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

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for 'CTP' and 'PDAC.' Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters.

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.

Intraindividual Consistency of Iodine Concentration in Dual-Energy Computed Tomography of the Chest and Abdomen

OBJECTIVES

Dual-energy computed tomography (DECT)-derived quantification of iodine concentration (IC) is increasingly used in oncologic imaging to characterize lesions and evaluate treatment response. However, only limited data are available on intraindividual consistency of IC and its variation. This study investigates the longitudinal reproducibility of IC in organs, vessels, and lymph nodes in a large cohort of healthy patients who underwent repetitive DECT imaging.

MATERIALS AND METHODS

A total of 159 patients, who underwent a total of 469 repetitive (range, 2-4), clinically indicated portal-venous phase DECT examinations of the chest and abdomen, were retrospectively included. At time of imaging, macroscopic tumor burden was excluded by follow-up imaging (≥3 months). Iodine concentration was measured region of interest-based (N = 43) in parenchymatous organs, vessels, lymph nodes, and connective tissue. Normalization of IC to the aorta and to the trigger delay as obtained from bolus tracking was performed. For statistical analysis, intraclass correlation coefficient and modified variation coefficient (MVC) were used to assess intraindividual agreement of IC and its variation between different time points, respectively. Furthermore, t tests and analysis of variance with Tukey-Kramer post hoc test were used.

RESULTS

The mean intraclass correlation coefficient over all regions of interest was good to excellent (0.642-0.936), irrespective of application of normalization or the normalization technique. Overall, MVC ranged from 1.8% to 25.4%, with significantly lower MVC in data normalized to the aorta (5.8% [1.8%-15.8%]) in comparison with the MVC of not normalized data and data normalized to the trigger delay (P < 0.01 and P = 0.04, respectively).

CONCLUSIONS

Our study confirms intraindividual, longitudinal variation of DECT-derived IC, which varies among vessels, lymph nodes, organs, and connective tissue, following different perfusion characteristics; normalizing to the aorta seems to improve reproducibility when using a constant contrast media injection protocol.

Fusion of Preinterventional MR Imaging With Liver Perfusion CT After RFA of Hepatocellular Carcinoma: Early Quantitative Prediction of Local Recurrence

OBJECTIVES

The aim of this study was to evaluate the ability of fusion of pretreatment magnetic resonance (MR) imaging with posttreatment perfusion-CT (P-CT) after radiofrequency ablation (RFA) of hepatocellular carcinomas (HCCs) and to determine treatment success in an objective, quantitative way.

MATERIALS AND METHODS

In this institutional review board-approved study, 39 patients (78.4% male; mean age 68.2 ± 8.5 years) with a total of 43 HCCs, who underwent RFA at our institution and had diagnostic pre-RFA MR imaging and post-RFA P-CT, were included in the study. Post-RFA P-CT was performed within 24 hours after RFA. In a first step, the pre-RFA MR imaging, depicting the HCC, was registered onto the post-RFA P-CT using nonrigid image registration. After image registration, the MR data were reloaded jointly with the calculated perfusion parameter volumes into the perfusion application for quantitative analysis. A 3-dimensional volume of interest was drawn around the HCC and the ablation zone; both outlines were automatically projected onto all perfusion maps. Resulting perfusion values (normalized peak enhancement [NPE, %]; arterial liver perfusion [ALP, in mL/min/100 mL]; BF [blood flow, mL/100 mL/min]; and blood volume [BV, mL/100 mL]) and histogram data were recorded. Local tumor recurrence was defined in follow-up imaging according to the EASL guidelines.

RESULTS

Image registration of MR imaging and CT data was successful in 37 patients (94.9%). Local tumor recurrence was observed in 5 HCCs (12%). In the local tumor recurrence group (LTR-group), HCC size was significantly larger (22.7 ± 3.9 cm vs 17.8 ± 5.3 cm, P = 0.035) and the ablation zone was significantly smaller (29.8 ± 6.9 cm vs 39.3 ± 6.8 cm, P = 0.014) compared with the no-local tumor recurrence group (no-LTR group). The differences (ablation zone - tumor) of the perfusion parameters NPE, ALP, BF, and BV significantly differed between the 2 groups (all P's < 0.005). Especially, the difference (ablation zone - tumor) of NPE and ALP, with a cutoff value of zero, accurately differentiated between LTR or no-LTR in all cases. A negative difference of these perfusion parameters identified local tumor recurrence in all cases.

CONCLUSIONS

Image registration of pre-RFA MR imaging onto post-RFA P-CT is feasible and allows to predict local tumor recurrence within 24 hours after RFA in an objective, quantitative manner and with excellent accuracy.

CT Dosimetry: What Has Been Achieved and What Remains to Be Done

Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively high doses associated with these procedures. The main aim of this review article is to provide an overview of the most frequently used metrics for CT radiation dose characterization, discuss their strengths and limitations, and present patient dose assessment methods. Computed tomography dosimetry is still based on a CT dose index (CTDI) measured using 100-mm-long pencil ionization chambers and standard dosimetry phantoms (CTDI100). This dose index is easily measured but has important limitations. Computed tomography dose index underestimates the dose generated by modern CT scanners with wide beam collimation. Manufacturers should report corrected CTDI values in the consoles of CT systems. The size-specific dose estimate has been proposed to provide an estimate of the average dose at the center of the scan volume along the z-axis of a CT scan. Size-specific dose estimate is based on CTDI and conversion factors and, therefore, its calculation incorporates uncertainties associated with the measurement of CTDI. Moreover, the calculation of size-specific dose estimate is straightforward only when the tube current modulation is not activated and when the patient body diameter does not change considerably along the z-axis of the scan. Effective dose can be used to provide typical patient dose values from CT examinations, compare dose between modalities, and communicate radiogenic risks. In practice, effective dose has been used incorrectly, for example, to characterize a CT procedure as a low-dose examination. Organ or tissue doses, not effective doses, are required for assessing the probability of cancer induction in exposed individuals. Monte Carlo simulation is a powerful technique to estimate organ and tissue dose from CT. However, vendors should make available to the research community the required information to model the imaging process of their CT scanners. Personalized dosimetry based on Monte Carlo simulation and patient models allows accurate organ dose estimation. However, it is not user friendly and fast enough to be applied routinely. Future research efforts should involve the development of advanced artificial intelligence algorithms to overcome drawbacks associated with the current equipment-specific and patient-specific dosimetry.

Virtual monoenergetic reconstructions of dynamic DECT acquisitions for calculation of perfusion maps of blood flow: Quantitative comparison to conventional, dynamic 80 kVp CT perfusion

PURPOSE

Investigation of potential improvements in dynamic CT perfusion measurements by exploitation of improved visualization of contrast agent in virtual monoenergetic reconstructions of images acquired with dual-energy computed tomography (DECT).

METHOD

For 17 patients with pancreatic carcinoma, dynamic dual-source DECT acquisitions were performed at 80kVp/Sn140kVp every 1.5 s over 51 s. Virtual monoenergetic images (VMI) were reconstructed for photon energies between 40 keV and 150 keV (5 keV steps). Using the maximum-slope model, perfusion maps of blood flow were calculated from VMIs and 80kVp images and compared quantitatively with regard to blood flow measured in regions of interest in healthy tissue and carcinoma, standard deviation (SD), and absolute-difference-to-standard-deviation ratio (ADSDR) of measurements.

RESULTS

On average, blood flow calculated from VMIs increased with increasing energy levels from 114.3 ± 37.2 mL/100 mL/min (healthy tissue) and 45.6 ± 25.3 mL/100 mL/min (carcinoma) for 40 keV to 128.6 ± 58.9 mL/100 mL/min (healthy tissue) and 75.5 ± 49.8 mL/100 mL/min (carcinoma) for 150 keV, compared to 114.2 ± 37.4 mL/100 mL/min (healthy tissue) and 46.5 ± 26.6 mL/100 mL/min (carcinoma) for polyenergetic 80kVp. Differences in blood flow between tissue types were significant for all energies. Differences between perfusion maps calculated from VMIs and 80kVp images were not significant below 110 keV. SD and ADSDR were significantly better for perfusion maps calculated from VMIs at energies between 40 keV and 55 keV than for those calculated from 80kVp images. Compared to effective dose of dynamic 80kVp acquisitions (4.6 ± 2.2mSv), dose of dynamic DECT/VMI acquisitions (8.0 ± 3.7mSv) was higher.

CONCLUSIONS

Perfusion maps of blood flow based on low-energy VMIs between 40 keV and 55 keV offer improved robustness and quality of quantitative measurements over those calculated from 80kVp image data (reference standard), albeit at increased patient radiation exposure.