Dual-energy CT iodine maps as an alternative quantitative imaging biomarker to abdominal CT perfusion: determination of appropriate trigger delays for acquisition using bolus tracking

Dual-layer dual-energy CT-derived pulmonary perfusion for the differentiation of acute pulmonary embolism and chronic thromboembolic pulmonary hypertension

OBJECTIVES

To evaluate dual-layer dual-energy computed tomography (dlDECT)-derived pulmonary perfusion maps for differentiation between acute pulmonary embolism (PE) and chronic thromboembolic pulmonary hypertension (CTEPH).

METHODS

This retrospective study included 131 patients (57 patients with acute PE, 52 CTEPH, 22 controls), who underwent CT pulmonary angiography on a dlDECT. Normal and malperfused areas of lung parenchyma were semiautomatically contoured using iodine density overlay (IDO) maps. First-order histogram features of normal and malperfused lung tissue were extracted. Iodine density (ID) was normalized to the mean pulmonary artery (MPA) and the left atrium (LA). Furthermore, morphological imaging features for both acute and chronic PE, as well as the combination of histogram and morphological imaging features, were evaluated.

RESULTS

In acute PE, normal perfused lung areas showed a higher mean and peak iodine uptake normalized to the MPA than in CTEPH (both p < 0.001). After normalizing mean ID in perfusion defects to the LA, patients with acute PE had a reduced average perfusion (IDmean,LA) compared to both CTEPH patients and controls (p < 0.001 for both). IDmean,LA allowed for a differentiation between acute PE and CTEPH with moderate accuracy (AUC: 0.72, sensitivity 74%, specificity 64%), resulting in a PPV and NPV for CTEPH of 64% and 70%. Combining IDmean,LA in the malperfused areas with the diameter of the MPA (MPAdia) significantly increased its ability to differentiate between acute PE and CTEPH (sole MPAdia: AUC: 0.76, 95%-CI: 0.68-0.85 vs. MPAdia + 256.3 * IDmean,LA - 40.0: AUC: 0.82, 95%-CI: 0.74-0.90, p = 0.04).

CONCLUSION

dlDECT enables quantification and characterization of pulmonary perfusion patterns in acute PE and CTEPH. Although these lack precision when used as a standalone criterion, when combined with morphological CT parameters, they hold potential to enhance differentiation between the two diseases.

CLINICAL RELEVANCE STATEMENT

Differentiating between acute PE and CTEPH based on morphological CT parameters is challenging, often leading to a delay in CTEPH diagnosis. By revealing distinct pulmonary perfusion patterns in both entities, dlDECT may facilitate timely diagnosis of CTEPH, ultimately improving clinical management.

KEY POINTS

• Morphological imaging parameters derived from CT pulmonary angiography to distinguish between acute pulmonary embolism and chronic thromboembolic pulmonary hypertension lack diagnostic accuracy. • Dual-layer dual-energy CT reveals different pulmonary perfusion patterns between acute pulmonary embolism and chronic thromboembolic pulmonary hypertension. • The identified parameters yield potential to enable more timely identification of patients with chronic thromboembolic pulmonary hypertension.

Spectral performance evaluation of a second-generation spectral detector CT

PURPOSE

The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities.

METHODS

A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported.

RESULTS

Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively.

CONCLUSION

The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Intra-patient variability of iodine quantification across different dual-energy CT platforms: assessment of normalization techniques

OBJECTIVES

To investigate intra-patient variability of iodine concentration (IC) between three different dual-energy CT (DECT) platforms and to test different normalization approaches.

METHODS

Forty-four patients who underwent portal venous phase abdominal DECT on a dual-source (dsDECT), a rapid kVp switching (rsDECT), and a dual-layer detector platform (dlDECT) during cancer follow-up were retrospectively included. IC in the liver, pancreas, and kidneys and different normalized ICs (NICPV:portal vein; NICAA:abdominal aorta; NICALL:overall iodine load) were compared between the three DECT scanners for each patient. A longitudinal mixed effects analysis was conducted to elucidate the effect of the scanner type, scan order, inter-scan time, and contrast media amount on normalized iodine concentration.

RESULTS

Variability of IC was highest in the liver (dsDECT vs. dlDECT 28.96 (14.28-46.87) %, dsDECT vs. rsDECT 29.08 (16.59-62.55) %, rsDECT vs. dlDECT 22.85 (7.52-33.49) %), and lowest in the kidneys (dsDECT vs. dlDECT 15.76 (7.03-26.1) %, dsDECT vs. rsDECT 15.67 (8.86-25.56) %, rsDECT vs. dlDECT 10.92 (4.92-22.79) %). NICALL yielded the best reduction of IC variability throughout all tissues and inter-scanner comparisons, yet did not reduce the variability between dsDECT vs. dlDECT and rsDECT, respectively, in the liver. The scanner type remained a significant determinant for NICALL in the pancreas and the liver (F-values, 12.26 and 23.78; both, p < 0.0001).

CONCLUSIONS

We found tissue-specific intra-patient variability of IC across different DECT scanner types. Normalization mitigated variability by reducing physiological fluctuations in iodine distribution. After normalization, the scanner type still had a significant effect on iodine variability in the pancreas and liver.

CLINICAL RELEVANCE STATEMENT

Differences in iodine quantification between dual-energy CT scanners can partly be mitigated by normalization, yet remain relevant for specific tissues and inter-scanner comparisons, which should be taken into account at clinical routine imaging.

KEY POINTS

• Iodine concentration showed the least variability between scanner types in the kidneys (range 10.92-15.76%) and highest variability in the liver (range 22.85-29.08%). • Normalizing tissue-specific iodine concentrations against the overall iodine load yielded the greatest reduction of variability between scanner types for 2/3 inter-scanner comparisons in the liver and for all (3/3) inter-scanner comparisons in the kidneys and pancreas, respectively. • However, even after normalization, the dual-energy CT scanner type was found to be the factor significantly influencing variability of iodine concentration in the liver and pancreas.

Pancreatic CT perfusion: quantitative meta-analysis of disease discrimination, protocol development, and effect of CT parameters

BACKGROUND

This study provides a quantitative meta-analysis of pancreatic CT perfusion studies, investigating choice of study parameters, ability for quantitative discrimination of pancreatic diseases, and influence of acquisition and reconstruction parameters on reported results.

METHODS

Based on a PubMed search with key terms 'pancreas' or 'pancreatic,' 'dynamic' or 'perfusion,' and 'computed tomography' or 'CT,' 491 articles published between 1982 and 2020 were screened for inclusion in the study. Inclusion criteria were: reported original data, human subjects, five or more datasets, measurements of pancreas or pancreatic pathologies, and reported quantitative perfusion parameters. Study parameters and reported quantitative measurements were extracted, and heterogeneity of study parameters and trends over time are analyzed. Pooled data were tested with weighted ANOVA and ANCOVA models for differences in perfusion results between normal pancreas, pancreatitis, PDAC (pancreatic ductal adenocarcinoma), and non-PDAC (e.g., neuroendocrine tumors, insulinomas) and based on study parameters.

RESULTS

Reported acquisition parameters were heterogeneous, except for contrast agent amount and injection rate. Tube potential and slice thickness decreased, whereas tube current time product and scan coverage increased over time. Blood flow and blood volume showed significant differences between pathologies (both p < 0.001), unlike permeability (p = 0.11). Study parameters showed a significant effect on reported quantitative measurements (p < 0.05).

CONCLUSIONS

Significant differences in perfusion measurements between pathologies could be shown for pooled data despite observed heterogeneity in study parameters. Statistical analysis indicates most influential parameters for future optimization and standardization of acquisition protocols.

CRITICAL RELEVANCE STATEMENT

Quantitative CT perfusion enables differentiation of pancreatic pathologies despite the heterogeneity of study parameters in current clinical practice.

Evaluation and timing optimization of CT perfusion first pass analysis in comparison to maximum slope model in pancreatic adenocarcinoma

For implementation, performance evaluation and timing optimization of CT perfusion first pass analysis (FPA) by correlation with maximum slope model (MSM) in pancreatic adenocarcinoma, dynamic CT perfusion acquisitions of 34 time-points were performed in 16 pancreatic adenocarcinoma patients. Regions of interest were marked in both parenchyma and carcinoma. FPA, a low radiation exposure CT perfusion technique, was implemented. Blood flow (BF) perfusion maps were calculated using FPA and MSM. Pearson's correlation between FPA and MSM was calculated at each evaluated time-point to determine optimum timing for FPA. Differences in BF between parenchyma and carcinoma were calculated. Average BF for MSM was 106.8 ± 41.5 ml/100 ml/min in parenchyma and 42.0 ± 24.8 ml/100 ml/min in carcinoma, respectively. For FPA, values ranged from 85.6 ± 37.5 ml/100 ml/min to 117.7 ± 44.5 ml/100 ml/min in parenchyma and from 27.3 ± 18.8 ml/100 ml/min to 39.5 ± 26.6 ml/100 ml/min in carcinoma, depending on acquisition timing. A significant difference (p value < 0.0001) between carcinoma and parenchyma was observed at all acquisition times based on FPA measurements. FPA shows high correlation with MSM (r > 0.90) and 94% reduction in the radiation dose compared to MSM. CT perfusion FPA, where the first scan is obtained after the arterial input function exceeds a threshold of 120 HU, followed by a second scan after 15.5-20.0 s, could be used as a potential imaging biomarker with low radiation exposure for diagnosing and evaluating pancreatic carcinoma in clinical practice, showing high correlation with MSM and the ability to differentiate between parenchyma and carcinoma.

Dual-energy computed tomography as a lower radiation dose alternative to perfusion computed tomography in tumor viability assessment

To present the utility of dual-energy computed tomography (DECT) in the assessment of angiogenesis of focal lesions as an example of a solitary pulmonary nodule (SPN). This prospective study comprised 28 patients with SPN who underwent DECT and perfusion computed tomography (CTP), according to a proprietary protocol. Two radiologists independently analyzed four perfusion parameters, namely blood flow (BF), blood volume (BV), the time to maximum of the tissue residue function (Tmax), permeability surface area product (PS) from CTP, in addition to the iodine concentration (IC) and normalized iodine concentration (NIC) of the SPN from DECT. We used the Pearson R correlation and interclass correlation coefficients (ICC_s). Statistical significance was assumed at p < 0.05. The mean tumor size was 23.5 ± 6.5 mm. We observed good correlations between IC and BF (r = 0.78, p < 0.000) and NIC and BF (r = 0.71, p < 0.000) as well as between IC and BV (r = 0.73, p < 0.000) and NIC and BV (r = 0.73, p < 0.000) and poor correlation between IC and PS (r = 0.38, p = 0.044).There was no correlation between NIC and PS (r = 0.35, p = 0.064), IC content and Tmax (r = - 0.28, p = 0.147) and NIC and Tmax (r = - 0.21, p = 0.266). Inter-reader agreement on quantitative parameters at CTP (ICC_PS = 0.97, ICC_Tmax = 0.96, ICC_BV = 0.98, and ICC_BF = 0.99) and DECT (ICC_IC = 0.98) were excellent. The radiation dose was significantly lower in DECT than that in CTP (4.84 mSv vs. 9.07 mSv, respectively). DECT is useful for the functional assessment of oncological lesions with less exposure to radiation compared to perfusion computed tomography.

Diagnostic Effectiveness of Dual Source Dual Energy Computed Tomography for Benign and Malignant Thyroid Nodules

Objective

To evaluate the diagnostic effectiveness of dual source dual energy computed tomography (DS-DECT) for benign and malignant thyroid nodules.

Methods

Between January 2019 and December 2021, 60 patients with surgically and pathologically verified thyroid nodules treated at our institution were recruited. DS-DECT was administered to all patients. The iodine content of lesioned and normal tissues, the normalized iodine concentration (NIC) and standardized CT values of benign and malignant nodules, the consistency of examination results and pathological findings, and diagnostic effectiveness were all investigated.

Results

The diagnosis accuracy was the same as that of surgical pathology, producing a 100% accuracy for the 60 patients with thyroid nodules (42 were benign and 18 were malignant). The iodine content of lesioned solid tissue differed significantly from that of normal tissue, as did the iodine content of malignant and benign nodules (P < 0.05). In the arterial phase, no significant difference was found in NIC and standardized CT values between benign and malignant nodules (P > 0.05). The optimal critical NIC for differentiating benign and malignant nodules in the venous phase was 0.74 and the standardized CT value was 0.79 HU according to the receiver operating characteristics (ROC) curve. Malignant nodules were diagnosed when the NIC was <0.74 and the standardized CT value was <0.79 HU, with AUC values of 0.89 and 0.93, respectively, where the sensitivity and specificity of the differential diagnosis of NIC were 90.48% (38/42) and 88.89% (16/18), respectively, and those of the differential diagnosis of standardized CT value were 92.86% (39/ 42) and 94.44% (17/18), respectively. The diagnosis accuracy of DS-DECT was 100%, and the diagnostic results of morphological characteristics and pathological testing were consistent. The sensitivity and specificity of the NIC values and standardized CT values in the venous phase differential diagnosis of benign and malignant nodules were compatible with the morphological differential diagnosis.

Conclusion

DS-DECT is highly accurate in determining the benignity and malignancy of thyroid nodules and has a strong potential for clinical promotion to allow for prompt treatment.

Detection of Incomplete Irreversible Electroporation (IRE) and Microwave Ablation (MWA) of Hepatocellular Carcinoma (HCC) Using Iodine Quantification in Dual Energy Computed Tomography (DECT)

Early detection of local tumor progression (LTP) after irreversible electroporation (IRE) and microwave ablation (MWA) of hepatocellular carcinoma (HCC) remains challenging. The goal of this study was to identify cases with insufficient ablation and prevent HCC recurrencies by measuring iodine uptake using dual-energy computed tomography (DECT). In 54 HCC-patients, the volumetric iodine concentration (VIC) of the central and peripheral ablation area was evaluated by DECT within 24 h after IRE or MWA. Follow-up was performed with CT and/or MRI at 6 weeks, 3, 6, 9, and 12 months, respectively. In both groups, LTP was solely detected in the peripheral area (IRE: n = 4; MWA: n = 4) and LTP patients showed significantly higher VIC values in the peripheral zone than patients without LTP (IRE: * p = 0.0005; MWA: * p = 0.000). In IRE-LTP patients, no significant difference between the VIC values of non-ablated liver tissue and the peripheral zone was detected (p = 0.155). The peripheral zones of IRE patients without LTP (* p = 0.000) and MWA patients, irrespective of the presence of LTP (LTP: * p = 0.005; without LTP: * p = 0.000), showed significantly lower VIC values than non-ablated liver parenchyma. Higher BCLC tumor stages were indicative for LTP (* p = 0.008). The study suggests that elevated iodine uptake in the peripheral ablation zone could help identify LTP after IRE and MWA of HCC.

Dual-energy CT perfusion imaging for differentiating invasive thymomas, thymic carcinomas, and lymphomas in adults

AIM

To evaluate the role of dual-energy computed tomography perfusion (DECTP) imaging in differentiating invasive thymomas (ITs), thymic cancers (TCs), and lymphomas in adults.

MATERIALS AND METHODS

Ninety-five patients with solid masses in the prevascular mediastinum who underwent DECTP examinations were enrolled in this study. The perfusion parameters (blood flow, BF; blood volume, BV; mean transit time, MTT; permeability surface, PS) and spectral parameters (water concentration, WC; iodine concentration, IC; normalised iodine concentration, NIC; the slope of spectral radiodensity [Hounsfield units] curve, λ_HU) of the lesions were analysed.

RESULTS

There were no differences in the MTT or WC values among ITs, TCs, and lymphomas (all p>0.05). The IC, NIC, and λ_HU values in the optimal arterial and venous phases and PS values of TCs were higher than those of ITs and lymphomas (all p<0.05), and there were no differences between ITs and lymphomas (all p>0.05). The BF and BV values of lymphomas were lower than those of ITs and TCs (all p<0.05), and there were no differences between ITs and TCs (all p>0.05). The cut-off values for BF and BV used to differentiate lymphomas from ITs and TCs were 42.83 ml/min/100 g and 4.66 ml/100 g, respectively (area under the receiver operating characteristic curve: 0.847 and 0.839; sensitivity, 80.6% and 82.1%; specificity, 75% and 71.4%; accuracy, 78.9% and 81.1%).

CONCLUSIONS

The perfusion and spectral parameters of DECTP imaging help to identify ITs, TCs, and lymphomas, and BF and BV values help to differentiate lymphomas from ITs and TCs.

Spectral Detector CT-Derived Pulmonary Perfusion Maps and Pulmonary Parenchyma Characteristics for the Semiautomated Classification of Pulmonary Hypertension

Objectives

To evaluate the usefulness of spectral detector CT (SDCT)-derived pulmonary perfusion maps and pulmonary parenchyma characteristics for the semiautomated classification of pulmonary hypertension (PH).

Methods

A total of 162 consecutive patients with right heart catheter (RHC)-proven PH of different aetiologies as defined by the current ESC/ERS guidelines who underwent CT pulmonary angiography (CTPA) on SDCT and 20 patients with an invasive rule-out of PH were included in this retrospective study. Semiautomatic lung segmentation into normal and malperfused areas based on iodine density (ID) as well as automatic, virtual non-contrast-based emphysema quantification were performed. Corresponding volumes, histogram features and the ID SkewnessPerfDef-Emphysema-Index (δ-index) accounting for the ratio of ID distribution in malperfused lung areas and the proportion of emphysematous lung parenchyma were computed and compared between groups.

Results

Patients with PH showed a significantly greater extent of malperfused lung areas as well as stronger and more homogenous perfusion defects. In group 3 and 4 patients, ID skewness revealed a significantly more homogenous ID distribution in perfusion defects than in all other subgroups. The δ-index allowed for further subclassification of subgroups 3 and 4 (p &lt; 0.001), identifying patients with chronic thromboembolic PH (CTEPH, subgroup 4) with high accuracy (AUC: 0.92, 95%-CI, 0.85–0.99).

Conclusion

Abnormal pulmonary perfusion in PH can be detected and quantified by semiautomated SDCT-based pulmonary perfusion maps. ID skewness in malperfused lung areas, and the δ-index allow for a classification of PH subgroups, identifying groups 3 and 4 patients with high accuracy, independent of reader expertise.

Quantitative contrast enhanced dual energy CT to predict avascular necrosis: a feasibility study of proximal humerus fractures

BACKGROUND

Avascular necrosis is a delayed complication of proximal humerus fractures that increases the likelihood of poor clinical outcomes. CT scans are routinely performed to guide proximal humerus fracture management. We hypothesized iodine concentration on post-contrast dual energy CT scans identifies subjects who develop avascular necrosis and ischemia due to compromised blood flow.

MATERIALS AND METHODS

55 patients with proximal humerus fractures enrolled between 2014 and 2017 underwent clinical, radiographic and contrast enhanced dual energy CT assessment. Iodine densities of the humeral head and the glenoid (control) were measured on CT. Subjects managed with open reduction internal fixation or conservatively (non-surgical) were followed for up to two years for radiographic evidence of avascular necrosis. Arthroplasty subjects underwent histopathologic evaluation for ischemia of the resected humeral head.

RESULTS

17 of 55 subjects (30.9%) were treated conservatively, 21 (38.2%) underwent open reduction internal fixation and 17 of 55 (30.9%) underwent arthroplasty. Of the 38 subjects treated conservatively or with ORIF, 20 (52.6%) completed 12 months of follow up and 14 (36.8%) 24 months of follow up. At 12 months follow up, two of 20 subjects (10%) and at 24 months 3 of 14 subjects (21.4%) developed avascular necrosis. At 12 months, the mean humerus/glenoid iodine ratio was 1.05 (standard deviation 0.24) in subjects with AVN compared to 0.91 (0.24) in those who did not. At 24 months, subjects with avascular necrosis had a mean humerus/glenoid iodine concentration ratio of 1.06 (0.17) compared to 0.924 (0.21) in those who did not. Of 17 arthroplasty subjects, 2 had severe ischemia and an iodine ratio of 1.08 (0.30); 5 had focal ischemia and a ratio of 1.00 (0.36); and 8 no ischemia and a ratio of 0.83 (0.08).

CONCLUSIONS

Quantifying iodine using dual energy CT in subjects with proximal humerus fractures is technically feasible. Preliminary data suggest higher humeral head iodine concentration may increase risk of avascular necrosis; however, future studies must enroll and follow enough subjects managed with open reduction internal fixation or conservatively for two or more years to provide statistically significant results. Trial Registrations NCT02170545 registered June 23, 2014, ClinicalTrials.gov.

Iodine concentration and tissue attenuation in dual-energy contrast-enhanced CT as a potential quantitative parameter in early detection of local pancreatic carcinoma recurrence after surgical resection

PURPOSE

Due to the difficult differentiation from non-specific postoperative soft tissue formation (PSF), early diagnosis of pancreatic carcinoma recurrence remains challenging. Thus, we investigated the diagnostic potential of dual-energy (DE) contrast-enhanced CT.

METHOD

After potentially curative pancreatic carcinoma resection, 31 consecutive patients with PSF were examined via DE perfusion CT, acquiring 34 images (80 kVp/140 kVp) every 1.5 s, as the initial purpose of this study was evaluating CT-Perfusion. Corresponding time points of arterial, pancreatic, and early venous phase were calculated from bolus trigger times in prior conventional CT. Iodine and 120 kVp-equivalent images were calculated. Regions of interest were placed in each soft tissue formation. Diagnosis of local recurrence was confirmed by regular follow-up or histopathology.

RESULTS

Final diagnosis was local recurrence in 17 patients and non-specific PSF in 14 patients. Iodine concentrations in early venous phase were significantly higher in recurrent carcinoma than in non-specific PSF (1.47 mg/ml vs. 0.96 mg/ml, p = 0.007). In earlier contrast phases iodine concentrations tended to be higher, but not significantly. CT numbers in recurrent carcinoma in 120 kVp-equivalent images in venous phase were significantly higher, too (74HU vs 47HU, p = 0.002). ROC-curve analysis for iodine concentrations in early venous phase suggests a cut-off value of ≥ 1.55 mg/ml for local recurrence (AUC = 0.78, specificity = 1.0, sensitivity = 0.53) and for CT numbers in 120kVp-equivalent images a cut-off value of ≥ 57HU (AUC = 0.82, specificity = 0.82, sensitivity = 0.71).

CONCLUSION

In difficult cases, measuring iodine concentrations or CT numbers in PSF in (early) venous phase DECT could be a valuable additional parameter for differentiating local recurrence from non-specific PSF.

Clinical Implementation of Dual-Energy CT for Gastrointestinal Imaging

Dual-energy CT (DECT) overcomes several limitations of conventional single-energy CT (SECT) for the evaluation of gastrointestinal diseases. This article provides an overview of practical aspects of the DECT technology and acquisition protocols, reviews existing clinical applications, discusses current challenges, and describes future directions, with a focus on gastrointestinal imaging. A head-to-head comparison of technical specifications among DECT scanner implementations is provided. Energy- and material-specific DECT image reconstructions enable retrospective (i.e., after examination acquisition) image quality adjustments that are not possible using SECT. Such adjustments may, for example, correct insufficient contrast bolus or metal artifacts, thereby potentially avoiding patient recalls. A combination of low-energy monochromatic images, iodine maps, and virtual unenhanced images can be included in protocols to improve lesion detection and disease characterization. Relevant literature is reviewed regarding use of DECT for evaluation of the liver, gallbladder, pancreas, and bowel. Challenges involving cost, workflow, body habitus, and variability in DECT measurements are considered. Artificial intelligence and machine-learning image reconstruction algorithms, PACS integration, photon-counting hardware, and novel contrast agents are expected to expand the multienergy capability of DECT and further augment its value.

Iodine Parameters in Triple-Bolus Dual-Energy CT Correlate With Perfusion CT Biomarkers of Angiogenesis in Renal Cell Carcinoma

OBJECTIVE. The purpose of this study is to determine the degree of the relationship between perfusion CT (PCT) parameters and iodine concentration metrics derived from triple-bolus dual-energy CT (DECT) and to compare the radiation dose delivered. SUBJECTS AND METHODS. This single-center prospective study was conducted from October 2015 to September 2017. Twenty-three consenting adults (15 men and eight women; mean [± SD] age, 56 ± 13 years [range, 25-78 years]) with renal cell carcinomas underwent consecutive PCT and triple-bolus DECT examinations. Triple-bolus DECT consisted of synchronous corticomedullary, nephrographic, and delayed phase scans acquired using a dual-source DECT scanner. Two readers independently analyzed blood flow, blood volume, and permeability, as measured by PCT, and iodine density and iodine ratio, as measured by triple-bolus DECT. Size-specific dose estimates were calculated for both groups. RESULTS. Interreader agreement was good for permeability (intraclass correlation coefficient [ICC] =.812) and blood flow (ICC = 0.849) and excellent for blood volume (ICC = 0.956), iodine density (ICC = 0.961), and iodine ratio (ICC = 0.956). Very strong positive correlations were found between blood volume and iodine density (p < 0.001) and between blood volume and iodine ratio (p < 0.001). Strong positive correlations were found between blood flow and iodine density (p < 0.001) and between blood flow and iodine ratio (p < 0.001). The correlations between permeability and iodine density (p = 0.01) and between permeability and iodine ratio (p = 0.02) were moderate. The mean size-specific dose estimate of triple-bolus DECT was approximately 15 times lower than that of PCT (p < 0.001). CONCLUSION. Quantitative iodine metrics derived from triple-bolus DECT showed significant correlation with CT parameters in renal cell carcinoma, with a significantly lower radiation dose.

Dual-energy CT: theoretical principles and clinical applications

The physical principles of dual-energy computed tomography (DECT) are as old as computed tomography (CT) itself. To understand the strengths and the limits of this technology, a brief overview of theoretical basis of DECT will be provided. Specific attention will be focused on the interaction of X-rays with matter, on the principles of attenuation of X-rays in CT toward the intrinsic limits of conventional CT, on the material decomposition algorithms (two- and three-basis-material decomposition algorithms) and on effective Rho-Z methods. The progresses in material decomposition algorithms, in computational power of computers and in CT hardware, lead to the development of different technological solutions for DECT in clinical practice. The clinical applications of DECT are briefly reviewed in relation to the specific algorithms.

Measuring Dynamic CT Perfusion Based on Time-Resolved Quantitative DECT Iodine Maps: Comparison to Conventional Perfusion at 80 kVp for Pancreatic Carcinoma

OBJECTIVES

Using dual-energy computed tomography (DECT) for quantifying iodine content after injection of contrast agent could provide a quantitative basis for dynamic computed tomography (CT) perfusion measurements by means of established mathematical models of contrast agent kinetics, thus improving results by combining the strength of both techniques, which was investigated in this study.

MATERIALS AND METHODS

A dynamic DECT acquisition over 51 seconds performed at 80/Sn140 kVp in 17 patients with pancreatic carcinoma was used to calculate iodine-enhancement images for each time point by means of 3-material decomposition. After motion correction, perfusion maps of blood flow were calculated using the maximum-slope model from both 80 kVp image data and iodine-enhancement images. Blood flow was measured in regions of interest placed in healthy pancreatic tissue and carcinoma for both of the derived perfusion maps. To assess image quality of input data, an adjusted contrast-to-noise ratio was calculated for 80 kVp images and iodine-enhancement images. Susceptibility of perfusion results to residual patient breathing motion during acquisition was investigated by measuring blood flow in fatty tissue surrounding the pancreas, where blood flow should be negligible compared with the pancreas.

RESULTS

For both 80 kVp and iodine-enhancement images, blood flow was significantly higher in healthy tissue (114.2 ± 37.4 mL/100 mL/min or 115.1 ± 36.2 mL/100 mL/min, respectively) than in carcinoma (46.5 ± 26.6 mL/100 mL/min or 49.7 ± 24.7 mL/100 mL/min, respectively). Differences in blood flow between 80 kVp image data and iodine-enhancement images were statistically significant in healthy tissue, but not in carcinoma. For 80 kVp images, adjusted contrast-to-noise ratio was significantly higher (1.3 ± 1.1) than for iodine-enhancement images (1.1 ± 0.9). When evaluating fatty tissue surrounding the pancreas for estimating influence of patient motion, measured blood flow was significantly lower for iodine-enhancement images (30.7 ± 12.0 mL/100 mL/min) than for 80 kVp images (39.0 ± 19.1 mL/100 mL/min). Average patient radiation exposure was 8.01 mSv for dynamic DECT acquisition, compared with 4.60 mSv for dynamic 80 kVp acquisition.

DISCUSSION

Iodine enhancement images can be used to calculate CT perfusion maps of blood flow, and compared with 80 kVp images, results showed only a small difference of 1 mL/100 mL/min in blood flow in healthy tissue, whereas patient radiation exposure was increased for dynamic DECT. Perfusion maps calculated based on iodine-enhancement images showed lower blood flow in fatty tissues surrounding the pancreas, indicating reduced susceptibility to residual patient breathing motion during the acquisition.

Evaluation of the effect of image noise on CT perfusion measurements using digital perfusion phantoms

OBJECTIVES

To assess the influence of image noise on computed tomography (CT) perfusion studies, CT perfusion software algorithms were evaluated for susceptibility to image noise and results applied to clinical perfusion studies.

METHODS

Digital perfusion phantoms were generated using a published deconvolution model to create time-attenuation curves (TACs) for 16 different combinations of blood flow (BF; 30/60/90/120 ml/100 ml/min) and flow extraction product (FEP; 10/20/30/40 ml/100 ml/min) corresponding to values encountered in clinical studies. TACs were distorted with Gaussian noise at 50 different strengths to approximate image noise, performing 200 repetitions for each noise level. A total of 160,000 TACs were evaluated by measuring BF and FEP with CT perfusion software, comparing results for the maximum slope and Patlak models with those obtained with a deconvolution model. To translate results to clinical practice, data of 23 patients from a CT perfusion study were assessed for image noise, and the accuracy of reported CT perfusion measurements was estimated.

RESULTS

Perfusion measurements depend on image noise as means and standard deviations of BF and FEP over repetitions increase with increasing image noise, especially for low BF and FEP values. BF measurements derived by deconvolution show larger standard deviations than those performed with the maximum slope model. Image noise in the evaluated CT perfusion study was 26.46 ± 3.52 HU, indicating possible overestimation of BF by up to 85% in a clinical setting.

CONCLUSIONS

Measurements of perfusion parameters depend heavily upon the magnitude of image noise, which has to be taken into account during selection of acquisition parameters and interpretation of results, e.g., as a quantitative imaging biomarker.

KEY POINTS

• CT perfusion results depend heavily upon the magnitude of image noise. • Different CT perfusion models react differently to the presence of image noise. • Blood flow may be overestimated by 85% in clinical CT perfusion studies.