Preliminary Protocol for Measuring the Reproducibility and Accuracy of Flow values on Digital PET/CT Systems in [15O]H2O Myocardial Perfusion Imaging Using a Flow Phantom

doi:10.21203/rs.3.rs-2699633/v1

Background:

Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols for assessing this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI.

Materials and Methods:

We implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [¹⁵O]H₂O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [¹⁵O]H₂O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [¹⁵O]H₂O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems.

Results:

The input time activity curves (TAC) showed dependency on the bolus injection curves. The measurements performed on the DMI-20 system produced Qin and Qout values equal to each other as well as to the reference flow value across all test-retest measurements. Also, all DMI-20 repeatability differences were below 15 %. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded the 15 % difference in half (6/12) of the measurements.

Conclusions:

This preliminary study proposes a protocol for measuring the accuracy and reproducibility of flow between different injector and PET systems. Our results suggest that flow values can be reproduced on DMI-20 or Vision-600 systems and with two different injector systems, with less than 15 % error. The most significant factor contributing to the variation was the injected bolus, which directly affects the phantom input TAC. In order to extend the protocol to a multi-center setting, a standardized and repeatable bolus injection protocol needs to be designed.

Several factors may decrease the accuracy of Positron Emission Tomography (PET) quantification within one system or between different PET systems. These factors involve injection protocols, data acquisition, different PET detector technologies, software implementations as well as reconstruction and data corrections [1–3]. The contribution of these factors is an additional challenge in myocardial perfusion imaging (MPI) quantification, as it relies on kinetic modelling to extract the myocardial blood flow (MBF) in absolute quantitative values, requiring accurate and reproducible image quantification.

The European Association of Nuclear Medicine Research Ltd. (EARL) has proposed guidelines for PET acquisition harmonization in order to achieve reproducible [¹⁸F]FDG tumor standardized uptake value (SUV) quantification [4, 5]. Similar procedural guidelines have been proposed for MPI, which frequently uses short lived tracers, such as [¹⁵O]H₂O [6]. Implementing harmonization measures for MPI is extremely important. Any test used for clinical decision-making and patient management needs to be accurate and should have adequate test–retest repeatability to enable management decisions for individual patients [2, 7]. Currently, only a few centers frequently perform MPI PET for the diagnosis of suspected coronary artery disease. However, as MPI PET is increasingly being used for the detection of myocardial ischemia [6], there is a growing need for more consistent and standardized evaluation of the technical factors contributing to the variation of MBF values in MPI studies [3].

The lack of suitable test objects has hindered the implementation of standardized protocols for harmonization of MPI. Phantoms used in PET harmonization studies are usually static phantoms, such as the National Electrical Manufacturers Association (NEMA) image quality phantom [8] or simple cylindrical phantoms [9]. Very few suitable phantoms have been available for harmonization purposes in dynamic MPI until recently. Gabrani-Juma et al. validated a flow phantom simulating MBF for MPI PET standardization purposes where image-derived flow values can be evaluated against a ground truth reference value [10]. This flow phantom offers an optimal basis for creating standardized protocols for assessing the accuracy and reproducibility of MBF values across PET systems as well as for investigating the contribution of purely technical factors on kinetic modelling and MBF quantitation.

Several studies have investigated harmonization measures of accurate SUV quantification for oncological [¹⁸F]FDG and neurological studies. Akamatsu et al. list several investigations of the latest PET harmonization strategies [11]. For example, SUV harmonization can be based on the image uniformity and spatial resolution with tracer-specific phantoms [12]. EARL has also performed extensive work for evaluating reproducible SUV quantitation and accreditation, and the investigations include harmonization protocols with standard acquisition parameters [5]. Furthermore, SUV (more specifically, SUVmean, SUVmax, and SUVpeak) has been reported to have 10 % coefficient of variation [13] and 27 % reproducibility rate for SUVmean and SUVpeak, and 33 % for SUVmax [14].

Multiple test-retest studies have also evaluated the reproducibility of MPI PET with [¹³N]NH₃ as well as [⁸²Rb] [15–17]. In addition, in [¹⁵O]H₂O MPI PET the repeatability for stress MBF values has been reported to be 27 % [18] and 25 % [19]. However, to the best of our knowledge, no studies have systematically investigated the contribution of technical factors on the test-retest reproducibility and accuracy with a calibrated standard. Therefore, the first step for implementing systematic harmonization protocols should be to evaluate these technical factors contributing to both the accuracy and reproducibility of MBF values between different bolus injector and PET/CT systems against physical reference standards. These evaluations might eventually result in standard clinical acquisition protocols that are capable of producing similar quantitative values across different PET systems and reducing the variation due to technical factors in MPI.

In this study, we evaluate the contribution of different technical factors on the accuracy and reproducibility of MBF values using a preliminary measurement protocol with two [¹⁵O]H₂O bolus injector systems of the same manufacturer, as well as two digital silicon photomultiplier (SiPM) -based PET/Computed Tomography (CT) systems of different vendors, using a flow phantom capable of simulating MBF.

PET systems

The study was conducted in a single-centre setting (Turku PET Centre). The protocol was implemented on two PET/CTs in Turku PET Centre; Discovery MI with 20 cm axial field-of-view (FOV) (DMI-20, GE Healthcare, Milwaukee, US) and Biograph Vision 600 (Vision-600, Siemens Healthineers, Erlangen, Germany). The system performance parameters are presented in Table 1.

Table 1. Performance characteristics of DMI-20 and Vision-600.

	Unit	DMI-20	Vision 600
Number of Detector Rings	-	4	8
Transaxial FOV	cm	70	78
Axial FOV	cm	20	26.1
Crystal Material	-	LSO	LSO
Crystal Array	-	4 x 9	5 x 5
Crystal Size	mm × mm × mm	3.95 × 5.30 × 25.0	3.2 × 3.2 × 20.0
Detector Type	-	SiPM	SiPM
Detector Array	-	3 × 6	-
Detector Active Area	mm × mm	4 × 6	16 × 16
Number of Output Channels	-	-	16
SiPM Pixel Array	-	2 x 3	-
Sensitivity	cps/kBq	13.7	16.4
Spatial Resolution	rad @ 1 cm	4.1	3.5
Peak NECR	kcps	193	306
Peak NECR Activity	kBq/ml	21.9	32
Peak NECR Scatter Fraction	%	40.6	38.7
Timing Resolution	ps	375	210
Energy Resolution	%	9.4	-

Flow phantom

The flow phantom used in this study is presented in detail in the paper of Gabrani-Juma et al. [10], to which the interested reader is directed for a more extensive description. In short, the flow phantom comprises a closed-circulation system, which pumps water from an exterior container into the circulation using a peristaltic pump. The system is located inside a plastic shell filled with water, shaped as the NEMA image quality phantom, simulating attenuation and scatter.

The pump flow rate, named Qpump may be adjusted by the user to produce a desired range of flow values. An injection port is located after the peristaltic pump, from which the activity can be administered directly to the system. Thereafter, the injected activity propagates directly to an input chamber (with a volume of 15.7 ml) which simulates the left ventricle blood pool. The input chamber is connected to an exchange cylinder that contains a perforated tube, from which the tracer permeates to the exchange cylinder. The exchange cylinder simulates the myocardium, and the water flow from the perforated tube into the exterior volume of the exchange cylinder simulates the blood perfusion. The volume of the exchange cylinder (Vcyl) is 160 ml. The flow rates coming out from the perforated tube and from the exchange cylinder are named Qtube and Qcyl, respectively, and are controlled by flow constrictor valves, allowing the user to create different types of restricted and unrestricted flow. The reference values for these flow rates are measured using calibrated flow meters (Omega Engineering Inc., Norwalk, US). A reference flow value Qref can then be derived from Qcyl using the flow meter calibration factors and a look-up table.

The input and tissue time-activity curves (TACs) needed for the kinetic modelling can be measured from the input chamber and exchange cylinder, respectively. This analysis is performed semi-automatically using software provided by the phantom vendor, where the regions of interest can be defined and then used to derive the TACs.

A phantom-specific two-compartmental (one-tissue) kinetic model is used, which is extensively explained in Gabrani-Juma et al. (Equations 2-6) [10]. The model includes two rate parameters, qin and qout, as well as an input signal fraction (ISF) and a delay parameter. qin [min^-1] denotes the rate parameter that describes the flow going from the perforated tube into the exterior volume of the exchange cylinder, and qout [min^-1] the rate parameter that describes the flow coming out from the exchange cylinder. ISF [dimensionless] accounts for the exchange cylinder spill-over from the perforated tube and delay [s] defines the tracer passage time from the input cylinder to the perforated tube. qin and qout are analogous to K1 and k2, the rate constants used in a two-compartmental kinetic model and commonly used to derive MBF e.g. for [¹⁵O]H₂O [20, 21]. qin and qout are multiplied by Vcyl in order convert them to flow values Qin and Qout in [ml/min]. In the phantom, for an ideal measurement, Qin = Qout = Qref.

Measurement protocol

The measurement protocol assessed 12 different flow values, from low to high. Qpump values were set to 150 ml/min, 200 ml/min, and 250 ml/min. Qcyl values were adjusted to 20 %, 40 %, 60 %, and 80 % of Qpump using the constrictor valves, to simulate reduced perfusion. Qcyl and Qtube values were recorded before and after the measurement and their mean values were applied as the reference flow values. Table 2 presents all recorded flow values from the phantom measurements

Table 2. Acquisition parameters for all measurements.

Measurement	*Qpump*	Constriction	*Qcyl*	Qcyl measured				*Qtube*	Qtube measured
				DMI-20		Vision-600			DMI-20		Vision-600
				Test	Retest	Test	Retest		Test	Retest	Test	Retest
150-20 %	150	20 %	30	31	30	32	30	120	124	127	125	111
150-40 %	150	40 %	60	61	59	67	54	90	98	99	92	90
150-60 %	150	60 %	90	91	94	93	79	60	63	61	62	60
150-80 %	150	80 %	120	119	120	124	104	30	32	31	27	33
200-20 %	200	20 %	40	41	43	40	41	160	171	168	168	154
200-40 %	200	40 %	80	80	80	86	86	120	132	134	129	129
200-60 %	200	60 %	120	126	131	115	114	80	83	80	82	83
200-80 %	200	80 %	160	163	162	159	146	40	41	42	40	42
250-20 %	250	20 %	50	49	47	55	52	200	200	203	205	194
250-40 %	250	40 %	100	102	99	103	0	150	162	166	169	0
250-60 %	250	60 %	150	153	160	159	133	100	103	100	108	101
250-80 %	250	80 %	200	200	204	210	184	50	52	53	51	50

All measurements were repeated twice within two weeks on each PET/CT system, in order to ensure test-retest reproducibility of the flow values, without changing the phantom set-up. All measurements were performed in the same order. Before each measurement session, the peristaltic pump was calibrated. The [¹⁵O]H₂O bolus was automatically administered into the flow phantom circulation via the [¹⁵O]H₂O RadioWaterGenerator (RWG, Hidex Oy, Turku, Finland) system. Both the DMI and the Vision system have their individual RWG systems installed next to the PET/CT system gantry.

Both RWGs are daily cross-calibrated with the radionuclide calibrator for [¹⁵O]H₂O. In the calibration procedure, the detected counts are time-corrected to the reference point, i.e., the time-point when the peak reaches the end of the infusion line. Based on the corrected counts, decay time, and radionuclide calibrator data, the software generates a calibration coefficient for the detector. Thereafter, the injected activity in units of [MBq] is calculated from the count-rate curve based on the calibration factor. The manufacturer guarantees 15 % accuracy within the requested activity production level on the system. The requested activity for each measurement was 500 MBq on both systems.

All injected activities with relative differences between test- and retest measurements are given in Table 3. To investigate the reproducibility of the activity administration between the measurements and the system, the bolus curves were extracted from both injector systems. Overall, due to using two different injector systems, the injected activities for DMI-20 were generally lower than 500 MBq, whereas the activities were higher than 500 MBq for Vision-600, although all the measurements were within the manufacturer specified 15 % limit.

Table 3. Injected activities for all the measurements. The manufacturer guarantees 15 % accuracy to 500 MBq.

	DMI-20			Vision-600
Measurement	Test	Retest	Difference (test/retest)	Test	Retest	Difference (test/retest)
	[MBq]	[MBq]	%	[MBq]	[MBq]	%
150-20 %	496	479	-3,43	572	595	4,02
150-40 %	478	487	1,88	534	552	3,37
150-60 %	482	448	-7,05	574	614	6,97
150-80 %	444	431	-2,93	590	610	3,39
200-20 %	474	450	-5,06	582	596	2,41
200-40 %	459	441	-3,92	579	605	4,49
200-60 %	507	480	-5,33	595	589	-1,01
200-80 %	514	497	-3,31	570	591	3,68
250-20 %	483	486	0,62	535	592	10,7
250-40 %	515	472	-8,35	537	609	13,4
250-60 %	492	510	3,66	592	598	1,01
250-80 %	511	483	-5,48	584	590	1,03

PET/CT acquisition

PET acquisition and image reconstruction was conducted in dynamic acquisition mode and followed the clinical MPI acquisition protocol used at our institute (Turku PET Centre) [22]. The acquisition duration was divided into 24 time frames, 14 × 5 s, 3 × 10 s, 3 × 20 s, and 4 × 30 s. Prior to each PET acquisition, a measurement-specific CT-based attenuation correction (CTAC) was acquired. The values used in the CT acquisitions were: tube voltage of 120 kV, exposure time of 500 ms, pitch factor of 1.375, tube current of approx. 96 mA (test) and approx. 61 mA (retest) on DMI-20, and tube voltage of 100 kV, exposure time of 500 ms, pitch factor of 1.2, and tube current of approx. 121 mA (test), and approx. 79 mA (retest) on Vision-600.

All data was decay corrected for the injected activity at the start of the measurement, which was derived directly from the data from the injector system. All quantitative corrections were applied for the PET data. Image reconstructions followed the clinical protocol used at our institute [22]. The reconstruction parameters are presented in Table 4.

Table 4. Reconstruction parameters used in this study.

Reconstruction parameters	DMI-20	Vision-600
Algorithm	OSEM	OP-OSEM
TOF	TOF	TOF
PSF	PSF	PSF
Iterations	16	8
Subsets	5	5
Matrix size	192	220
Gaussian post-filter [mm]	5	6
FOV [cm]	35	35

Image analysis

All PET images were analyzed using the QuantifyDCE software (Shelley Medical Imaging Technologies, Ontario, Canada), which is specifically developed for the analysis of the flow phantom data. The details of the image analysis and kinetic modelling are presented in Gabrani-Juma et al. [10]. In short, the software requires the user to define the input chamber and exchange cylinder volumes-of-interests (VOIs) from which the software automatically extracts the input and tissue TACs. Thereafter, the software automatically applies a two-compartmental kinetic model for the TACs, which outputs Qin, Qout, delay, and ISF values. The image analysis was conducted similarly for all data sets by a single operator, and ROI locations were fixed for each measurement session. Retest measurement 250-40 % on Vision-600 was left out from all analyses due to QuantifyDCE not being able to execute the analysis for the data set.

Data analysis

For quality control, the flow meter readings from Qcyl and Qtube were extracted for all measurements and compared in terms of relative difference to the expected value from Qpump. We report the relative differences for the sum Qtube+Qcyl in comparison to expected Qpump for all measurements.

The comparison of the bolus curves and TACs was conducted visually as well as quantitatively by evaluation of the areas-under-the-curves (AUCs), whereas the modelled flow values were compared quantitatively and statistically.

The extracted bolus activity curves from the [¹⁵O]H₂O generator were inspected visually for each measurement to investigate their reproducibility. Bolus AUCs were computed from each bolus curve to investigate their contribution to the measured input TAC as well as the tissue TAC from the phantom. Bolus AUCs with the input and tissue AUCs are presented in Supplementary File I. Both input and tissue TACs with the modelled TACs were inspected visually to assess the reproducibility between the systems.

Thereafter, comparison for Qin and Qout values was made between all test and retest measurements for both PET/CT systems. The absolute error of Qin and Qout with respect to Qref was calculated as

Figure 1 shows the differences of the flow meter recordings from the sum of the Qcyl and Qtube values with respect to the set Qpump values on the measurements from DMI-20 and Vision-600. The measurements performed on DMI-20 showed similar difference ranges between test and retest measurement for different flow settings, whereas the measurements performed on Vision-600 produced larger differences between test and retest measurements. In all flow phantom measurements, the flow meter readings showed smaller than 10 % relative difference. Overall, the magnitude of the errors was similar between both systems, except for measurements 150-80 %, 200-80 %, 250-20 %, and 250-80 %.

Figure 2 shows the bolus curves extracted from test and retest measurements on both RWG dispenser systems installed on the DMI-20 and Vision-600 PET/CT systems. The bolus administration was similar on both systems, although the RWG installed on the Vision-600 system produced higher bolus peaks in most of the measurements compared to the RWG installed on the DMI-20 system. Subsequently, the measurements performed on Vision showed higher injected activities (Table 3) and bolus AUCs (Supplemental Figure 1) and consequently higher peaks for the input TACs (Figure 4) as well as input AUCs (Supplemental Figure 2).

Figures 3 and 4 show TACs from all measurements recorded on both PET/CT systems. The shape of input, tissue and modelled time activity curves are similar between test and retest measurements as well as between systems. Although, the peak amplitudes of the input TACs on the measurements on Vision-600 are higher compared to the measurements on DMI-20. When it comes to tissue and modelled TACs, the measurements on DMI-20 show visible differences in tissue and modelled TACs on one measurement (250-80 %) between test and retest measurements. Vision-600, however, shows visible differences on three measurements (150-20 %, 150-80 %, and 200-80 %) between test and retest measurements in tissue and modelled TACs.

Table 5 shows Qin and Qout flow values, as well as their flow value errors with respect to Qref from Equation (1) for test and retest measurements. Overall, most of the flow value errors fall below 10 % on both test and retest measurements performed on DMI-20. However, the Vision-600 retest measurements show higher flow value errors especially with Qin compared to DMI-20, several being larger than 10 %. This follows from the higher bolus and input TAC peaks, which affect more to the modelled value Qin.

Table 5. Modelled Qin and Qout values as well as their absolute relative errors with respect to Qref from all measurements on both DMI-20 and Vision-600.

Measurement	DMI-20		Vision-600		DMI-20		Vision-600		DMI-20		Vision-600		DMI-20		Vision-600
	Qin		Qin		Qout		Qout		Qin error		Qin error		Qout error		Qout error
	Test	Retest	Test	Retest	Test	Retest	Test	Retest	Test	Retest	Test	Retest	Test	Retest	Test	Retest
150-20	40	37	38	33	38	37	49	29	19	23	25	31	22	24	3	40
150-40	72	72	76	58	63	64	78	59	8	6	9	19	19	15	7	18
150-60	111	111	106	84	106	107	104	94	2	1	4	12	2	4	6	2
150-80	146	145	156	108	138	140	141	120	6	5	9	10	0	1	2	1
200-20	49	50	47	44	43	47	49	42	15	17	18	24	27	22	15	28
200-40	99	99	95	82	94	93	98	92	2	2	8	12	3	4	5	1
200-60	159	156	139	118	156	154	146	133	9	3	4	11	7	2	9	0
200-80	188	181	166	150	187	185	170	168	2	5	11	12	3	3	9	1
250-20	69	68	64	56	64	62	62	55	4	6	12	19	3	3	13	20
250-40	118	121	116	-	118	-	124	-	1	-	4	-	1	-	3	-
250-60	182	197	184	148	181	205	208	167	2	5	1	4	1	9	11	8
250-80	179	179	223	188	176	174	243	213	22	23	7	13	23	26	1	1

Figure 5 shows repeatability errors of Qin and Qout between test and retest measurements (Equation 2) on the measurements on DMI-20 and Vision-600. All DMI-20 measurements fall below 15 % repeatability errors whereas Vision-600 shows larger than 15 % errors for six measurements (half of the measurements). The mean ± standard deviation of errors for DMI-20 Qin and Qout are 2.1 % ± 2.6 % and 3.3 % ± 4.1 %. The corresponding numbers for Vision-600 Qin and Qout are 23 % ± 25 % and 22 % ± 27 %, respectively.

Figure 6 shows that Qin and Qout values are close to the reference flow values (Qref) on DMI-20. In addition, the linear polynomial fits are close to the reference line. However, on the measurements performed on Vision-600 both Qin and Qout values diverge more from each other as well as from the reference line (Figure 6).

Figure 7 describes the correlation of Qin and Qout flow values between DMI-20 and Vision-600 in terms of test- and retest measurements. The correlation plot shows that flow values experience larger differences between DMI-20 and Vision-600 at high flow rates, also seen as larger deviation between Qin and Qout linear fits when compared to the line-of-identity. For Qin values, the test measurements are closer to the line-of-identity whereas for Qout values, the retest measurements are closer. Overall, there is similar correlation for Qin and Qout between the systems.

The Bland-Altman plots in Figure 8 show similar lines-of-agreement (LoAs) for both Qin and Qout. DMI-20 and Vision-600 Qin values vary more with higher flow values compared to Qout. The mean differences between DMI-20 and Vision-600 were 9.9 ml/min and -0.3 ml/min for Qin and Qout. Therefore, Qin shows more dependency on the flow rate.

This study proposed a preliminary protocol for assessing the technical factors contributing to the accuracy and reproducibility of quantitative flow values in MPI. This protocol could eventually be used for planning harmonization measurements for myocardial perfusion imaging using [¹⁵O]H₂O as well as the flow phantom in the future. We evaluated the impact of technical factors on the modelled flow values, Qin and Qout, extracted from the flow phantom. The accuracy and reproducibility was evaluated between test and retest measurements, two [¹⁵O]H₂O injectors as well as two digital PET systems, Discovery MI and Biograph Vision 600. However, there is no specific reason why the protocol could not be applied to analog PET/CT systems as well.

The administered [¹⁵O]H₂O activities were repeatable between test and retest measurements on both systems (Table 3). RWG installed on DMI-20 produced over 10 % relative difference between the test and retest measurements only with one measurement, and the RWG on Vision-600 on two measurements. All administered activities were within 15 % from the requested 500 MBq and fall within the vendor specifications. However, the RWG installed on the Vision-600 produced systematically higher activities and that directly contributed to the amplitude of the bolus curves.

In order to minimize the outside technical factors affecting the modelled flow values and to conduct quality control of our measurements, we also measured the error of Qtube+Qcyl with respect to Qpump. Ideally Qtube+Qcyl should be identical to Qpump but experience differences in most measurements on both DMI-20 and Vision-600. Vision-600 showed larger Qtube+Qcyl differences than DMI-20. Overall, all Qtube+Qcyl vs Qpump differences were below 10 % on both systems (Figure 1), which is why we assume that this factor only has a minor effect on the modelled flow values.

The phantom protocol includes quality control procedures, which we conducted to minimize the variation of Qtube+Qcyl. The most important is the pump calibration prior to each measurement session. There could also be a benefit from calibrating the pump whenever the flow rate or the constriction of Qcyl or Qtube is altered between measurements. However, this might not be practical as changing the calibration between measurements may compromise the measurement reproducibility. Also, the flow meter calibration applied in the QuantifyDCE software should ensure the validity of the flow meter readings and therefore the accuracy of the recorded flow values should be guaranteed by using the single pump calibration.

In general, the most significant factor found contributing to the differences in the input TACs and the resulting flow values is the difference in [¹⁵O]H₂O bolus peak amplitudes between measurements performed on DMI-20 and Vision-600 (Figure 2). The difference resulted to higher bolus AUCs as well as input AUCs on Vision-600 (Supplemental Figures 1, 2). The direct relationship between bolus and input TAC is caused due to the bolus travelling directly from the injection port to the input chamber without mixing or dispersion on the way, in a relatively short distance and time. Another factor contributing to this variation is that the PET/CT systems have their own individual RWGs, although they are calibrated to the same reference and should not contain delivery variations from the target activity of more than 15 %. However, systematic differences in bolus delivery between test and retest measurements could be seen especially in the measurements conducted with Vision-600 (Figure 2). Nevertheless, the input TACs were reproducible between the systems and the measurements (Figures 3 and 4).

We have observed that the dependency of the input TACs of the bolus delivery does not seem to propagate directly to the tissue and modelled TACs. The tissue TACs showed high reproducibility between measurements and systems (Figure 3 and Figure 4). We could not detect a clear discrepancy between the tissue and modelled TACs between the test and retest measurements, and only their height visually altered in one measurement on DMI-20 (250-80 %) and three measurements on Vision-600 (150-20 %, 150-80 %, and 200-80 %). Overall, the similar behavior in tissue TACs between test and retest measurements and systems is possibly due to the tracer already mixing within the exchange cylinder and washing out at a certain rate. This is primarily determined by the internal flow characteristics of the system, controlled by the pump and the flow constrictor valves.

Most importantly, we could show that most of the measurements were highly repeatable as the repeatability errors of Qin and Qout were below 15 % on majority of the measurements (Figure 5, Table 5). The highest repeatability was seen on the measurements performed on the DMI-20 system. However, as the bolus peak amplitudes were higher with the measurements on Vision-600, and thus the input TACs were larger, the modelled flow values consequently experienced more variation on Vision-600 when compared to DMI-20. A large factor contributing to this variation is the use of individual RWGs on each PET/CT system, which would indicate that most of the differences originate not from the PET/CT system alone, but their origin is also in the different bolus delivery systems.

In general, the input curve discrepancies due to the bolus delivery mainly resulted in higher Qin differences between test and retest measurements (Table 5). This results from Qin being analogous to K1 (wash-in) parameter from the two-compartmental model, and being more sensitive to variations in the input function. We also saw more variations in the Qin parameter with higher flow rates, indicating that Qin might be sensitive to the bolus as well as flow rate (Figure 8). The changes in the input TAC height seemed not to affect the Qout parameter directly similarly as Qin. The Qout values are analogous to the k2 (wash-out) rate parameter, which makes them more sensitive to changes in the tissue TAC. However, variations could also be seen in the Qout parameter in the correlation analysis (Figure 6 and 7) as well as in the agreement analysis (Figure 8), although in general the relative errors between the measurements were smaller with the Qout parameter.

In the Bland-Altman analysis (Figure 8), Qin and Qout showed differences between DMI-20 and Vision-600 and varied more with high flow values. Although Qout showed a smaller mean difference, there were variations in both Qin and Qout in both the test and retest measurements between the systems. Altogether, the LoAs were similar for Qin and Qout and the mean difference between DMI-20 and Vision-600 was 9.9 ml/min and -0.3 ml/min for Qin and Qout, respectively (Figure 8). The results show that although both Qin and Qout can be derived between two injector systems and two PET/CT systems with a small bias compared to the actual flow values, further measures need to be implemented to reduce the variation between the systems to increase the reproducibility of both Qin and Qout.

When comparing the flow phantom measurements to the data published from MBF values measured on clinical subjects, the mean values and standard deviations of the repeatability errors between test and retest measurements on Vision-600 (Qin: 23 % ± 25 % and Qout: 22 % ± 27 %) were still within a similar range that is measured in clinical myocardial perfusion studies [23]. For example the table in Klein et al. describes that overall the stress MBF values have repeatability accuracy from 11 % to 34 %, and for [¹⁵O]H₂O the reported values are 27 % [18] and 25 % [19]. In this regard, the measured values from the phantom study agree well with the results gained from the clinical subjects. As compared to our phantom study, higher variation in patient studies reflect the contribution of differences in biological factors, such as the systemic hemodynamic state, on MBF.

Finally, these experiments confirmed that the proposed imaging protocol with several flow values as well as imaging parameters including reconstructions can be applied between different injector and PET/CT systems to provide an understanding for technical accuracy and reproducibility of the quantitative flow values between test-retest measurements. A future multi-center study would be essential to provide upper and lower limits for the Qin and Qout values for calibration purposes and would give specific information about the different factors contributing to the variations of MBF values between centers and PET/CT systems. However, in order to apply this protocol for multi-center setting, a careful investigation for standardizing the bolus delivery between different sites and dispenser systems needs to be conducted first. Ensuring an even more consistent tracer administration profile should improve test-retest repeatability further [24].

Limitations

The remaining technical factors from the PET systems that may affect the modelled flow values are for example the higher sensitivity of the Vision-600 compared to DMI-20 (13.7 cps/MBq on DMI-20 and 16.4 cps/MBq on Vision-600) as well as smaller spatial resolution of Vision-600 compared to DMI-20 (4.1 mm on DMI-20 and 3.5 mm on Vision-600) (Table 1). However, no direct relationship of the contribution of these factors could be derived, to determine their effect to the quantification differences between the systems. Future studies conducted on multiple systems will complement the understanding of these factors.

As an additional limitation, the flow phantom has internal characteristics that may affect the repeatability of the measurements. For example, the pressure variations inside the hoses, the air bubbles within the phantom, the air pressure and humidity of the scanner room, and the phantom flow meter inaccuracies are likely to affect the modelled flow values. In addition, the flow phantom presents only a simplified simulation of myocardial perfusion.

In clinical subjects, there will be more variations due to physiological factors, including tracer dispersion and individual reactions to pharmacological stress, in addition to other physiological factors that contribute to the measured MBF. However, the flow phantom allows studying the contribution of different technical factors in controlled and measurable way, where the ground truth is known. Moreover, the phantom includes several calibration and quality control measures to ensure the reproducibility and comparability of the measurements.

Another limitation is that the clinical reconstruction protocols were used on each system without specific harmonization. Ideally, the reconstruction parameters could be harmonized between systems. For example, EARL has proposed guidelines to apply reconstruction parameters that result into equal voxel sizes regardless of the PET system used [4]. In this study, on DMI-20 the voxel sizes were (x, y, and z) 1.82 mm, 1.82 mm, and 2.79, and on Vision-600 1.65 mm, 1.65 mm, and 3. Thus, the final reconstructed image resolution on both systems was similar despite using the clinical protocol. Moreover, assessing the clinical protocol on each system is more appropriate for a multi-center situation without specific harmonization conducted. However, the effect of harmonizing system reconstructions should be investigated in the future.

Furthermore, there is still room for validation of the protocol with multiple tracers used for MPI. The present protocol was assessed only for [¹⁵O]H₂O. [⁸²Rb] and [¹³N]NH₃ are commonly used in MPI as perfusion tracers. Thus, at least a cross-verification to the present protocol or designing a tracer-specific protocol for these tracers is required. Moreover, modifying the protocol to be used with [¹⁸F]-labelled tracers would be highly useful, as they are widely available in different centers. Thereafter, preliminary harmonization measures could be evaluated using also ¹⁸F-tracers, as long as the protocol would have been cross-calibrated with the present [¹⁵O]H₂O protocol.

Summary of the Findings and Future

We were able to demonstrate 15 % repeatability across all measurements on DMI-20 and on half of the measurements on Vision-600. This relatively high repeatability is expected, as in a single-center setting there are several factors that are advantageous for minimizing the bias and variability between the measurements. First, the [¹⁵O]H₂O bolus injectors were calibrated to a common reference within the center. Second, the acquisition protocols and the activity delivery were standardized between the measurements. Third, both PET systems were cross-calibrated to the common reference within the center, their acquisition durations as well as reconstruction frame times were the same, with relatively similar settings in the reconstruction parameters.

However, in multi-center settings there will be several more technical factors affecting the measurements, all of which should be investigated separately. Therefore, the present findings provide support for assessing the current protocol for measurement of accuracy and reproducibility of flow values in a multi-centre setting for myocardial perfusion imaging with [¹⁵O]H₂O, where the first factor is to start with ensuring high repeatability and reproducibility of the delivery of the bolus, especially with different injector systems.

A preliminary protocol for measuring the accuracy and reproducibility of flow values in [¹⁵O]H₂O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15 % in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol, which should be carefully investigated in a multi-center setting.

Availability of data and material

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Funding

This study has received funding from the Insrumentarium Science Foundation, Finnish Foundation for Cardiovascular Research, EMPIR programme co-financed by the Participating States, the European Union’s Horizon 2020 research and innovation programme (15HLT05 PerfusImaging, 19SIP04 TracPETperf), the UK’s Department for Science, Innovation and Technology (DSIT), and the Doctoral Programme of Clinical Research in the University of Turku.

Authors’ contributions

Conceptualization, R.S., J.T.; Methodology, R.S., J.T., H.P., L.K., T.T., Validation, A.F., N.A.S., M.T., A.S., Investigation, R.S., J.T., L.K., Writing-Original Draft Preparation, R.S., Supervision, M.T., A.S., J.T.

Acknowledgements

This study was conducted within the Finnish Center of Excellence in Molecular Imaging in Cardiovascular and Metabolic Research supported by the Academy of Finland, University of Turku, Turku University Hospital and Åbo Akademi University.

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Preliminary Protocol for Measuring the Reproducibility and Accuracy of Flow values on Digital PET/CT Systems in [15O]H2O Myocardial Perfusion Imaging Using a Flow Phantom

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