Timeliness of splenic time-to-peak affects liver tumor CT perfusion measurements

doi:10.21203/rs.3.rs-1217401/v1

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Research Article

Timeliness of splenic time-to-peak affects liver tumor CT perfusion measurements

https://doi.org/10.21203/rs.3.rs-1217401/v1

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Aim

In liver CT perfusion, the dual-input maximum slope (DI-MS) method is commonly used to estimate perfusion to aid diagnosis of tumors. The DI-MS method relies on a model that assumes the splenic time-to-peak (TTP) separates arterial and portal venous perfusion, and occurs prior to venous perfusion. In this preclinical study, we examined how the timeliness of splenic TTP affects DI-MS perfusion calculations of liver tumors.

Materials and Methods

We analyzed imaging data obtained from 11 New Zealand White rabbits bearing a single implanted VX2 tumor in liver. A liver 320-slice CT perfusion protocol (5,400 images per study) was used to generate images. Times for arterial and portal slopes were recorded, and hepatic arterial perfusion (HAP), portal perfusion (HPP) and perfusion index (HPI) for liver and tumor were separately calculated using manual and automated methods. T-test comparisons and Bland-Altman plot analyses were performed.

Results

Mean tumor TTP occurred at 9.79 s (SD=3.41) and splenic TTP at 9.75 s (SD=4.47, p=0.98). In 3/11 (27.27%) cases, tumor SP occurred prior to spleen (mean difference=1.33 s, SD=1.15 s). In these cases, mean automated HPP values were 43.8% (SD=52.48) higher compared to manually computed ones. There were statistically significant differences between automated and manual methods for normal liver and tumor HPI and HPP (p<0.01 and p<0.0001, respectively), but not HAP values (p=0.125 and p=0.78, respectively). There was also a statistically significant variation between methods for tumor HPP and HPI (p=0.001, respectively).

Conclusion

In 320-slice CT perfusion of liver in this preclinical model, we observed that tumor TTP occurred prior to splenic TTP in 27.27% of tumors in liver. This temporal relationship affects tumor perfusion calculations and should be identified to address potential deviations of model assumptions.

Nuclear Medicine & Medical Imaging

X-ray computed tomography (CT)

liver cancer

perfusion imaging

maximum slope

dual input maximum slope

perfusion compartment model

The liver exhibits unique flow dynamics originating from its dual vascular supplies^1–4. Approximately 25% of the blood supply to the liver is arterial provided by the hepatic artery, and approximately 75% is venous provided by the portal vein from the spleen, gastrointestinal tract and associated organs⁴. By contrast, hypervascular primary and metastatic liver tumors are supplied primarily by the hepatic artery, thus forming the basis of all intra-arterial interventions for these tumors^5–7.

Diagnostic oncologic imaging and imaging-based evaluations of tumor response to therapy rely on accurate blood flow quantification⁸. Non-invasive blood perfusion measurements are conducted by imaging contrast changes occurring in tissues using a series of rapid sequential scans with X-ray computed tomography (CT) following intravenous delivery of iodinated contrast material^8,9. Imaging contrast increases and decreases as the concentration of contrast material within a tissue increases via arterial flow and subsequently decreases with its depletion by venous outflow¹⁰. Direct CT assessment of tissue perfusion is possible because the concentration of contrast material in tissue measured with CT and expressed in Hounsfield units (HU) is directly proportional to the local concentration of contrast material in the tissue¹¹. This quantitative information is unavailable from conventional contrast–enhanced CT where the degree of tumor enhancement at certain time points (i.e., arterial or portal venous phase) is assessed using qualitative criteria¹². Methods to calculate blood perfusion have evolved during the past two decades to include maximum slope (MS), tracer kinetic (single compartment), and deconvolution^7–19.

Described by Peters et al., the MS method was the first mathematical model applied to quantify tissue perfusion²⁰. Miles et al. used it to quantify hepatic perfusion by dynamic contrast-enhanced CT¹⁰, which they validated with dynamic colloid scintigraphy^14,15. The method was later modified by Blomley et al.¹⁶, and its simplicity has motivated its adoption for use in many studies involving quantification of liver perfusion for three decades⁸. The MS method assumes no venous outflow in the tissue, therefore only that portion of the time-density curve (TDC), which occurs before the start of venous outflow, is considered. Using this method, the time of peak enhancement of the tissue region of interest (ROI) is chosen as the beginning of the venous outflow and is defined as end phase (EP, measured in seconds). By definition, after EP the enhancement decreases, occurring only if contrast material is leaving the tissue, signaling venous outflow. Further, a start phase (SP, measured in seconds) is defined as the time when contrast material first enters the tissue, signified by the start of contrast enhancement. A short and fast contrast bolus is administered to ensure validity of the no-venous outflow assumption ^21–25.

In a single supply organ, the maximum slope of the TDC, occurring between the SP and EP, is calculated and divided by the peak enhancement in HU of the supplying artery, to obtain the blood flow per unit volume. This is described by the following equation:

\(\frac{F}{V}=\frac{{\left.\left.\frac{d\left[c\left(t\right)\right]}{dt}\right|max\right|}_{t=SP}^{t=EP}}{\left.a\left(t\right)\right|max}\) 1)

where F is the blood flow rate in the tissue, V is the tissue volume, SP is the start phase, EP is the end phase, \(c\left(t\right)\) and \(a\left(t\right)\) are the concentration of contrast medium in the tissue and artery at time, t.

With liver being a dual blood supply organ, fed by hepatic artery and portal vein, a different approach is used. As the MS method is a derivation of Fick’s principle, a generalized approach enables separate evaluation of each contribution, hepatic (arterial) and portal (venous), to the dual liver blood supply motivating its modification for the liver ²⁶. It is generally assumed that contrast material supplied by the artery accumulates without venous outflow. Therefore, the slope of the TDC in a general sense is determined from the rate of intake of contrast material into the tissue, with the TDC of arterial supply providing information of the contrast concentration. It is also assumed that no contributions to contrast arise from other organs or tissues supplying the portal vein prior to the time of the splenic peak, i.e. peak contrast in spleen. In other words, observed increasing contrast in liver is explicitly assumed to be supplied only by arterial flow. Thus, the time of splenic peak enhancement designates the beginning of the portal phase (SP) and the time of peak liver tissue enhancement designates the end of the portal phase (EP). In subjects having normal blood circulation, peripherally intravenously injected iodinated contrast material arrives first within the hepatic artery, followed by the portal vein ²⁴. Even though contrast medium from the splenic and pancreatic circulation arrives in the portal vein earlier than that from intestinal circulation, the contribution of the portal vein to hepatic enhancement is usually low within the arterial phase of the contrast injection ^25,27.

To account for the dual input, the MS is thus modified to (DI-MS):

\({\left(\frac{F}{V}\right)}_{A}=\frac{{\left.\left.\frac{d\left[c\left(t\right)\right]}{dt}\right|max\right|}_{t=SP}^{t=SM}}{\left.a\left(t\right)\right|max}\) 2)

\({\left(\frac{F}{V}\right)}_{P}=\frac{{\left.\left.\frac{d\left[c\left(t\right)\right]}{dt}\right|max\right|}_{t=SM}^{t=EP}}{\left.p\left(t\right)\right|max}\) 3)

where \({\left(\frac{F}{V}\right)}_{A}\)is the arterial blood flow rate per unit tissue volume, \({\left(\frac{F}{V}\right)}_{P}\)is the portal blood flow rate per unit tissue volume, SM is the time at which spleen attains maximum enhancement, and \(p\left(t\right)\) is the concentration of contrast medium in the portal vein at a time t .

For the DI-MS method to be valid for liver tumors, the SP should occur before the EP of a hypervascular liver tumor. An accurate estimation of the temporal relationship between tumor and splenic peak enhancement depends on various parameters, including individual patient characteristics and image acquisition parameters, curve fitting, motion artifacts, image processing and type of scanner. Moreover, manual and automated selection of SP and EP have been reported to lead to measurement discrepancies of CT perfusion values ^21,22. Modern wide-array volume CT scanners can rapidly (<1 s) and simultaneously scan up to 16 cm of tissue, facilitating simultaneous visualization of the liver (with tumor) and spleen. This introduces fewer respiratory motion and misregistration artifacts ²³. The aim of this study was to evaluate the temporal relationship between tumor peak and splenic peak enhancements for calculation of perfusion parameters using the DI-MS method in a rabbit model bearing implanted tumor in liver, using a wide-array CT perfusion scanner.

Animal Model and Tumor Implantation

Eleven adult male New Zealand White rabbits (Robinson Services, Inc. Mocksville, NC), were used in this study. All rabbits weighed 3.5–4.2 kg prior to imaging. Rabbits were housed in an Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)-accredited facility in compliance with the Guide for the Care and Use of Laboratory Animals ²⁸. All procedures and protocol were approved by the Johns Hopkins Institutional Animal Care and Use Committee (IACUC). Male white New Zealand rabbits were selected for their relevance to intra-arterial procedures and liver tumor imaging as part of ongoing studies of liver cancer. The VX2 cell line was originally purchased from the American Type Culture Collection (ATCC) and has been maintained at the Johns Hopkins University via serial tumor cell implantations in New Zealand White rabbit thighs. As the VX2 tumor cell line is the only rabbit cell line maintained in our laboratory, we tested for species specificity by karyotyping to rule out contamination with human and other non-human cell lines (data available upon request). Karyotyping was routinely performed after thawing each batch of frozen cells and before publication.

At designated time points, individual animals were randomly selected for inclusion in study cohorts prior to implantation of VX2 tumor in the liver for subsequent CT perfusion imaging. Each animal received tumor implantation in the left lobe of the liver as detailed in previous publications ^29–31. The tumors were allowed to grow in the rabbit livers for 13–15 days before imaging ^32,33.

Wide-Array CT Perfusion Protocol

For CT perfusion imaging, each rabbit was first sedated with an intramuscular injection of ketamine (20 mg/kg) and xylazine (8 mg/kg) and subsequently scanned with a wide-array 320-slice clinical CT scanner (Aquilion ONE, Toshiba, Japan). The CT perfusion protocol included at least one non-contrast enhanced volume acquisition, followed by a series of contrast-enhanced CT acquisitions. Isoosmolar contrast iodixanol (1.5 ml/kg, 320 mg I/ml-Visipaque, GE Healthcare Inc., Princeton, NJ) was injected intravenously at a rate of 1 ml/s via a 21G butterfly needle inserted in a marginal ear vein, followed by a saline flush of 7 ml at the same rate. CT perfusion scanning parameters were: FOV = 22cm, KV = 120, mA = 80, slice thickness = 0.5mm, scan delay = 6 s. Based on the above information, the dose exposure for each animal was defined on the monitor to be CTDIvol = 164.70 mGy for a total duration of 17.5 s. Total intermittent scanning time for each rabbit scan was 77 s. Wide-array CT scans were obtained at 2-s intervals for the first 25 s, and every 3 s thereafter for an additional 42 s. Each scan required 0.5 s (one volume acquisition equals a single gantry rotation at a speed of 0.5 s per 360°). A total of 27 acquisitions with 5,400 images were obtained during each CT perfusion study. These were subsequently transferred to a dedicated workstation for image reconstruction and analysis.

Image reconstruction and registration

Following CT acquisition, images were reconstructed with adaptive iterative dose reduction 3D (AIDR 3D, Toshiba Medical Systems, Japan), the manufacturer’s commercial hybrid iterative reconstruction algorithm that enables combining reconstruction in the raw data and image space domains. The reconstruction kernel (FC17), and the reconstruction pixel spacing (0.349 mm) were fixed for all studies.

For image registration, all imaging data from each study were exported to a dedicated workstation using manufacturer provided software (Body Registration; Toshiba Medical Systems, Tochigi, Japan) that corrects the spatially non-consistent position of each organ among the 27-image series of each study. The program adjusted the position of each organ three-dimensionally, i.e., proportionally along any axis and rotationally. For each registered study, a total of 25-image series was subsequently generated for body perfusion analysis.

CT Perfusion measurements

First, TDCs were derived from registered image series by placing circular ROIs over the aorta at the level of the porta hepatis, the main portal vein, the right and left lobes of the liver, as well as over the tumor at the level of longest axial diameter. The size of each ROI was at least 1.0 cm² or larger, except in the portal vein and the aorta, which were set to cover their shortest axis at the level of the hepatic hilum (and with a diameter of 1.0 mm²). Each TDC comprised 25 time points.

Perfusion parameters were calculated by using: a) the dedicated commercial software, on a pixel-by-pixel basis that uses the dual-input MS model (Body Perfusion; Toshiba Medical System, Tochigi, Japan); and, b) MATLAB software (Version 9.0, Mathworks, Natick MA)⁹. For each organ or tissue of interest, care was taken to place each ROI at the same level as for TDC measurements. Flow measurements were expressed in mL per 100 mL per min. Hepatic arterial perfusion (HAP), hepatic portal perfusion (HPP) and hepatic perfusion index (HPI) were calculated for tumor, and left and right hepatic parenchyma.

A MATLAB algorithm was developed to implement the dual-input MS method and calculate the perfusion values. After background subtraction, curve fitting was performed for all the TDCs using a smoothing-spline function in MATLAB. The fitted TDCs were discretized using time steps of 0.1s for calculating maximum value or slope. For each ROI, the SP was selected as the time point where the enhancement reached 10% of the peak enhancement in the corresponding TDC²⁵. Similarly, the EP for each ROI was the time corresponding to the peak enhancement. The HAP and HPP values were calculated using Eq. 3 and 4, respectively. The perfusion value hence obtained has units of s⁻¹, which is then changed to ml ⋅ (min100ml)⁻¹ by multiplying the perfusion value with 100´60 [ml ⋅ s ⋅ (min100ml)⁻¹]. The perfusion index was calculated using:

\(HPI=\frac{HPA}{\left(HPA+HPP\right)}*100\) 4)

Statistical analysis

Statistical analysis included descriptive statistics for continuous variables (mean and standard deviation), t-test comparisons between liver and tumor normally distributed variables, Wilcoxon sign-rank test for non-parametric comparisons, as well as agreement between corresponding measurements obtained with the two different methods with the Bland-Altman analysis³⁴. The Levene’s test was used to test for equality of variances and the Shapiro-Wilk test was used to test for normal distribution. The Pitman’s test of variance for paired samples based on the bivariate normal distribution that compares the variance of the difference with the variance of the average was also performed and reported concurrent with each Bland-Altman plot³⁵. Stata 12 (StataCorp. 2011, Stata Statistical Software: Release 12, College Station, TX: StataCorp LP) was used for the statistical analysis.

Mean time of occurrence of SP for aorta was 5.3 s (SD=2.59 s), portal vein 15 seconds (SD=3.97 s), spleen 9.75 sec (SD=4.47 s), tumor 9.79 s (SD=3.41 s), and liver 17.40 s (SD=1.05 s). There was no statistically significant difference between times of SP for tumor and spleen (p = 0.98). However, in 27.3% of cases (3/11), time of SP of tumor occurred prior to spleen (mean difference in SP, ΔSP = 1.33 s, SD = 1.15 s). Mean time for manual measurement of arterial slope in tumor was 14.86 s (SD = 3.65 s) and for liver 23.19 s (SD = 4.56 s). Mean time for manual measurement of portal slope in tumor was 24.92 s (SD = 7.20 s) and for liver it was 30.02 s (SD = 4.03 s). Mean time of occurrence of EP for tumor was 33.25 s (SD = 13.42 s) and for liver it was 42.42 s (SD = 5.07 s). Measured time-density curve (TDC) characteristics, including start phase, time to measure arterial and portal slope, as well as end phase, for aorta, portal vein, tumor, liver and spleen are shown in Table 1. A typical TDC of aortic, portal venous, splenic, tumor and hepatic enhancement is shown in Figure 1. Typical perfusion maps as calculated with the Body Perfusion software are shown in Figure 2.

Table 1

Time density curves (TDC) start (SP) and end (EP) phases measured for indicated organs.
Variable*	n		Mean				Std. Err.	[95% Conf.
			(s)				(s)	(s)
Aorta
SP	11		5.31				0.78	[3.56	7.05]
EP	11	16.18				0.93		[14.11	18.26]
Portal Vein
SP	11		15.00				1.20	[12.33	17.67]
EP	11	28.27				1.27		[25.45	31.10]
Liver
SP	22		17.40				1.05	[15.22	19.58]
Arterial Slope	22	23.19				0.97		[21.17	25.22]
Portal Slope	22	30.02				0.86		[28.23	31.81]
EP	22	42.42				1.08		[40.17	44.67]
Tumor
SP	11	9.79		1.03				[7.50	12.09]
Arterial Slope	11	14.86		1.10				[12.41	17.32]
Portal Slope	9	24.92		2.40				[19.38	30.45]
EP	11	33.25		4.05				[24.23	42.27]
Spleen
SP	11		9.75		1.35			[6.75	12.76]
EP	11	23.52		1.37				[20.47	26.58]
ΔSP tumor-spleen	11	0.036		-0.319				[0.747	-0.674]
* SP=Start Phase, EP= End Phase, ΔSP=difference in SP times.

Mean tumor HAP using the automated software was 87.5 ml ⋅ (min/100 ml)⁻¹ (SD = 26.61) and mean tumor HAP with MATLAB (manual) calculation was 56.1 ml ⋅ (min/100 ml)⁻¹

(SD = 17.01). Mean liver HAP using the automated software was 48.5 ml ⋅ (min/100 ml)⁻¹ (SD=13.59) and mean liver HAP with manual method was 38.6 ml ⋅ (min/100 ml)⁻¹ (SD=14.43). Overall, from each method it was possible to differentiate tumor from liver for all variables, with the exception of HAP (p = 0.78 for the automated method and p = 0.125 for manual method). Table 2 lists mean HAP, HPP and HPI values calculated with each method for tumor and liver, as well as p-values for comparisons.

Table 2

Comparison of values obtained by Manual or Automated methods.
Analysis Method	Variable*	n	Mean	SD	95% CI		P-value
Automated	Liver HAP	11	90.36	6.56	[75.74	104.98]	0.78
	Tumor HAP	11	87.52	8.02	[69.64	105.40]
	Liver HPP	11	301.53	12.09	[274.58	328.47]	0.003*
	Tumor HPP	11	41.42	5.79	[28.52	54.32]
	Liver HPI	11	173.77	5.83	[160.79	186.75]	0.00001
	Tumor HPI	11	69.35	2.90	[62.88	75.81]
Manual	Liver HAP	11	78.29	10.80	[54.22	102.36]	0.125
	Tumor HAP	11	59.15	5.13	[47.72	70.57]
	Liver HPP	11	268.22	17.09	[230.14	306.30]	0.00001
	Tumor HPP	11	30.45	11.13	[5.66	55.25]
	Liver HPI	11	44.56	4.56	[34.40	54.73]	0.0039
	Tumor HPI	11	71.50	6.89	[56.15	86.85]
* hepatic arterial perfusion (HAP); hepatic portal perfusion (HPP); and hepatic perfusion indices (HPI). HAP and HPP values are expressed in mL per 100 mL per min and HPI in %. P-values are for t-test unpaired comparisons, except for (*), which denotes p-value of Wilcoxon signed-rank test.

In animals with tumor time of SP preceding splenic time of SP, mean automated HPP values were increased by 43.8% (SD = 52.48) compared to manually calculated values. Tumors of these animals showed a negative portal phase duration, as calculated with the manual method, due to the splenic maximum enhancement occurring after the time of the EP of the tumor. By definition, the portal slope is calculated only from that portion of TDC that lies after the time of occurrence of the splenic maximum and before time of venous outflow (EP). Figure 3 shows representative data and corresponding fitted curves of time-dependent contrast dynamics measured in artery, portal vein, spleen, and left liver for a subject with EP of tumor occurring before EP of spleen. TDCs for healthy liver and tumor are plotted separately for comparison. The EP of left and right hepatic lobes occurred at 46.7 s and 45 s, respectively, which is greater than 13 s after the peak time of the spleen 30.5 s. The period between the peak splenic enhancement and EP is the period during which the portal flow dominates and this is when the maximum slope of the TDC curve is recorded. For tumor, EP occurs at 29.6 s, which precedes the time of peak splenic enhancement. This suggests that by the time the portal flow begins to dominate, the tumor TDC has already gone beyond its maximum, signaling the start of the venous outflow. This violates the assumptions upon which the MS method is based and hence the MS cannot be used to calculate the hepatic portal flow of the tumor in such a case. A graph of average and standard deviation for liver and tumor SP, arterial maximum slope, portal maximum slope and EP, for all subjects is shown in Figure 4. Of note, another discrepancy observed was the violation of no venous outflow assumption, which was observed in one subject only. The TDCs for artery, portal vein, spleen, left/right liver and tumor of that subject animal are shown in Figure 5. The slope for the left/right liver and tumor are illustrated and the key time points of the TDCs are also shown. Although the EP for the tumor occurs at 44 s, this is 13.5 s after the peak splenic enhancement (30.5 s), consequently the no-venous outflow assumption has been violated. The TDC for liver attains its global maximum at 44 s and the maximum portal slope is calculated at 39.25 s. The portal slope was calculated where the no-venous outflow assumption was invalid.

Next, we assessed agreement of the two methods for perfusion calculations with the Bland-Altman plot analysis. Mean HAP, HPP and HPI values calculated using each method, as well as mean difference in variance and Pitman’s test for difference in variance, are shown in Table 3. Overall, there was no statistically significant difference in the variances between the two methods for calculations of liver HAP, HPP and HPI (p > 0.08 for all comparisons), as well as tumor HAP (p = 0.09). There was, however a statistically significant difference in the measured variance between methods for tumor HPP and HPI (p = 0.001, respectively), indicating poor agreement between the two methods.

Table 3

Agreement between manual (MATLAB) and automated (Toshiba, Body Perfusion) methods.
				Mean	95% CI for	Pitman’s	Pitman's
	Mean (SD)	Mean (SD)		Difference	Difference	r	p-value
Tumor*	59.14 (17.01)	87.51 (26.61)		-28.373	(-41.55 -15.19)	-0.532	0.092
HAP	59.14 (17.01)	87.51 (26.61)		-28.373	(-41.55 -15.19)	-0.532	0.092
Tumor HPP	30.45 (36.90)	`41.41 (19.20)		-10.964	(-25.06 -3.13)	0.861	0.001
Tumor	71.5 (22.84)	69.34 (9.62)		2.155	(-9.04 -13.35)	0.836	0.001
HPI	71.5 (22.84)	69.34 (9.62)		2.155	(-9.04 -13.35)	0.836	0.001
Left	38.09 (10.74)	51.9 (15.64)		-13.809	(-26.31 -1.30)	-0.359	0.278
Liver
HAP
Left	123.65 (24.21)	156 (28.42)		-32.345	(-49.33 -15.36)	-0.189	0.577
Liver
HPP
Left	23.63 (5.27)	25.18 (7.30)		-1.545	(-6.80 3.71)	-0.325	0.33
Liver
HPI
Right Liver HAP	39.15 (17.91)	45.18	(10.88)	-6.036	(-15.62 -3.54)	0.546	0.082
Right Liver HPP	134.11 (28.34)	150.76 (20.06)		-16.655	(-35.21-1.90)	0.358	0.28
Right Liver HPI	22.28(7.56)	23.01(4.43)		-0.727	(-5.50, 4.04)	0.52	0.101
* hepatic arterial perfusion (HAP), hepatic portal perfusion (HPP) and hepatic perfusion indices (HPI) values of liver (left and right hepatic lobes) and tumor, according to Bland-Altman analyses.

The dual-input maximum slope method has been used in CT perfusion of liver for more than two decades ^9,10,14−16. Earlier CT perfusion studies performed in helical CT scanners were hampered with limited field-of-view (i.e., 3-5 cm) of the scanner in the craniocaudal direction and presence of partial volume effects, among other factors ⁸. A significant recent technological advancement is the development of wide-array CT scanners, which are capable of high temporal frequency imaging over a large tumor or body volume ^36,37. For liver imaging, this is critical as wide-array CT technology enables rapid (<1s) scanning of the whole liver (up to 16 cm), providing temporal homogeneity and minimizing errors related to motion ²³.

The DI-MS method was the first used to quantify hepatic perfusion parameters by dynamic CT ¹⁰. Since this method was first described by Miles et al¹⁰ and modified by Blomley et al ¹⁶, its simplicity has contributed to its use for many studies⁸. Many CT perfusion studies of liver using the DI-MS method have focused primarily on tumor diagnosis and differentiation, successfully demonstrating the ability of this method to distinguish perfusion differences between liver tumors and normal liver⁸. Very few studies have concentrated on evaluating the temporal relationship between SP of spleen and tumor SP, within the context of the DI-MS method. Since the spleen is the reference organ for separating arterial from portal flow with this method, SP timeliness and differences between the spleen and the organ or tissue of interest are crucial for accurately measuring perfusion values.

In this study, we demonstrated that the SP of spleen and therefore, the selection of splenic maximum, as a time point of reference for distinguishing the arterial and portal phases in the liver and tumor, might occur after the SP of tumor and subsequently lead to erroneous

CT perfusion calculations of tumor using the DI-MS method, as this occurrence violates one assumption upon which the DI-MS method is based. Nearly 30% of animals in this study bearing implanted VX2 tumors in liver showed SP occurring before the SP of spleen, leading to invalid calculations of the maximum slope of portal blood flow to tumor. Even in subjects for which peak splenic enhancement occurred before the EP of tumor, we observed very short time intervals (~ 2 sec) between the occurrence of peak splenic enhancement and EP of tumor enhancement.

A potential reason for this observation could be reduced blood supply from portal vein to tumors displaying these properties, which may indicate unexpected tumor growth. The VX2 tumor is a rabbit anaplastic squamous cell carcinoma that typically displays rapid growth and hypervascularity³⁹. Indeed, it is these properties that have made it indispensable to interventional radiologists in preclinical investigations of hepatocellular carcinoma. Thus, a 30% rate of hypovascularity cannot be ruled out from our results, though such an occurrence is unexpected. Regardless, our results suggest that a reliance on vascular perfusion imaging analysis using the DI-MS method, particularly automated analysis sequences that incorporate assumptions without additional checks, may be prone to a biasing error without inclusion of additional data.

Our pilot-scale study is the first to investigate the strength of the relationships and agreement between the manual (MATLAB-based) and automated (manufacturer-provided) methods for three CT perfusion measures (HAP, HPP and HPI) of healthy liver and tumor, in a large animal model. Through deployment of Bland-Altman plots and Pitman’s test for variance, we discovered significant variances in tumor HPI and tumor HPP between the manual and automated methods, indicating a low level of agreement for these two measures.

As a pilot-scale study, it has several limitations. First, a small number of animals were enrolled. Despite the small number, we were able to observe limitations to conventional use of the DI-MS method in almost 30% of subjects. Second, our study was insufficiently powered to directly compare the two methods (manual vs. automated) of perfusion measurements. Third, a pathology gold standard reference was not employed for validation against each method, particularly with regard to vascular density (e.g. CD31 stained immunohistochemistry). We also did not use any other reference organ, such as the kidney¹⁰. We did not use the modified Blomely method, as in both methods, the time of peak splenic concentration is used as a surrogate to distinguish the arterial and the portal phases in the liver¹⁶. Moreover, as Miles et al have noted, measurement of portal flow might be inaccurate for measurement of venous outflow¹⁵. Deconvolution model method was not used for calculation of perfusion values in this study^8,19,38. This method assumes that the shape of R(t) is a plateau with a single exponential wash-out. Though this assumption works well for most organs, it may be unsuitable for organs having complex circulatory pathways such as liver, for which it is preferable to use compartmental analysis. Deconvolution methods are appropriate for measuring lower levels of perfusion (< 20 ml ⋅ (min/100ml)⁻¹) as they are able to tolerate greater image noise due to inclusion of the complete time series of images in calculation. Last, respiratory misregistration could not be completely avoided using the wide-array CT technology. This may have contributed to the observed discrepancy with the no venous outflow assumption, evidenced by the occurrence of several peaks on the tumor TDC in selected cases. Image registration alone may be insufficient to offset respiratory motion and respiratory gating has been proposed. Respiratory gating, however, requires the use of equipment often unavailable in a clinical setting. Some authors have proposed spatial and spatiotemporal filtering to reduce noise in perfusion CT images, and other algorithms to guarantee high fidelity of the time resolution²². Time-independent reference methods should be considered when calculating liver tumor perfusion values in wide-array CT scanners⁹.

CT Perfusion of liver is a functional technique becoming increasingly common as CT scanner technology advances^{8,12,19,23,38}. Wide-array CT technology offers true volumetric coverage of more than 10 cm of liver and tumor in a very short time (<1 s), reducing image degradation to motion artifacts and providing true volumetric coverage. With such techniques, the start phase and peak enhancement of tumors may be more accurately recognized to occur prior to the reference organ time-to-peak, leading to miscalculations of perfusion with a reliance on a single method, i.e. DI-MS, without the benefit of additional data. This study demonstrates that the temporal timeliness between the start phase of an assumed hypervascular liver tumor and spleen may differ from the assumptions upon which the analyzing methods are based. Furthermore, we demonstrated that two methods of calculating the same values purportedly using the same technique, can produce significantly different result. As CT technology advances, it is crucial to reduce variation between perfusion calculation methods, to increase further applicability of the CT perfusion technique.

Acknowledgements

E.L. received funding from the National Institutes of Health, National Cancer Institute grants (R01CA194574 and R21CA161626). R.I. received funding from National Institutes of Health, National Cancer Institute (R01CA194574, 5R01CA247290, and R21CA161626). W.J.S. received funding from the National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering (U01EB018758). R.H. received partial support from the Department of Mechanical Engineering, Johns Hopkins Whiting School of Engineering. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Competing interests

E.L. and R.I. are inventors on patents issued or pending. All patents are assigned to Johns Hopkins University. In addition, R.I. is an inventor on patents assigned to Aduro Biotech; and, he is a member of the Scientific Advisory Board for Imagion BioSystems. All other authors declare no potential conflict of interest.

Author contributions statement

E.L., R.H., and R.I. conceived the experiments, and drafted the manuscript. S.M. and E.L. conducted the experiments in rabbits. E.L., R.I., R.H., A.A., and W.J.S. analyzed results. All authors reviewed and contributed edits to the manuscript.

1. Bradley, S. E., Ingelfinger, F. J., Bradley, G. P. & Curry, J. J. The Estimation of Hepatic Blood Flow in Man. J Clin Invest 24, 890-897, doi:10.1172/JCI101676 (1945).

2. O'Connor, M. K., MacMathuna, P. & Keeling, P. W. Hepatic arterial and portal venous components of liver blood flow: a dynamic scintigraphic study. J Nucl Med 29, 466-472 (1988).

3. Tygstrup, N., Winkler, K., Mellemgaard, K. & Andreassen, M. Determination of the hepatic arterial blood flow and oxygen supply in man by clamping the hepatic artery during surgery. J Clin Invest 41, 447-454, doi:10.1172/JCI104497 (1962).

4. Schenk, W. G., Jr., Mc, D. J., Mc, D. K. & Drapanas, T. Direct measurement of hepatic blood flow in surgical patients: with related observations on hepatic flow dynamics in experimental animals. Ann Surg 156, 463-471 (1962).

5. Liapi, E. & Geschwind, J. F. H. Transcatheter and ablative therapeutic approaches for solid malignancies. J Clin Oncol 25, 978-986, doi:Doi 10.1200/Jco.2006.09.8657 (2007).

6. Cady, B. & Oberfield, R. A. Arterial infusion chemotherapy of hepatoma. Surg Gynecol Obstet 138, 381-384 (1974).

7. Swinton, N. W., Cady, B., Nahra, K. S. & Watkins, E., Jr. Arterial infusion chemotherapy of liver metastases arising from rectal and colonic cancer. Proc R Soc Med 63 Suppl, 21-23 (1970).

8. Kim, S. H., Kamaya, A. & Willmann, J. K. CT perfusion of the liver: principles and applications in oncology. Radiology 272, 322-344, doi:10.1148/radiol.14130091 (2014).

9. Materne, R. et al. Non-invasive quantification of liver perfusion with dynamic computed tomography and a dual-input one-compartmental model. Clin Sci (Lond) 99, 517-525 (2000).

10. Miles, K. A. Measurement of tissue perfusion by dynamic computed tomography. Br J Radiol 64, 409-412, doi:10.1259/0007-1285-64-761-409 (1991).

11. Hindmarsh, T. Elimination of water-soluble contrast media from the subarachnoid space. Investigation with computer tomography. Acta Radiol Suppl 346, 45-49 (1975).

12. Murakami, T. & Tsurusaki, M. Hypervascular benign and malignant liver tumors that require differentiation from hepatocellular carcinoma: key points of imaging diagnosis. Liver Cancer 3, 85-96, doi:10.1159/000343864 (2014).

13. Cuenod, C. et al. Early changes in liver perfusion caused by occult metastases in rats: detection with quantitative CT. Radiology 218, 556-561, doi:10.1148/radiology.218.2.r01fe10556 (2001).

14. Miles, K. A., Hayball, M. & Dixon, A. K. Colour perfusion imaging: a new application of computed tomography. Lancet 337, 643-645 (1991).

15. Miles, K. A., Hayball, M. P. & Dixon, A. K. Functional images of hepatic perfusion

obtained with dynamic CT. Radiology 188, 405-411, doi:10.1148/radiology.188.2.8327686 (1993).

16. Blomley, M. J. et al. Liver perfusion studied with ultrafast CT. J Comput Assist Tomogr 19, 424-433 (1995).

17. Axel, L. Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis. Radiology 137, 679-686, doi:10.1148/radiology.137.3.7003648 (1980).

18. Axel, L. Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique. Invest Radiol 18, 94-99 (1983).

19. Ng, C. S. et al. Effect of dual vascular input functions on CT perfusion parameter values and reproducibility in liver tumors and normal liver. J Comput Assist Tomogr 36, 388-393, doi:10.1097/RCT.0b013e318256b1e2 (2012).

20. Peters, A. M. et al. Noninvasive measurement of blood flow and extraction fraction. Nucl Med Commun 8, 823-837 (1987).

21. D'Antò, M. et al. in Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on. 1-4 (IEEE).

22. Romano, M., D’Antò, M., Bifulco, P., Fiore, F. & Cesarelli, M. Robustness to noise of arterial blood flow estimation methods in CT perfusion. BMC Research Notes 7, 540, doi:10.1186/1756-0500-7-540 (2014).

23. Liapi, E., Mahesh, M. & Sahani, D. V. Is CT perfusion ready for liver cancer treatment evaluation? J Am Coll Radiol 12, 111-113, doi:10.1016/j.jacr.2014.10.007 (2015).

24. Bae, K. T., Heiken, J. P. & Brink, J. A. Aortic and hepatic contrast medium enhancement at CT. Part I. Prediction with a computer model. Radiology 207, 647-655, doi:10.1148/radiology.207.3.9609886 (1998).

25. Bae, K. T. Intravenous contrast medium administration and scan timing at CT: considerations and approaches. Radiology 256, 32-61, doi:10.1148/radiol.10090908 (2010).

26. Fick, A. Ueber die Messung des Blutquantums in den Herzventrikeln. Sitz. Physik. Med. Ges 2, 16 (1870).

27. Fleischmann, D. How to design injection protocols for multiple detector-row CT angiography (MDCTA). Eur Radiol 15 Suppl 5, E60-65 (2005).

28. National Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals. Guide for the Care and Use of Laboratory Animals. 8th edn, (National Academies Press (US), 2011).

29. Buijs, M. et al. Quantitative proton MR spectroscopy as a biomarker of tumor necrosis in the rabbit VX2 liver tumor. J Vasc Interv Radiol 22, 1175-1180 (2011).

30. Chen, J. H. et al. Induction of VX2 carcinoma in rabbit liver: comparison of two inoculation methods. Lab Anim 38, 79-84 (2004).

31. Lee, K. H. et al. Distribution of iron oxide-containing Embosphere particles after transcatheter arterial embolization in an animal model of liver cancer: evaluation with MR imaging and implication for therapy. J Vasc Interv Radiol 19, 1490-1496 (2008).

32. Yamamoto, A. et al. Evaluation of tris-acryl gelatin microsphere embolization with monochromatic X Rays: comparison with polyvinyl alcohol particles. J Vasc Interv Radiol 17, 1797-1802 (2006).

33. Hong, K., Kobeiter, H., Georgiades, C. S., Torbenson, M. S. & Geschwind, J. F. Effects of the type of embolization particles on carboplatin concentration in liver tumors after transcatheter arterial chemoembolization in a rabbit model of liver cancer. J Vasc Interv Radiol 16, 1711-1717 (2005).

34. Bland, J. M. & Altman, D. G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307-310 (1986).

35. Pitman, E. A note on normal correlation. Biometrika 31, 9-12 (1939).

36. Kandel, S. et al. Whole-organ perfusion of the pancreas using dynamic volume CT in patients with primary pancreas carcinoma: acquisition technique, post-processing and initial results. European radiology 19, 2641-2646, doi:10.1007/s00330-009-1453-z (2009).

37. Motosugi, U. et al. Multi-organ perfusion CT in the abdomen using a 320-detector row CT scanner: Preliminary results of perfusion changes in the liver, spleen, and pancreas of cirrhotic patients. European Journal of Radiology 81, 2533-2537 (2012).

38. Kanda, T. et al. CT hepatic perfusion measurement: comparison of three analytic methods. Eur J Radiol 81, 2075-2079, doi:10.1016/j.ejrad.2011.07.003 (2012).

39. Parvinian, A., Casadaban, L.C., Gaba, R.C. Development, growth, propagation, and angiographic utilization of the rabbit VX2 model of liver cancer: a pictorial primer and “how to” guide. Diagn. Interv. Radiol. 20(4), 335-340 (2014).

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Timeliness of splenic time-to-peak affects liver tumor CT perfusion measurements

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Abstract

Aim

Materials and Methods

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Introduction

Materials And Methods

Animal Model and Tumor Implantation

Wide-Array CT Perfusion Protocol

Image reconstruction and registration

CT Perfusion measurements

Statistical analysis

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References

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