal cancer (CRC).

METHOD

One hundred and twenty-four patients who underwent energy spectrum CT pre-operatively were recruited and split into mutated KRAS group (n = 50) and wild-type KRAS group (n = 74). DESCT parameters, including monochromatic CT value, iodine concentration, water concentration, and effective atomic number were measured independently by two reviewers in the arterial, venous, and delayed phases. Normalized iodine concentration (NIC) and slope k of the spectral HU curve were calculated. Evaluate other imaging features such as ATL/LTL ratio, tumor gross pattern, pericolorectal fat invasion (PFI) was also performed by these reviewers. Independent predictors for KRAS mutation were screened out using logistic regression, and these predictors were presented as a nomogram. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram.

RESULTS

The slope k in the arterial phase, effective atomic number in the arterial phase, NIC in the venous phase, ATL/LTL ratio and PFI were significant independent predictors for KRAS mutation. Based on these independent predictors, a quantitative nomogram was developed to predict individual KRAS mutation probability. The nomogram had excellent performance with an AUC of 0.848 and excellent calibration. DCA showed that our nomogram has outstanding clinical utility.

CONCLUSIONS

This study demonstrates that a DESCT based nomogram has potential value for individual preoperative identification of KRAS mutation in CRC patients.

Value of virtual monochromatic spectral image of dual-layer spectral detector CT with noise reduction algorithm for image quality improvement in obese simulated body phantom

BACKGROUND

Dual-layer spectral detector CT (SDCT) may provide several theoretical advantages over pre-existing DECT approaches in terms of adjustment-free sampling number and dose modulation, beam hardening correction, and production spectral images by post-processing. In addition, by adopting noise reduction algorithm, high contrast resolution was expected even in low keV level. We surmised that this improvement would be beneficial to obese people. Therefore, our aim of study is to compare image quality of virtual monochromatic spectral images (VMI) and polychromatic images reconstructed from SDCT with different body size and radiation dose using anthropomorphic liver phantom.

METHODS

One small and one large size of body phantoms, each containing eight (four high- and four low-contrast) simulated focal liver lesions (FLLs) were scanned by SDCT (at 120 kVp) using different Dose Right Indexes (DRIs). VMI were reconstructed from spectral base images from 40 keV to 200 keV. Hybrid iterative reconstruction (iDose4) was used for polychromatic image reconstruction. Image noise and contrast to noise ratio (CNR) were compared. Five radiologists independently rated lesion conspicuity, diagnostic acceptability and subjective noise level in every image sets, and determined optimal keV level in VMI.

RESULTS

Compare with conventional polychromatic images, VMI showed superior CNR at low keV level regardless of phantom size at every examined DRIs (Ps < 0.05). As body size increased, VMI had more gradual CNR decrease and noise increase than conventional polychromatic images. For low contrast FLLs in large phantom, lesion conspicuities at low radiation dose levels (DRI 16 and 19) were significantly increased in VMI (Ps < 0.05). Subjective image noise and diagnostic acceptabilities were significantly improved at VMI in both phantom size.

CONCLUSIONS

VMI of dual-layer spectral detector CT with noise reduction algorithm provides improved CNR, noise reduction, and better subjective image quality in imaging of obese simulated liver phantom compared with polychromatic images. This may hold promise for improving detection of liver lesions and improved imaging of obese patients.

Influence of Monoenergetic Images at Different Energy Levels in Dual-Energy Spectral CT on the Accuracy of Computer-Aided Detection for Pulmonary Embolism

PURPOSE

To investigate the influence of monoenergetic images of different energy levels in spectral computed tomography (CT) on the accuracy of computer aided detection (CAD) for pulmonary embolism (PE).

MATERIALS AND METHODS

CT images of 20 PE patients who underwent spectral CT pulmonary angiography were retrospectively analyzed. Nine sets of monochromatic images from 40 to 80 keV at 5 keV interval were reconstructed and then independently analyzed for detecting PE using a commercially available CAD software. Two experienced radiologists reviewed all images and recorded the number of emboli manually, which was used as the reference standard. The CAD findings for the number of PE at different energies were compared with the reference standard to determine the number of true positives and false positives with CAD and to calculate the sensitivity and false positive rate at different energies.

RESULT

There were 120 true emboli. The total numbers of CAD-detected PE at 40-80 keV were 48, 67, 63, 87, 106, 115, 138, 157, and 226. Images at low energies had low sensitivities and low false positive rates; images at high energies had high sensitivities and high false positive rates. At 60 keV and 65 keV, CAD achieved sensitivity at 81.67% and 84.17%, respectively and false positive rate at 7.55% and 12.17%, respectively to provide the optimum combination of high sensitivity and low false positive rate.

CONCLUSION

Monochromatic images of different energies in dual-energy spectral CT affect the accuracy of CAD for PE. The combination of CAD with images at 60-65 keV provides the optimum combination of high sensitivity and low false positive rate in detecting PE.

The value of single-source dual-energy CT imaging for discriminating microsatellite instability from microsatellite stability human colorectal cancer

OBJECTIVES

To demonstrate the value of single-source dual-energy computed tomography (ssDECT) imaging for discriminating microsatellite instability (MSI) from microsatellite stability (MSS) colorectal cancer (CRC).

METHODS

Thirty-eight and seventy-six patients with pathologically proven MSI and MSS CRC, respectively, were retrospectively selected and compared. These patients underwent contrast-enhanced abdominal ssDECT scans before any anti-cancer treatment. Effective atomic number (Eff-Z) in precontrast phase, slope k of spectral HU curve in precontrast (k-P), arterial (k-A), venous (k-V), and delayed phase (k-D), normalized iodine concentration in arterial (NIC-A), venous (NIC-V), and delayed phase (NIC-D), of tumors in two groups were measured by two reviewers. Consistency of measurements was tested by intra-class correlation coefficients (ICC). Mann-Whitney U test or Student's t test was used to compare above values between MSI and MSS. Multivariate logistic regression was used to analyze multiple parameters. Receiver operating characteristic curves were calculated to assess diagnostic efficacies.

RESULTS

Interobserver agreement was excellent (ICC > 0.80). MSI CRC had significantly lower values in all measurements (NIC-A, V, D; k-P, A, V, D; Eff-Z) than MSS CRC. For discriminating MSI from MSS CRC, the area under curve (AUC) using k-A was the highest (AUC, 0.803; sensitivity, 72.4%; specificity, 76.3%). The multivariate logistic regression (selection method, Enter) with combined ssDECT parameters (NIC-A, NIC-V, NIC-D, Eff-Z, k-P, k-A, k-V, k-D) significantly improved diagnostic capability with AUC of 0.886 (sensitivity, 81.6%; specificity, 81.6%).

CONCLUSIONS

The combination of multiple parameters in ssDECT imaging by multivariate logistic regression provides relatively high diagnostic accuracy for discriminating MSI from MSS CRC.

KEY POINTS

• ssDECT generates multiple parameters for discriminating CRC with MSI from MSS. • ssDECT measurements for MSI CRC were significantly lower than MSS CRC. • Combination of ssDECT parameters further improves diagnostic capability for differentiation.

Diagnostic value of single-source dual-energy spectral computed tomography in differentiating parotid gland tumors: initial results

Background

An accurate preoperative diagnosis that helps distinguish between benign and malignant parotid gland tumors is very important because the results strongly affect surgical procedures. We aimed to evaluate the value of single-source dual-energy computed tomography (ssDECT) in differentiating malignant from benign parotid gland tumors.

Methods

Fifty patients underwent enhanced neck ssDECT scanning before surgery. The images were analyzed using the gemstone spectral imaging (GSI) viewer software.

Results

Fifty-two tumors (43 patients) were confirmed histopathologically, comprising of 12 pleomorphic adenomas (PAs), 24 Warthin tumors (WTs) (15 patients), and 16 malignant tumors (MTs). The iodine concentration (IC), normalized iodine concentration to common carotid artery (NIC_A) and slope value of the spectral curve (λ_HU) of the WTs were significantly higher than those of MTs and PAs (all P<0.05). The optimal IC, NIC_A and λ_HU thresholds for differentiating PAs from MTs were 0.91 mg/mL, 0.15 and 1.09, respectively, achieving sensitivities of 91.7%, 91.7% and 91.7%, specificities of 95.0%, 85.0% and 95.0%, and accuracies of 94.2%, 86.5% and 94.2%, respectively for distinguishing PAs from MTs. The optimal IC, NIC_A and λ_HU thresholds for distinguishing WTs from MTs were 1.46 mg/mL, 0.20 and 1.72, achieving sensitivities of 91.7%, 95.8% and 91.7%, and specificities of 89.3%, 85.7% and 89.3%, respectively. The accuracy was 90.4%, 90.4% and 90.4%, respectively.

Conclusions

The parameters of ssDECT in enhanced CT scans are useful in the differential diagnosis of parotid tumors.

Low kV versus dual-energy virtual monoenergetic CT imaging for proven liver lesions: what are the advantages and trade-offs in conspicuity and image quality? A pilot study

PURPOSE

Single-energy low tube potential (SE-LTP) and dual-energy virtual monoenergetic (DE-VM) CT images both increase the conspicuity of hepatic lesions by increasing iodine signal. Our purpose was to compare the conspicuity of proven liver lesions, artifacts, and radiologist preferences in dose-matched SE-LTP and DE-VM images.

METHODS

Thirty-one patients with 72 proven liver lesions (21 benign, 51 malignant) underwent full-dose contrast-enhanced dual-energy CT (DECT). Half-dose images were obtained using single tube reconstruction of the dual-source SE-LTP projection data (80 or 100 kV), and by inserting noise into dual-energy projection data, with DE-VM images reconstructed from 40 to 70 keV. Three blinded gastrointestinal radiologists evaluated half-dose SE-LTP and DE-VM images, ranking and grading liver lesion conspicuity and diagnostic confidence (4-point scale) on a per-lesion basis. Image quality (noise, artifacts, sharpness) was evaluated, and overall image preference was ranked on per-patient basis. Lesion-to-liver contrast-to-noise ratio (CNR) was compared between techniques.

RESULTS

Mean lesion size was 1.5 ± 1.2 cm. Across the readers, the mean conspicuity ratings for 40, 45, and 50 keV half-dose DE-VM images were superior compared to other half-dose image sets (p < 0.0001). Per-lesion diagnostic confidence was similar between half-dose SE-LTP compared to half-dose DE-VM images (p ≥ 0.05; 1.19 vs. 1.24-1.32). However, SE-LTP images had less noise and artifacts and were sharper compared to DE-VM images less than 70 keV (p < 0.05). On a per-patient basis, radiologists preferred SE-LTP images the most and preferred 40-50 keV the least (p < 0.0001). Lesion CNR was also higher in SE-LTP images than DE-VM images (p < 0.01).

CONCLUSION

For the same applied dose level, liver lesions were more conspicuous using DE-VM compared to SE-LTP; however, SE-LTP images were preferred more than any single DE-VM energy level, likely due to lower noise and artifacts.

Comparison of image quality and radiation exposure between conventional imaging and gemstone spectral imaging in abdominal CT examination

OBJECTIVE

To compare patients' image quality and radiation exposure between gemstone spectral imaging (GSI) with rapid kV switching technique and conventional polychromatic imaging (CPI) performed in abdominal CT examinations.

METHODS

Adult patients who were referred to abdominal CT from October 2015 to March 2016 were enrolled. Unenhanced CT with CPI mode and tri-phase (arterial/portal/delayed phase) contrast-enhanced scan with GSI mode were performed with different protocols respectively. Regions of interest (ROIs) were drawn on muscle and fat. Parametric results of the image noise, signal-to-noise ratio (SNR) and clinical image quality in these regions between the monochromatic images reconstructed at 65 keV and conventional polychromatic images were compared. Radiation dose was also compared between CPI and GSI.

RESULTS

43 patients were recruited. Compared to conventional imaging, the noise level was generally not significantly different between GSI images in arterial phase and portal phase, and significantly higher (around 10%) in delayed phase. The SNR of GSI in portal phase was significantly higher than that of conventional imaging, and was similar between arterial phase/delayed phase and conventional imaging. The clinical image quality between conventional imaging and GSI was generally not significantly different. The dose length product was reduced by 0.3-20.1% in GSI compared to conventional imaging.

CONCLUSION

GSI reduces the radiation exposure slightly, however maintains or even improves image quality. These results may warrant the application of GSI in patients referred for abdominal CT. Advances in knowledge: Compared to CPI, GSI reduces the radiation exposure slightly, however maintains or even improves image quality in abdominal CT. These findings may warrant the application of GSI in patients referred for abdominal CT.

Application of dual-energy spectral CT imaging in differential diagnosis of bladder cancer and benign prostate hyperplasia

The aim of this study was to explore the clinical value of dual-energy spectral CT imaging in the differential diagnosis between bladder cancer and benign prostate hyperplasia (BPH).We retrospectively analyzed images of 118 patients who received pelvic dual-energy spectral CT imaging. These patients were later confirmed to have bladder cancer in 61 patients and BPH in 57 patients. CT values of the 2 lesion types from 40 to 140 keV were measured from the monochromatic spectral CT image to generate spectral HU curves. The slope of the spectral curve and the lesion effective atomic number were calculated. The measured parameters were analyzed with independent-sample Mann-Whitney U test.There was a statistically significant difference in CT value between the 2 groups from 40 to 90 keV, with the biggest difference at 40 keV (median and interquartile range: 83.3 HU and 22.9 HU vs 60.6 HU and 16.7 HU, Z = 5.932, P < 0.001). The slope of the spectral HU curve for bladder cancer was markedly higher than that of BPH (median and interquartile range: 0.48 and 0.23 vs 0.26 and 0.22, Z = 5.162, P < 0.001); the difference in effective atomic number (median and interquartile range: 7.99 and 0.21 vs 7.80 and 0.20, Z = 5.233, P < 0.001) was also statistically significant.Dual-energy spectral CT imaging provides high sensitivity and specificity for differentiating bladder cancer from benign prostate hyperplasia.

Improving Image Quality of Bronchial Arteries with Virtual Monochromatic Spectral CT Images

OBJECTIVE

To evaluate the clinical value of using monochromatic images in spectral CT pulmonary angiography to improve image quality of bronchial arteries.

METHODS

We retrospectively analyzed the chest CT images of 38 patients who underwent contrast-enhanced spectral CT. These images included a set of 140kVp polychromatic images and the default 70keV monochromatic images. Using the standard Gemstone Spectral Imaging (GSI) viewer on an advanced workstation (AW4.6,GE Healthcare), an optimal energy level (in keV) for obtaining the best contrast-to-noise ratio (CNR) for the artery could be automatically obtained. The signal-to-noise ratio (SNR), CNR and objective image quality score (1-5) for these 3 image sets (140kVp, 70keV and optimal energy level) were obtained and, statistically compared. The image quality score consistency between the two observers was also evaluated using Kappa test.

RESULTS

The optimal energy levels for obtaining the best CNR were 62.58±2.74keV.SNR and CNR from the 140kVp polychromatic, 70keV and optimal keV monochromatic images were (16.44±5.85, 13.24±5.52), (20.79±7.45, 16.69±6.27) and (24.9±9.91, 20.53±8.46), respectively. The corresponding subjective image quality scores were 1.97±0.82, 3.24±0.75, and 4.47±0.60. SNR, CNR and subjective scores had significant difference among groups (all p<0.001). The optimal keV monochromatic images were superior to the 70keV monochromatic and 140kVp polychromatic images, and there was high agreement between the two observers on image quality score (kappa>0.80).

CONCLUSIONS

Virtual monochromatic images at approximately 63keV in dual-energy spectral CT pulmonary angiography yielded the best CNR and highest diagnostic confidence for imaging bronchial arteries.

Spectral CT with monochromatic imaging and metal artifacts reduction software for artifacts reduction of ¹²⁵I radioactive seeds in liver brachytherapy

PURPOSE

To investigate the optimal monochromatic energy for artifacts reduction from (125)I seeds as well as image improvement in the vicinity of seeds on monochromatic images with and without metal artifacts reduction software (MARS) and to compare this with traditional 120-kVp images, so as to evaluate the application value of gemstone spectral imaging for reducing artifacts from (125)I seeds in liver brachytherapy.

MATERIALS AND METHODS

A total of 45 tumors from 25 patients treated with (125)I seed brachytherapy in the liver were enrolled in this study. Multiphasic spectral computed tomography (CT) scanning was performed for each patient. After a delay time of 15 s of portal vein phase, a traditional 120-kVp scan was performed, focusing on several planes of (125)I seeds only. The artifact index (AI) in the vicinity of seeds and the standard deviation (SD) of the CT density of region of interest in the outside liver parenchyma were calculated. Artifact appearance was evaluated and classified on reconstructed monochromatic S and 120-kVp images. Image quality in the vicinity of seeds of three data sets were evaluated using a 1-5 scale scoring method. The Friedman rank-sum test was used to estimate the scoring results of image quality.

RESULTS

The greatest noise in monochromatic images was found at 40 keV (SD = 27.38, AI = 206.40). The optimal monochromatic energy was found at 75 keV, which provided almost the least image noise (SD = 10.01) and good performance in artifact reduction (AI = 102.73). Image noise and AI reduction at 75 keV was decreased by 63.44 and 50.23%, compared with at 40 keV. Near-field thick artifacts were obvious in all 45 lesions, in 120-kVp images, and 75-keV images, but basically reduced in 75 keV MARS images and artifacts completely invisible in 7 lesions. The number of diagnosable images (score ≥3) was significantly more in the 75-keV MARS group (28/45), and the 75-keV group (22/45) than in the 120-kVp group (11/45) (p < 0.0167 for both). Compared with 120-kVp images alone, 75-keV images plus 75-keV MARS images can increase tumor visibility around seeds and increase the proportion of diagnostic images to 84.4% (38/45).

CONCLUSION

Spectral CT producing 75-keV MARS images could substantially reduce near-field thick artifacts caused by (125)I seeds and improve image quality, even to a state of being completely free from artifacts. Spectral CT imaging (with and without MARS) can provide more accurate CT images for estimating efficacy after (125)I seed brachytherapy in the liver.

Computer-Aided Diagnosis for Distinguishing Pancreatic Mucinous Cystic Neoplasms From Serous Oligocystic Adenomas in Spectral CT Images

OBJECTIVE

This preliminary study aims to verify the effectiveness of the additional information provided by spectral computed tomography (CT) with the proposed computer-aided diagnosis (CAD) scheme to differentiate pancreatic serous oligocystic adenomas (SOAs) from mucinous cystic neoplasms of pancreas cystic lesions.

MATERIALS AND METHODS

This study was conducted from January 2010 to October 2013. Twenty-three patients (5 men and 18 women; mean age, 43.96 years old) with SOA and 19 patients (3 men and 16 women; mean age, 41.74 years old) with MCN were included in this retrospective study. Two types of features were collected by dual-energy spectral CT imaging as follows: conventional and additional quantitative spectral CT features. Classification results of the CAD scheme were compared using the conventional features and full feature data set. Important features were selected using support vector machine classification method combined with feature-selection technique. The optimal cutoff values of selected features were determined through receiver-operating characteristic curve analyses.

RESULTS

Combining conventional features with additional spectral CT features improved the overall accuracy from 88.37% to 93.02%. The selected features of the proposed CAD scheme were tumor size, contour, location, and low-energy CT values (43 keV). Iodine-water basis material pair densities in both arterial phase (AP) and portal venous phase (PP) were important factors for differential diagnosis of SOA and MCN. The optimal cutoff values of long axis, short axis, 40 keV monochromatic CT value in AP, iodine (water) density in AP, 43 keV monochromatic CT value in PP, and iodine (water) density in PP were 3.4 mm, 3.1 mm, 35.7 Hu, 0.32533 mg/mL, 39.4 Hu, and 0.348 mg/mL, respectively.

CONCLUSION

The combination of conventional features and additional information provided by dual-energy spectral CT shows a high accuracy in the CAD scheme. The quantitative information of spectral CT may prove useful in the diagnosis and classification of SOAs and MCNs with machine learning algorithms.

Fused monochromatic imaging acquired by single source dual energy CT in hepatocellular carcinoma during arterial phase: an initial experience

OBJECTIVE

To explore whether single and fused monochromatic images can improve liver tumor detection and delineation by single source dual energy CT (ssDECT) in patients with hepatocellular carcinoma (HCC) during arterial phase.

METHODS

Fifty-seven patients with HCC who underwent ssDECT scanning at Beijing Cancer Hospital were enrolled retrospectively. Twenty-one sets of monochromatic images from 40 to 140 keV were reconstructed at 5 keV intervals in arterial phase. The optimal contrast-noise ratio (CNR) monochromatic images of the liver tumor and the lowest-noise monochromatic images were selected for image fusion. We evaluated the image quality of the optimal-CNR monochromatic images, the lowest-noise monochromatic images and the fused monochromatic images, respectively. The evaluation indicators included the spatial resolution of the anatomical structure, the noise level, the contrast and CNR of the tumor.

RESULTS

In arterial phase, the anatomical structure of the liver can be displayed most clearly in the 65-keV monochromatic images, with the lowest image noise. The optimal-CNR monochromatic images of HCC tumor were 50-keV monochromatic images in which the internal structural features of the liver tumors were displayed most clearly and meticulously. For tumor detection, the fused monochromatic images and the 50-keV monochromatic images had similar performances, and were more sensitive than 65-keV monochromatic images.

CONCLUSIONS

We achieved good arterial phase images by fusing the optimal-CNR monochromatic images of the HCC tumor and the lowest-noise monochromatic images. The fused images displayed liver tumors and anatomical structures more clearly, which is potentially helpful for identifying more and smaller HCC tumors.