Reproducibility of VPCT Parameters in the Normal Pancreas: Comparison of Two Different Kinetic Calculation Models Sascha Kaufmann, MD, Maximilian Schulze, MD, Thomas Horger, MSc, Aenne Oelker, MSc, Konstantin Nikolaou, MD, Marius Horger, MD Rationale and Objectives: To assess the reproducibility of volume computed tomographic perfusion (VPCT) measurements in normal pancreatic tissue using two different kinetic perfusion calculation models at three different time points. Materials and methods: Institutional ethical board approval was obtained for retrospective analysis of pancreas perfusion data sets generated by our prospective study for liver response monitoring to local therapy in patients experiencing unresectable hepatocellular carcinoma, which was approved by the institutional review board. VPCT of the entire pancreas was performed in 41 patients (mean age, 64.8 years) using 26 consecutive volume measurements and intravenous injection of 50 mL of iodinated contrast at a flow rate of 5 mL/s. Blood volume(BV) and blood flow (BF) were calculated using two mathematical methods: maximum slope + Patlak analysis versus deconvolution method. Pancreas perfusion was calculated using two volume of interests. Median interval between the first and the second VPCT was 2 days and between the second and the third VPCT 82 days. Variability was assessed with within-patient coefficients of variation (CVs) and Bland–Altman analyses. Interobserver agreement for all perfusion parameters was calculated using intraclass correlation coefficients (ICCs). Results: BF and BV values varied widely by method of analysis as did within-patient CVs for BF and BV at the second versus the first VPCT by 22.4%/50.4% (method 1) and 24.6%/24.0% (method 2) measured in the pancreatic head and 18.4%/62.6% (method 1) and 23.8%/28.1% (method 2) measured in the pancreatic corpus and at the third versus the first VPCT by 21.7%/61.8% (method 1) and 25.7%/34.5% (method 2) measured also in the pancreatic head and 19.1%/66.1% (method 1) and 22.0%/31.8% (method 2) measured in the pancreatic corpus, respectively. Interobserver agreement measured with ICC shows fair-to-good reproducibility. Conclusions: VPCT performed with the presented examinational protocol is reproducible and can be used for monitoring purposes. Best reproducibility was obtained with both methods for BF and with method 2 also for BV data for both follow-up studies. Key Words: Volume perfusion computed tomography (VPCT); perfusion parameters; pancreas perfusion; deconvolution method; maximum slope; Patlak analysis. ªAUR, 2015

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olume perfusion computed tomography (VPCT) is an imaging technique enabling the acquisition of functional perfusion-based data complementary to classical morphologic CT diagnostics (1–8). Some authors have demonstrated that perfusion measurements can be easily integrated in a normal whole-body CT examinational protocol meant to acquire information about the course of oncologic and inflammatory diseases (9,10). For the purpose of performing perfusion-based monitoring of all these disorders, reproducibility of functional parameters is imperative. Some previous reports have dealt with this issue presenting

Acad Radiol 2015; 22:1099–1105 From the Department of Diagnostic and Interventional Radiology, Eberhard €bingen, Germany (K.S., S.M., Karls University, Hoppe-Seyler-St 3, 72076 Tu € t Mu €nchen, Garching, Germany (H.T., N.K.,H.M.); and Technische Universita O.A.). Received November 16, 2014; accepted April 29, 2015. Address correspondence to: S.K. e-mail: [email protected] ªAUR, 2015 http://dx.doi.org/10.1016/j.acra.2015.04.005

in part contradictory data and emphasizing the role of using standardized examination protocols (11–13). Moreover, the calculation kinetic models (compartmental vs. deconvolution) for perfusion quantification differ, and their strengths and limitations have been already reported (14,15). These models quantify perfusion parameters and allow pixel-by-pixel calculation of a range of physiological parameters (blood flow [BF], blood volume [BV], mean transit time [MTT], time to peak, and k-trans or flow extraction product, defined as the sum of flow within the microvasculature and capillary permeability) and depiction as parametric maps. The pancreas is a common site for primary and secondary tumors and for inflammatory diseases, therefore, an important anatomic site in which to evaluate appropriate imaging techniques. In addition, the pancreas represents a less mobile organ that allows optimized motion correction and is lying adjacent to the aorta, which is preferentially used, as the arterial input vessel. Our objectives were to assess the variability of perfusion CT measurements in the normal pancreas tissue measured twice by repeat VPCT within 48 hours and once again 1099

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3 months later to evaluate the robustness of results delivered by two different kinetic perfusion models. MATERIALS AND METHODS Patients and Target Lesions

Our prospective study for liver response monitoring to local therapy in patients experiencing unresectable hepatocellular carcinoma was approved by the institutional review board, written informed consent was obtained from all patients, and the study complied with Health Insurance Portability and Accountability Act regulations. Liver measurements included also the pancreas, so we retrospectively analyzed pancreas perfusion in terms of data reproducibility. Institutional ethical board approval for retrospective analysis of pancreas perfusion was obtained separately for all patients. Inclusion criteria were perfusion of the hole pancreas, normal pancreas function based on the evaluation of amylase and lipase, normal CT morphology, and exclusion of chronic or acute pancreatitis based on laboratory analysis, clinical examination, and CT examination. Exclusion criteria were known chronic or acute pancreatitis, alcohol abuse, elevated amylase and lipase, and morphologic disorders of the pancreas like tumor, fatty, or fibrotic degeneration of the pancreas. Furthermore, patients were excluded from evaluation if the pancreas shows any morphologic disorders or if amylase and lipase showed an increase after transarterial chemoembolisation (TACE). A total of 41 patients (36 men and 5 women; mean age, 64.8 years; range, 37–78 years, respectively) from a cohort of 51 patients were eligible for retrospective perfusion data analysis. CT Perfusion Scanning Technique

All examinations were performed on a 128-row CT scanner (Somatom Definition AS+, Siemens Healthcare, Forchheim, Germany). The CT protocol consisted of a nonenhanced abdominal low-dose CT (40 mAs, 100 kV, slice thickness (SL), 5.0 mm; collimation, 128  0.6 mm; tube rotation time, 0.5 seconds; and pitch, 0.6), which was obtained to localize the liver porta. Subsequently, a VPCT of the tumor using adaptive spiral scanning technique was performed. Perfusion parameters were 80 kV, 100 mAs for patients 70 kg, collimation of 64  0.6 mm with z-flying focal spot (Z coverage 6.9 cm), and 26 CT-whole coverages of the liver tumor. For measurements of tissue BF, an acquisition time of 45 seconds comprising the ‘‘perfusion phase’’ (ie, first pass of contrast material) is advisable (16). We used a total acquisition time of 40 seconds. To assess the optimal delay time for contrast agent administration, we first performed a test bolus using 6-mL iodine contrast agent and starting then the perfusion measurements accordingly. Hence, we usually had only 1–2 nonenhanced series in the protocol. Thus, 1100

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we used a temporal sampling frequency of 1.5 seconds in helical shuffle mode. Patients were asked to resume shallow breathing for the entire duration of the study. Fifty milliliter Ultravist 370 (Bayer Vital Leverkusen, Germany) was injected at a flow rate of 5 mL/s in an antecubital vein followed by a saline flush of 50-mL NaCl at 5 mL/s. Contrast medium was administered by using a dual-head pump injector (Stellant, Medtron, Saarbruecken, Germany). One set of axial images with a slice thickness of 3 mm for perfusion analysis was reconstructed without overlap, using a smooth tissue convolution kernel (B10f). All images were transferred to an external workstation (Multi-Modality Workplace, Siemens) for analysis. CT Perfusion Analysis

All data sets were transferred to a dedicated workstation (Syngo MMWP, VE 36A, Siemens Healthcare, Forchheim, Germany), and quantitative data evaluation was performed with a commercial software (Syngo Volume Perfusion CT Body; Siemens Healthcare). Data evaluation was performed by 2 experienced radiologists with 20-year and 5-year experience on the field of oncological imaging including perfusion studies (M.H. and S.K.). Automated motion correction and four-dimensional (4D) noise reduction of all data sets were applied using an integrated motion correction algorithm with nonrigid deformable registration for anatomic alignment. 4D noise reduction is a frequency-dependent filter applied on the dynamic data: high spatial frequencies which do not contain the relevant perfusion information are averaged to improve the signal-to-noise ratio, whereas low spatial frequencies containing the dynamic perfusion information are untouched to keep the maximal perfusion information. These data were supplied by the vendor. The region of interest (ROI) manually placed in the abdominal aorta for measuring the arterial input function (AIF) was as large as possible to avoid averaging with the surrounding tissue signal and was at mean 1.9 cm2. Pancreas parenchymal perfusion was calculated using two volume of interest (VOI) sets one for the pancreatic head and one for the pancreatic tail. The VOIs were chosen as large as possible and placed to avoid vessels and artifacts. The mean VOI size for perfusion measurements of the pancreas was 0.2 cm3. The VOIs were reproduced automatically at the second and the third VPCT. For perfusion calculation, we used two mathematical calculation methods (models): maximum slope (BF) + Patlak analysis (BV) versus deconvolution model (BF and BV). These two different kinetic calculation software programs were used which are both Food and Drug Administration approved and are part of the postprocessing software recommended by the vendor. The first one-compartmental model uses the maximum slope method for calculation of BF and the Patlak analysis (two compartments) for determining the BV. Results were compared with those obtained with the so-called deconvolution method.

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Statistical Analysis

Summary statistics of the perfusion CT parameters BF and BV as well as of their standard deviations (SDs) are reported as means, medians and range, and by method. CT parameters were assumed to be log-normally distributed because pancreas perfusion parameters follow log-normal distributions, as is typical for biologic systems. We further assume that the differences are randomly distributed and mutually independent and uniformly bounded. Thus, we can conclude, using the central limit theorem, that the sum of differences is normally distributed. The measurements represent defined points in time, the time between which is normally distributed. If we assume changes in the pancreas perfusion, as measured by the CT parameters, to be exponential, we obtain the assumption of logarithmic-normal distribution. CT parameters were transformed to the logarithmic scale for the calculation of the within-patient coefficient of variance (wCV) for each measure at each location at each stage for each method. The wCV and its confidence interval were calculated from the square root of the within-patient variation (wSD) and its confidence interval using the formula wCV = exp(wSD) 1. For all scenarios, the wCVs are given for the normal values (not for the standard values), the smaller the wCV the better. In addition, the variances at the second and the third VPCT are reported to describe how much the measurements vary in total. We calculated also intraclass correlation coefficients (ICCs). The ICC is a measure of reproducibility of replicate measures from the same subject. It ranges between 0 and 1, with ICC = 0 indicating no reproducibility and ICC = 1 indicating perfect reproducibility. It is defined as the ratio of the between-subject variance divided by the sum of the between-subject and the within-subject variance (total variance). According to Rosner (17) values $0.75 indicate excellent reproducibility, values between 0.4 and 0.75 indicate fairto-good reproducibility, and values #0.4 indicate poor reproducibility. The calculations were performed using Matlab R2013b. RESULTS All VPCT data sets were measurable with both kinetic perfusion calculation models (Fig 1). The median interval between the first, the second and the third VPCT was 2 (range, 1–69 days) and 82 (range, 28– 116 days). The calculated mean radiation exposure for liver perfusion measurements was 7.5 mSv. Interobserver agreement measured with ICC shows fairto-good agreement between all measurements (range, 0.41– 0.72). A complete overview of ICC (in %) is tabulated on Table 1. Results of Maximum Slope and Patlak Kinetic Modeling

BF and BV values obtained in the pancreatic head and tail at the first VPCT yielded similar results with a mean value of

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73.2/18.6 (SD, 9.1/4.0) in the pancreatic head, and a mean value of 78.3/16.5 (SD, 8.4/3.4) in the pancreatic tail. BF and BV values obtained in the pancreatic head and tail at the second and the third VPCT yielded following results with a mean value of 75.9/19.5 (SD, 9.4/4.9), respectively, 71.5/17.8 (SD, 9.4/3.6) in the pancreatic head, and a mean value of 79.3/20.6 (SD, 8.8/5.2), respectively, 75.8/18.4 (SD, 7.3/3.0) in the pancreatic tail. A complete overview of mean values including ranges is tabulated on Table 1. Results of Deconvolution Kinetic Method

BF and BV values obtained in the pancreatic head and tail at the first VPCT yielded similar results with a mean value of 116.4/22.9 (SD, 24.3/4.0) in the pancreatic head and a mean value of 123.1/22.2 (SD, 25.8/3.0) in the pancreatic tail. BF and BV values obtained in the pancreatic head and tail at the second and the third VPCTyielded following results with a mean value of 118.0/22.7 (SD, 33.0/5.5), respectively, 111.3/23.7 (SD, 29.6/4.5) in the pancreatic head, and a mean value of 125.3/23.4 (SD, 29.8/3.4), respectively, 112.8/24.1 (SD, 22.4/3.2) in the pancreatic tail. A complete overview of mean values including ranges is tabulated on Table 1. DISCUSSION Our study shows that variability in BF measurements at repeatVPCTof the normal pancreas is in the range of about 30%, irrespective of the calculation model used. Using the deconvolution model, BV measurements yielded also almost equally good results. All in all, the deconvolution method proved to be more robust with acceptable deviation of results at follow-up. For the assessment of variability values, we find a first orientation in literature values (18). According to the authors, comparatively few studies have assessed the reproducibility of tumor perfusion CT measurements so far. They suggest that wCVs for single-breath-hold perfusion CT data are typically in the range of 70%–90% for the lung (18). Within-patient CVs of 23% for BF, 14% for BV, 35% for MTT, and 17% for permeability have been reported in a study of 10 patients with rectal cancer (19). It has to be noted that this study evaluated an organ in an anatomic location with higher differences in tissue attenuation which makes segmentation for volume perfusion measurements more robust. Our results lie within this range. Perfusion measurements have advanced to a potential tool in the functional imaging, in particular, in the oncologic diagnosis where accurate response to novel antiangiogenic drugs has become imperative. Many imaging and perfusion techniques are in use for this task like dynamic contrast-enhanced magnetic resonance imaging, contrastenhanced ultrasound, perfusion CT, and nuclear medicine modalities like 15O-labeled water positron emission tomography (15O-PET) or radioactive microspheres and appropriate kinetics modeling exists for all these techniques (20–23). However, each 1101

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Figure 1. (a–f): 52-year-old female patient with hepatocellular carcinoma (child A) due to chronic hepatitis C. Maximum intensity projection from volume perfusion computed tomography (a), arterial phase computed tomography (b), and blood flow and blood volume calculated using two mathematical modeling methods: maximum slope + Patlak analysis (c,e) versus deconvolution models (d,f) are shown. The arrow shows an example how perfusion measurements have been performed (eg, in the pancreatic tail). Same VOI size was used for the measurements in the pancreatic head. (Color version of figure is available online.)

of the most commonly used kinetic models for perfusion calculation has its known limitations. The maximum slope (one compartment) model which estimates perfusion using the peak height of the tissue concentration curve normalized to the AIF assumes that there is no relevant venous outflow from the tumor or organ to be measured at the time of the maximum slope of the tissue density curve because otherwise, a significant venous outflow would lead to an underestimation of BF (14). For this purpose, we adapted our protocol by using a short acquisition time of 40 seconds. The Patlak twocompartment kinetic model allows the calculation of BV. Both these methods are sensitive to noise, so that low-energy protocols may affect final results leading to miscalculation. Hence, noise might have influenced perfusion measurements and their reproducibility in our study, as we performed VPCT using a reduced energy protocol (80 kV and 100/120 mAs). On the other hand, the deconvolution analysis uses arterial and tissue time–concentration curves to calculate the impulse residue function, which represents the fraction of contrast medium that remains in the tissue as time evolves after bolus injection (14). This method assumes uniform distribution of contrast material within extravascular space of tissue and no back flux of contrast material from the interstitial space into capillaries, 1102

which might also generate small calculation errors. In addition, a lot of other variables exist which may severely influence results of perfusion CT–like concentration and viscosity of contrast agent (24), circulation time which is patient dependent, volume and flow of contrast material and saline chaser that is injected, and CT-protocol–related parameters like kV, mAs, and hardware characteristics (table speed, motion, and noise correction algorithms). Using a standardized CT-perfusion imaging protocol including volume, flow, concentration, and temperature of applied contrast medium for short-term follow-up, we assumed that results should be comparable from a technical point of view. Because of known heterogeneity, for example, of organ (pancreas) perfusion, volume sampling is expected to yield more confident and reproducible data. To pay tribute to the necessity of keeping radiation exposure in a lower range, we used a reduced-dose protocol (80 kV and 100/120 mAs). The impact of reduced-dose examinational CT-perfusion protocols has been investigated by Watanabe et al. (25) who reported no significant difference in the analysis of results between ultralowdose and normal-dose CT-perfusion parameters. Newly, perfusion-CT data of pancreas examinations have been published using a similar reduced-dose (low kV) examinational protocol reporting a good image quality (15). To minimize noise by

50.4 (37.5–64.5) 61.8 (45.6–79.8) 69.8 (51.2–90.6) 24.0 (18.3–30.0) 34.5 (26.5–43.5) 33.2 (26.1–41.8) 62.6 (46.2–80.9) 66.1 (48.6–85.6) 74.4 (54.4–97.0) 28.1 (21.4–35.3) 31.8 (24.1–40.0) 28.7 (21.8–36.1)

61.3 54.7 50.2 63.5 47.4 54.1 54.6 43.3 30.8 51.0 48.5 46.7

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Deconvolution

Maximum slope + Patlak Pancreas tail

Deconvolution

BF, blood flow; BV, blood volume; CI, confidence interval; CV, coefficient of variation; ICC, intraclass correlation coefficient.

18.6 (4.2–52.7) 19.5 (3.4–59.0) 17.8 (1.73–60.3) 22.9 (2.9–41.6) 22.7 (4.0–36.0) 23.7 (4.6–37.0) 16.5 (1.2–42.3) 20.6 (4.4–56.0) 18.4 (2.0–56.0) 22.2 (8.3–37.2) 23.4 (9.5–36.5) 24.1 (7.1–41.0) 68.3 71.5 70.6 45.2 50.3 42.4 63.5 70.7 53.4 50.2 56.7 40.8 22.4 (17.1–28.0) 21.7 (16.6–27.0) 21.2 (16.1–26.4) 24.6 (18.8–30.8) 25.7 (19.6–32.2) 28.4 (21.6–35.7) 18.4 (14.1–22.9) 19.1 (14.6–23.7) 27.0 (20.5–33.8) 24.0 (18.3–29.9) 22.0 (16.8–27.4) 31.4 (23.7–39.5) 73.2 (21.0–116.6) 75.9 (27.1–112.0) 71.5 (23.1–120.0) 116.4 (57.6–193.9) 118.0 (72.0–267.9) 111.3 (41.1–183.2) 78.3 (34.0–101.9) 79.3 (23.8–117.0) 75.8 (26.2–134.7) 123.1 (60.4–192.8) 125.3 (45.04–237.1) 112.8 (41.1–183.4) 1 2 3 1 2 3 1 2 3 1 2 3 Maximum slope + Patlak Pancreas head

ICC, % Within-Patient CV, % (95% CI) BV ICC, % Within-Patient CV, % (95% CI) BF Point of Time Analysis Method Location

TABLE 1. Overview of Mean Values Including Ranges and the Within-Patient Coefficient of Variance, Its Confidence Interval, and the ICC (in %) for BF and BV Measured at Three Different Time Points with Two Different Analysis Methods

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low-dose VPCT protocol, we also used a softer reconstruction kernel (B10f), a reconstruction slice thickness of 3 mm and a 512  512 image matrix. Notably, our examinational protocol permitted continuous shallow breathing, which theoretically makes data acquisition more susceptible to motion-related artifacts. Admittedly, this impediment is expected to play a minor role in case of the pancreas, which knowingly is not that susceptible to motion. Using this protocol, our results showed best reproducibility of measured perfusion parameters for BF, irrespective of the calculation model used. Notably, there was even a trend toward less variability for measurements in the pancreatic tail versus the pancreatic head which in our opinion reflects the local anatomy with less influence of adjacent pulsatile large vessels and particularly liver motion on the pancreas own motion and the fact that anatomic structures (retroperitoneal fatty tissue) next to the pancreas also show less shifting and also less vascularization. Concerns about artifactual impression of rapid inflow of contrast agent generated by sudden changes from lower densities, for example, of normal mesenteric fatty tissue to pancreas tissue density could not be confirmed in case of BF in our study (13). Other influencing factors like the age-dependent (26) and physiological changes in pancreatic perfusion dependent on food intake are also expected to play a not so minor role in this setting. This latter aspect was not considered in this monitoring study for the time points 2 and 3 (for the first VPCTexamination, patients had to fasten as it was performed immediately before TACE). At follow up, VPCTwas used for reassessment of hepatic pathology (eg, relapse and need for repeat-TACE) and therefore patients were not explicitly advised to fasten before VPCT. Not at least, even the circulation time has an impact on the organ perfusion that cannot be easily evaluated. Moreover, a major aspect that should be discussed in this given clinical setting that could in part explain the ranges of variability of our results is the particular character of this TACE monitoring study. Knowingly, the pancreas receives blood from branches of both the celiac and the superior mesenteric artery. The superior pancreaticoduodenal artery and inferior pancreaticoduodenal artery run along the front and the back surfaces of the head of the pancreas, whereas the splenic artery runs along the top margin of the pancreas and supplies the neck, body, and tail of the pancreas. The gastroduodenal artery represents the point where the proper hepatic artery arises. Depending on the location of the hepatocellular carcinoma that should undergo TACE and also on the embolization technique used (eg, complete stasis as end point) and the particle size, some degree of reflux (particle redirection) toward the pancreas supplying arteries might occur. This side effect generates usually no or only mild symptoms, but is expected to impact the organ perfusion, in particular, in the pancreatic head. This is presumably the main explanation for the better reproducibility of results in the pancreatic body and tail versus head in our study. In the consequence, an even more accurate reproducibility of repeated perfusion measurements might be postulated in healthy volunteers who would 1103

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be in support of the use of this technique. The mean BF values measured in our cohort were in line with previous reports on this issue showing expectedly a great interindividual variation and higher values for the deconvolution model (12,27). They differ in part from other previous works that used a lower temporal resolution (3 seconds), which is essential for accurate calculation of BF using a one-compartment model (28). Perfusion measurements lack generally a gold standard with one exception 15O-labeled water (H215O) which has been used to noninvasively monitor tumor perfusion. However, even data using H215O-PET showed some degree of variability between test and retest values recommending, for example, that a change of less than 18% in tumor perfusion and 32% in VT (

Reproducibility of VPCT parameters in the normal pancreas: comparison of two different kinetic calculation models.

To assess the reproducibility of volume computed tomographic perfusion (VPCT) measurements in normal pancreatic tissue using two different kinetic per...
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