254 Sec 4am Is the Same as the Art of Pressure

J Med Imaging (Bellingham). 2019 Apr; 6(2): 021603.

Initial evaluation of iii-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation

Lauren M. Shepard,^a, ^b Kelsey N. Sommer,^a, ^b Erin Angel,^c Vijay Iyer,^d Michael F. Wilson,^d Frank J. Rybicki,^{due east} Dimitrios Mitsouras,^eastward Sabee Molloi,^f and Ciprian Northward. Ionita^a, ^b, ^k, ^*

Lauren K. Shepard

^aUniversity at Buffalo, University Department of Biomedical Engineering, Buffalo, New York, United states

^bCanon Stroke and Vascular Research Center, Buffalo, New York, United States

Kelsey Northward. Sommer

^aUniversity at Buffalo, University Department of Biomedical Technology, Buffalo, New York, United States

^bCatechism Stroke and Vascular Inquiry Center, Buffalo, New York, United States

Erin Angel

^cCanon Medical Systems U.s., Tustin, California, United states

Vijay Iyer

^dAcademy at Buffalo Medicine, Interventional Cardiology, UBMD, Buffalo, New York, United states

Michael F. Wilson

^dAcademy at Buffalo Medicine, Interventional Cardiology, UBMD, Buffalo, New York, The states

Frank J. Rybicki

^eastwardUniversity of Ottawa, Ottawa Hospital Research Institute and the Department of Radiology, Ottawa, Canada

Dimitrios Mitsouras

^eastUniversity of Ottawa, Ottawa Hospital Research Institute and the Section of Radiology, Ottawa, Canada

Sabee Molloi

^fUniversity of California Irvine, University Department of Radiological Sciences, Irvine, California, United States

Ciprian North. Ionita

^aUniversity at Buffalo, University Department of Biomedical Engineering, Buffalo, New York, United States

^bCatechism Stroke and Vascular Research Centre, Buffalo, New York, United states

^{one thousand}University at Buffalo, Academy Department of Neurosurgery, Buffalo, New York, United States

Received 2018 Aug fifteen; Accepted 2019 February xix.

Abstruse.

We developed 3-dimensionally (3D) printed patient-specific coronary phantoms that are capable of sustaining physiological menses and pressure level conditions. We assessed the accuracy of these phantoms from coronary CT acquisition, benchtop experimentation, and CT-FFR software. Five patients with coronary artery disease underwent 320-detector row coronary CT angiography (CCTA) (Aquilion One, Canon Medical Systems) and a catheter lab procedure to measure out fractional flow reserve (FFR). The aortic root and three main coronary arteries were segmented (Vitrea, Vital Images) and 3D printed (Eden 260V, Stratasys). Phantoms were connected into a pulsatile menstruation loop, which replicated physiological flow and pressure gradients. Contrast was introduced and the phantoms were scanned using the same CT scanner model and CCTA protocol as used for the patients. Image information from the phantoms were input to a CT-FFR research software (Canon Medical Systems) and compared to those derived from the clinical information, along with comparisons betwixt paradigm measurements and benchtop FFR results. Phantom diameter measurements were within 1 mm on average compared to patient measurements. Patient and phantom CT-FFR results had an absolute mean difference of iv.34% and Pearson correlation of 0.95. We accept demonstrated the capabilities of 3D printed patient-specific phantoms in a diagnostic software.

Key words: patient specific 3D printed phantoms, CT-FFR, coronary phantoms, blood flow simulations, coronary CT angiography

one. Introduction

Coronary CT angiography (CCTA) is currently appropriate for imaging patients with defined run a risk factors for coronary artery disease (CAD), one of the leading causes of death in the globe.¹ The high negative predictive value makes CT useful for many patient cohorts,¹ ^, ⁱⁱ but the exam remains limited for intermediate risk patients and those with stable CAD. For intermediate take chances patients, more authentic noninvasive methods are needed to determine the hemodynamic significance of CAD. The current reference standard for assessing hemodynamic significance for CAD is interventional fractional flow reserve (FFR), which determines CAD severity via hemodynamic significance.³ This technique has a cutoff value of FFR $< 0.8$ for treatment; however, for FFR values in the range of 0.75 to 0.85, the certainty of repeating the same measurement is $< lxxx %$ , and information technology is suggested for patients inside this range to undergo an boosted diagnostic method.^four ^, ⁵ At that place are likewise risks associated with this diagnostic method, such as radiation dose, traumatic injury to the coronary wall at the fourth dimension of catheterization, and ischemia from plaque dislodgement.^six ^, ⁷ As such, noninvasive diagnostic tools have been developed for CAD take a chance assessment.⁸ Referred to every bit CT-FFR, these methods utilize CT angiography images and computational fluid dynamic methods to estimate the menses weather in the coronaries, potentially bypassing the endovascular process and associated risks for diagnosis of CAD. CT-FFR methods incorporate coronary avenue geometries from CT and simulate blood flow conditions to estimate pressure gradients and calculate FFR.^ix ^– ¹¹ Previous trials have been completed to investigate measurement of FFR from CCTA and successfully able to measure CT-FFR with a sensitivity and specificity of 84.3% and 87.9%, but the CT-FFR did underestimate the disease severity.¹² This underestimation can exist a consequence of artifacts from the CT images, such as beam hardening or movement, and/or inaccuracies in the computational fluid dynamic simulations used, including boundary resistance, which cannot be measured noninvasively, leading to variations in CT-FFR results.¹³ There are challenges in accurately simulating blood period conditions and replicating the rubberband properties of vasculature.¹² There is also the limiting cistron of validating CT-FFR diagnostic software which requires large clinical trials to validate the technology. These aspects point the need for an accurate and repeatable validation method of testing which tin can exist reproduced across diverse imaging platforms and simulation software.

In recent years, 3D press has provided researchers with an invaluable tool for replicating circuitous patient anatomy in a benchtop arrangement.¹⁴ At that place are vast applications for 3D printing, and in particular, cardiovascular 3D printing¹⁵ has been recognized in several domains. Applications include surgical planning, simulating interventions, and replicating structural diseases.¹⁵ Previous research has shown the use of 3D printing to create coronary phantoms with stenosis; however, these phantoms were idealized and made out of rigid materials that do not replicate the compliance of vasculature.¹⁶ Recently, 3D printing has been utilized to create patient-specific coronary phantoms that accept been successfully used for flow measurements.¹⁷ ^– ¹⁹ Despite a express number of commercial polymers that are available for 3D press, these phantoms have been demonstrated to approximately replicate the mechanical backdrop of vasculature.¹⁸ Although phantoms accept been implemented in the final decade to simulate physiologic components, including flow control, elastic compliance, and controlled menstruum waveforms in specific arterial beds, this has not been washed in the coronary circulation in a manner that incorporates actual patient anatomy and vessel compliance.²⁰ This project expands on the electric current applications of 3D printing to further develop cardiac phantoms with physiological flow weather condition for accurate CT imaging of coronary flow. In addition, the accuracy of the development and manufacturing of these phantoms was assessed.

We have investigated the use of 3D printed patient-specific coronary phantoms to reproduce patient CT-FFR results, starting from the acquisition stage to benchtop flow experiments to software simulation. Using CT imaging, the 3D printed phantoms were successfully imaged with a CCTA protocol and implemented in a CT-FFR software. Accuracy of the phantoms compared to the patients was verified using measurements from the CCTA images and benchtop assessment of FFR. This research verified the apply of 3D printed patient-specific phantoms as a physiologically accurate tool for use in and validation of diagnostic image-based software.

two. Purpose

3D press of cardiovascular anatomy tin can be used for radiology applications, such equally diagnostic software validation and prototype guided surgical training. Nosotros have adult accurate coronary tree phantoms from materials that are capable of sustaining physiological flow and pressure conditions while maintaining the stress and strain characteristics of homo coronary arteries. These phantoms were used to improve coronary menstruum cess and for imaging using common diagnostic procedures. This allowed for the 3D printed patient-specific phantoms to be used for the validation of a CT-FFR inquiry software.

three. Materials and Methods

Patients underwent written informed consent and enrolled into our study following IRB approval. All patients underwent clinically indicated 320-detector row CCTA (Aquilion ONE, Canon Medical Systems) with 100 kVp, 111 mAs, 0.five mm piece thickness, and a reconstructed voxel size of 0.63 mm, isotropic. These patients then had a clinically indicated coronary catheterization that included invasive FFR measurement. Invasive FFR measurements were recorded at the Gates Vascular Constitute (Buffalo, New York) at a distance of two lesion lengths below the distal terminate of the stenosis. Using angiography images from the endovascular procedure, we measured the altitude along the vasculature where the clinicians measured FFR. This measurement distance was used to measure CT-FFR at the same location equally the invasive FFR in both the patient and phantom images. Of the five patients used in this study, invasive FFR was performed on six vessels (four LAD, 1 LCX, and 1 RCA).

3.1. Phantom Design and Manufacturing

The CCTA data were used to segment the aorta, left anterior descending (LAD), left circumflex (LCX), and right coronary avenue (RCA) using a Vitrea workstation (Vital Images, Minnetonka, Minnesota) [Fig. ane (angio CT)]. The segmented vasculature was exported as a stereolithography (STL) file for advanced mesh manipulation using a 3D modeling software (Autodesk Meshmixer, San Rafael, California) [Fig. 1 (segment geometry)]. The coronary vasculature was manipulated into a previously reported three branch approach,^eighteen which simplifies and smoothes the vasculature geometry to include only the aorta and the iii main coronary arteries. The minimal smoothing process was tailored to simply reduce artifacts while maintaining the overall geometry of the arterial lumen using a previously reported technique.¹⁹ The vasculature geometry was also manipulated to create a vessel wall to allow flow. Access ports were created on the aorta and the iii coronary arteries to allow connection of pressure sensors during menses experimentation. The distal vessel access ports are placed at approximately the location where the invasive FFR is measured. The last step in the mesh manipulation process was to append a base of operations to the vasculature to provide stability during flow experimentation. This was completed by designing a base of operations construction in SolidWorks (SolidWorks Corp., Waltham, Massachusetts) and importing the STL into Autodesk Meshmixer. One time in Autodesk Meshmixer, the base is aligned with the vasculature and a Boolean difference is performed on the base. This geometric function uses boundaries of the two structures to subtract the vasculature from the base, thus creating an opening for the inner lumen of the vasculature.²¹ Following the Boolean difference, the vasculature and base are combined into one structure.²² Once mesh manipulation was completed, the coronary vasculature was 3D printed using a Stratasys (Eden Prairie, Minnesota) Eden260V printer. Tango+, a soft rubber-like polymer from Stratasys, was selected for printing because the polymer mimics the compliance of vasculature.¹⁸ The process of creating the phantoms is outlined in Fig. ane, with the 5 main steps in the design process highlighted.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g001.jpg

Five central steps in phantom blueprint process, starting with CT angiography images from the patient, segmentation of the desired geometry, simplifying and smoothing of vasculature, designing a support for the vasculature and appending information technology, then finally 3D printing the phantom.

3.2. Flow Experimentation

In one case 3D printed, each patient-specific phantom was established in a pulsatile flow loop using a CompuFlow yard programmable physiological catamenia pump (Shelley Medical Imaging Technologies, London, Ontario, Canada). This pump is capable of simulating pulsatile flow rates mimicking those seen in the coronary arteries. The user can import waveforms, allowing simulation of physiologic catamenia rates and waveforms, and the pump can output an ECG signal triggered by a specific signal in the waveform. A 60-40% h2o-glycerol mixture, approximate viscosity of iii.vii cP and judge density of 1.1 g/mL,²³ was used with the pulsatile pump to replicate the viscosity of blood. Effigy ii shows the waveform used by the pulsatile pump to simulate physiologic weather condition. Each phantom was subjected to an average flow charge per unit of 4.3 mL/s, mimicking period rates seen through the 3 main coronary arteries of $\sim 250 mL / \min$ .²⁴

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g002.jpg

Waveform used with the CompuFlow 1000 programmable pulsatile pump.

Effigy 3 shows the key components of the menstruation loop within this study. The pulsatile pump feeds the water–glycerol mixture from a reservoir to the phantom. There are 3 features on the phantom: compliance chamber to dampen the menstruum, vessel outflow command, and pressure sensors to tape the proximal and distal pressure level. The compliance chamber and vessel outflow controls were used to further the accurateness of the physiological flow and pressure conditions in the phantom. First, distal resistance was generated for the coronary arteries to mimic the resistance from separate capillary beds. Previous conference proceedings from this report have shown the significant impact of distal resistance on the measured FFR values.²⁵ Simulation of the capillary bed outcome on flow is non possible with the electric current 3D printing technologies; while 3D printing resolution would allow cosmos of very fine structures, removal of support material would be very challenging. Hence, distal resistance can exist fake within reasonable ranges by controlling the diameter of the outflow tubes with mechanical clamps for each coronary artery. By adjusting the distal resistance, the flow rates and pressures in each individual artery were regulated. 2d, the aorta flow and pressure were regulated using an air compliance chamber as seen in Fig. 4(a), based on a previous written report.²⁵ These two period controls were adjusted in conjunction until pressure level within the aorta reached a minimum of 80 mmHg to ensure physiological accuracy.²² ^, ²⁵ ^, ²⁶ Previous in vitro studies using idealized 3D printed coronary phantoms have indicated a dependence on FFR with increasing aortic pressure, making this aligning in the 3D printed phantoms a necessary stride toward replicating physiological weather condition.^sixteen

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g003.jpg

Overview of key components of flow loop for experimentation. Blue arrows correspond the direction of flow.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g004.jpg

Benchtop setup of 3D printed patient-specific phantoms. (a) Phantoms in established flow loop with programmable pulsatile pump with pressure sensors attached and (b) phantom, outlined in cherry, in Aquilion ONE scanner for CCTA scans.

Figure four(a) shows the setup of the phantoms within the menses loop and Fig. 4(b) shows the phantom setup inside the CT gantry. Each phantom had pressure sensors appended to the aorta and 3 coronary arteries, using the admission ports created during mesh manipulation, to ensure each phantom was undergoing physiologically accurate pressure level atmospheric condition. These force per unit area values were recorded and used to summate the "benchtop FFR" from the flow experimentation as another method to assess the accuracy of the 3D printed phantoms.

Once physiological flow weather were achieved, the patient-specific phantoms were and so used for CCTA testing. Each phantom underwent a similar 320-detector row CCTA (Aquilion I, Canon Medical Systems) to the patients, with 120 kVp, 44 mAs, and 0.5 mm slice thickness. There was a slight variation (to better emulate the physiology) of the protocol used clinically for the v patients enrolled in this study. This variation in protocol involved a two-mL volume of contrast (370 mgI/mL) at a flow charge per unit of 0.4 mL/s followed by a saline affluent existence mixed in the menstruation loop to attain contrast enhancement in the phantoms. To ensure similarity between the two datasets, bolus triggering with the same threshold was used for epitome acquisition. Thus, arteries were fully opacified in both information sets and the dissimilarity gradient was minimized. In addition, the ECG output from the CompuFlow 1000 pulsatile pump was used to trigger the CT conquering during the 70% to 99% R-R cycle. To replicate the CT-FFR protocol which requires four volumes to run the simulations, we also reconstructed the data for four volumes corresponding to 70%, 80%, 90%, and 99% R-R cycle. Figure v shows CCTA images of both the patient (A) and the phantom (B) with three different views, for the seventy% volume. Once CCTA images were collected for each phantom, the accurateness of the phantoms was assessed every bit described in Sec. 3.three.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g005.jpg

CCTA images for case #3. Patient (a) CCTA of LCX and (b) phantom CCTA images of LCX.

3.3. Phantom Accurateness Assessment

The patient and phantom CCTA images were imported and segmented using Mimics Research (Materialise, Plymouth, Michigan) to perform assessment on the vasculature geometry. In society to complete the measurements, centerlines were generated for the aorta and iii main vessels, LAD, LCX, and RCA. Figures 6(a) and half-dozen(b) show a comparison of a patient and corresponding phantom geometry within Mimics, including the centerlines in red.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g006.jpg

(a) Segmented patient and (b) phantom CCTA images in Mimics Research, centerline shown in crimson. In the phantoms, just the master arteries (LAD, LCX, and RCA) were maintained. Measurement of tortuosity in (c) patient and (d) phantom.

One time the centerlines were calculated for all three vessels, various measurements regarding the geometry were recorded at distances ranging from 10 to 100 mm from the ostium, using 10 mm increments. This was completed in all iii vessels for both patients and phantoms. The parameters that were measured include: minimum diameter, maximum diameter, best fit diameter, cross-sectional area, and tortuosity. All-time fit diameter is defined every bit the mean of all bore measurements. Tortuosity is defined as shortest distance/vessel length and was utilized to verify the 3D geometry of the vasculature to ensure the soft material used in 3D press did not deform the vasculature. Figures 6(c) and 6(d) testify an instance of the geometry inside Mimics Enquiry and the collection of tortuosity measurements for the LAD. Note that this patient had a more often than not occluded RCA. Once the measurements were collected, the information were analyzed to decide the absolute hateful differences for the phantom CCTA images from the patient CCTA images.

3.4. CT-FFR Software

The CT-FFR algorithm used for this research is an on-site research tool (Canon Medical Systems, Tustin, California). CT information betwixt 70% and 99% of the R-R interval are imported into the software,¹¹ and the phase with the least amount of move is selected as the target phase. Once the user has selected the target phase, the software automatically calculates the centerlines and contours of the three main coronary arteries. The user and then reviews the measurements and has the selection to adapt centerlines and contours inside the multiplanar and axial prototype views to ensure an accurate lumen segmentation. One time any necessary edits are made, the software utilizes multiphase acquisition and fluid structure analysis to simulate flow conditions. Details about this software take been published previously.^x ^, ²⁷ The CT-FFR is so calculated, and the user is able to accommodate the location of the CT-FFR measurement. The CCTA images for all patients and phantoms were imported into this software and the CT-FFR was calculated post-obit the same steps. Effigy 7 shows the CT-FFR software used and the process of acquiring results.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g007.jpg

CT-FFR software utilized for this research, patient data. Viewing imported images from 70% to 99% R-R and selecting the phase with the least amount of motion as the target phase (top image). Generation of centerline and contours (bottom left image). CT-FFR measurement with user command for distal measurement location indicated (bottom right image).

CT-FFR results were measured at various distal locations measured from the ostium of the three primary coronary arteries: LAD, LCX, and RCA. These distances ranged from ten to 100 mm, and ten mm increments were used. In addition, the CT-FFR was recorded at a measurement distance of 2 lesion lengths below the distal end of the stenosis for comparison with the patient invasive FFR results. The CT-FFR results for both the patients and phantoms were quantitatively compared to determine how accurately the phantoms recreated the patient results. For this analysis, CT-FFR for the patient information was used equally the reference standard.

4. Results

4.1. Phantom Accuracy

V different parameters were measured for both patients and phantoms: minimum diameter, maximum diameter, best fit bore, cantankerous-exclusive area, and tortuosity. Equally mentioned in Sec. iii.3, these measurements were collected at ten-mm increment distances ranging from 10 to 100 mm from the ostium of the LAD, LCX, and RCA. Figures viii(a)–8(due east) show the comparing between patient and phantom CCTA images for the five dissimilar measurements. In all figures, a line of unity is included to show the platonic correlation betwixt the patient and phantom measurements, likewise every bit linear regression values and trendlines. Vessels with known stenosis ( $n = 6$ ) were assessed separately from all vessels to determine the accuracy in diseased vessels specifically.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g008.jpg

Comparison of all measurements betwixt patient and phantom images, (a) minimum diameter, (b) maximum diameter, (c) best fit diameter, (d) cross-exclusive surface area, and (e) tortuosity. A line of unity is included in all graphs to show the ideal comparing.

Table one presents the absolute mean difference and range of differences between the patient and phantom images for all v measurements at the 10 measurement locations, every bit well as the overall difference. Analysis is performed on all vessels collectively and stenosed vessels separately. On average, the phantom diameter measurements were within 1 mm of the patient images and this difference decreased when investigating but the vessels with stenosis. The cross-sectional area had a greater difference in the phantom measurements compared to the patient. And finally, the tortuosity had a very pocket-sized boilerplate deviation for the phantoms, verifying that despite the touch of gravity on our elastic phantoms, we are maintaining the three-dimensional geometry.

Table 1

Comparison of the absolute mean departure for the five geometric measurements along the distal vasculature at each measurement location. The absolute mean divergence and range of differences were calculated for all vessels and simply stenosed vessels.

Measurement location from ostium (mm)		Minimum diameter (mm)	Maximum diameter (mm)	All-time fit diameter (mm)	Cross-sectional surface area ( ${mm}^{2}$ )	Tortuosity
10		$0.xc \pm 0.41$	$0.99 \pm 0.77$	$0.88 \pm 0.28$	$7.17 \pm iii.14$	$0.01 \pm 0.02$
20		$0.63 \pm 0.54$	$i.23 \pm ane.11$	$0.85 \pm 0.57$	$6.47 \pm 5.47$	$0.04 \pm 0.04$
30		$0.69 \pm 0.fifty$	$0.84 \pm 0.55$	$0.75 \pm 0.29$	$four.76 \pm two.18$	$0.05 \pm 0.05$
40		$0.90 \pm 0.64$	$1.13 \pm 0.71$	$0.84 \pm 0.59$	$half-dozen.03 \pm 4.92$	$0.06 \pm 0.04$
50		$0.64 \pm 0.64$	$1.05 \pm 0.72$	$0.57 \pm 0.68$	$4.06 \pm 5.18$	$0.05 \pm 0.05$
sixty		$0.39 \pm 0.xix$	$0.69 \pm 0.63$	$0.44 \pm 0.35$	$3.02 \pm two.68$	$0.06 \pm 0.06$
70		$0.82 \pm 0.67$	$1.18 \pm 1.01$	$0.85 \pm 0.83$	$5.49 \pm v.46$	$0.07 \pm 0.06$
80		$0.92 \pm 0.65$	$i.10 \pm 0.73$	$0.93 \pm 0.threescore$	$5.63 \pm iii.96$	$0.07 \pm 0.07$
90		$0.77 \pm 0.65$	$one.19 \pm ane.16$	$0.91 \pm 0.64$	$5.48 \pm 4.93$	$0.06 \pm 0.06$
100		$0.66 \pm 0.48$	$0.53 \pm 0.44$	$0.54 \pm 0.53$	$3.27 \pm 2.67$	$0.06 \pm 0.07$
Average accented mean departure	All vessels	$0.72 \pm 0.56$	$1.00 \pm 0.82$	$0.75 \pm 0.58$	$5.02 \pm 4.34$	$0.05 \pm 0.05$
Average accented mean departure	Stenosed vessels	$0.66 \pm 0.57$	$0.82 \pm 0.seventy$	$0.64 \pm 0.57$	$4.46 \pm iv.43$	$0.06 \pm 0.06$
Range of differences	All vessels	$- 1.19$ to 2.18	$- four.09$ to 4.47	$- 2.28$ to 2.39	$- fifteen.44$ to nineteen.03	$- 0.ii$ to 0.nineteen
Range of differences	Stenosed vessels	$- 0.92$ to 2.18	$- 2.17$ to ii.49	$- 0.97$ to 2.39	$- 9.36$ to 18.25	$- 0.07$ to 0.19

4.ii. Benchtop FFR

Pressure measurements were collected during flow experimentation to determine the benchtop FFR, divers as the ratio of distal to proximal force per unit area. The benchtop FFR results accept been compared to the invasive FFR as well as the CT-FFR measured on the patient and phantom images.²² ^, ²⁵ All FFR values were measured at approximately the same location as the invasive FFR of two lesion lengths beneath the distal end of the stenosis. The comparison of the iv FFR measurements for the phantoms used in this study is displayed in Table ii. The stenosis grade, or the percent occluded, was measured by 2 users in the phantoms and compared to the patients by recording the minimum diameter in the stenosed region and dividing this value by the diameter prestenosis. The average pct stenosis and standard deviation are reported in Tabular array 2. At that place was an absolute mean difference in the minimum bore in the stenosed region of 0.63 mm, range 0.0 to 2.3 mm, with a reconstructed voxel size of 0.429 mm. Example #five had an iFR (instantaneous wave-free ratio) measurement instead of FFR (indicated by *), which is closely correlated to FFR as FFR = 0.68iFR + 0.18.²⁸ We used this conversion for the data analysis in this paper. Only vessels with stenosis were included as these were the vessels the clinicians recorded the invasive FFR.

Tabular array 2

Comparison of four measurements of FFR (benchtop FFR, invasive FFR, patient CT-FFR, and phantom CT-FFR). Example #5 had an invasive iFR measurement (denoted *), which was converted to an FFR value. Percent stenosis measurements are included for comparison between the patients and phantoms.

	Stenosed vessel	Lesion length (mm)	Patient percentage stenosis (%)	Phantom percentage stenosis (%)	Benchtop FFR	Invasive FFR	Patient CT-FFR	Phantom CT-FFR
Case #1	LAD	25	$59.9 \pm 10$	$53.2 \pm 0.4$	0.76	0.76	0.77	0.72
Example #2	LAD	eleven.3	$47.2 \pm 5$	$28.1 \pm 7.2$	0.81	0.94	0.97	0.95
Case #3	LAD	18.6	$59.0 \pm 12$	$56.3 \pm 12$	0.92	0.90	0.94	0.90
Case #3	LCX	12.1	$62.three \pm 23$	$63.ii \pm 12$	0.91	0.98	0.99	0.97
Case #4	LAD	xv.8	$63.two \pm 10$	$50.7 \pm 12$	0.73	0.76	0.78	0.81
Case #v	RCA	33.two	$66.three \pm 17$	$38.i \pm x$	0.93	0.81*	0.92	0.87

Table iii displays the Pearson correlations between the diverse FFR measurements. In some cases, in that location is significant variance when compared to the phantom results, which can be attributed to the differences in the geometry that were discussed in Sec. iv.ane. While the differences in geometry were on average within 1 mm, the difference is seen in the benchtop FFR and phantom CT-FFR results. However, with the exception of example #4 phantom CT-FFR, all results were in agreement for handling consequence based on the FFR threshold of 0.viii.

Table 3

Comparing of the Pearson correlation values betwixt the four different FFR measurements (benchtop FFR, invasive FFR, patient CT-FFR, and phantom CT-FFR).

	Pearson correlation
Invasive FFR and benchtop FFR	0.57
Invasive FFR and patient CT-FFR	0.92
Invasive FFR and phantom CT-FFR	0.92
Benchtop FFR and patient CT-FFR	0.78
Benchtop FFR and phantom CT-FFR	0.62
Patient CT-FFR and phantom CT-FFR	0.95

four.three. CT-FFR Software

Every bit mentioned in Sec. three.iv, CT-FFR was measured at x mm increments from the ostium of each vessel, with a range from x to 100 mm in patient CT information too as phantom CT information. A comparison was made for the overall correlation betwixt the phantom CT-FFR and patient CT-FFR results. Figure 9 shows the comparison of all CT-FFR results for the iii vessels. A line of unity was also included as the platonic result. The Pearson correlation for all patient CT-FFR and phantom CT-FFR values was 0.81.

An external file that holds a picture, illustration, etc. Object name is JMI-006-021603-g009.jpg

Comparison of all CT-FFR results for both the phantom and the patient.

In addition, the percentage deviation was calculated for each patient to decide the accurateness of the phantoms in replicating the CT-FFR results. As shown in Sec. 4.ane, the phantoms accept some variation from the patient images and this is shown in the CT-FFR results. The absolute hateful per centum difference in the CT-FFR software was calculated for the various distal measurement distances for all five patients was 4.34% and the differences ranged from $- 17.89 %$ to 10%. Overall, the phantoms typically had lower CT-FFR results compared to the patients for 10 out of 14 vessels. The absolute mean percent difference calculations for each of the 3 master coronary vessels for the v cases are displayed in Tabular array 4.

Table 4

Absolute hateful percent difference for patient and phantom CT-FFR results, averaged over all measurement distances for each of the 3 main coronary arteries (LAD, LCX, and RCA). Example #3 has a partially occluded RCA, indicated past (north/a).

	Absolute mean percent departure (%)
Vessel	LAD	LCX	RCA
Case #1	iv.86	5.33	8.89
Case #ii	1.94	8.69	0.64
Case #3	2.15	4.08	n/a
Case #four	iii.49	8.16	2.81
Case #5	iv.42	2.xix	four.47

v. Discussion

We take developed a benchtop system for testing 3D printed patient-specific phantoms with physiological flow and pressure conditions. This system utilizes several features to maintain clinical flow conditions. The utilization of a pulsatile pump allows u.s.a. to simulate flow rates and waveforms present in the coronary arteries. We utilized two flow controls that increase the accuracy of the flow conditions, the aortic flow damper and the distal mechanical clamps on the outflow tubes. The distal outflow manipulation is a disquisitional part of the flow loop as the resistance generated by the precapillary arterioles needs to be mimicked for accurate menstruum experiments. Past research has demonstrated the capabilities of these phantoms for successfully measuring pressure level and computing FFR.²² ^, ²⁵ In add-on, the previous work demonstrated the importance of flow regulation as they presented the pregnant variation between pressure measurements with the improver of distal resistance.²⁵ As of now, the regulation of distal resistance is a manual process and may be different from one experiment to another, depending on main artery lumen geometry, such as diameter, tortuosity, and stenosis. This arroyo was implemented to replicate distal resistance due to the presence of the capillary bed and resulted in some variation of benchtop FFR measurements when compared to the gold standard (invasive FFR) every bit indicated in Tables 2 and 3. This method was used to bypass the current limitations of 3D printing hollow fine structures. In this setup, the differences betwixt benchtop FFR and phantom CT-FFR were no larger than xv%. Whenever a better simulation of capillary bed is needed, the modular characteristic of the 3D printed phantoms allow interfacing with more accurate phantoms that are fabricated with different technologies.²⁹ ^, ³⁰

Our piece of work demonstrates that such phantoms tin be scanned with commercial CT hardware for software validation starting with image acquisition, reconstruction, flow measurements, and figurer simulations. Through the employ of iodine contrast combined with simulated physiologic pulsatile flow, each phantom was successfully imaged using a CCTA protocol similar to the ane used clinically. Visual comparisons of the images have shown that the patient geometry of the coronary vasculature is mimicked in our phantoms, with some small variations. The main difference is the differences in the aorta, which is a result of a pattern option to cut the ends of the aorta to reduce printing costs. Another visual divergence is the pocket-size bumps present from the sensor port additions. The pressure sensor ports may impact the computational fluid dynamics used to measure CT-FFR. In the CT-FFR software the user has the capabilities to alter the vessel contours to ensure the sensor ports are not included in the calculation. There may nonetheless be some inaccuracy in this area, but it will just affect an area of approximately 2 mm on the vessel. This is something that could be addressed past using other pressure sensors, such as the pressure wires used to mensurate FFR invasively. However, these other sensors have unknown implications on the pressure and flow conditions.

The accuracy of our 3D printed patient-specific phantoms was assessed using the segmented geometry from CCTA images and completing several measurements on the coronary arteries. We chose geometric measurements over other metrics, such as the Dice coefficient or Hausdorff distance, as these would yield inaccuracies as the phantoms are fabricated of a soft material and gravity causes sagging of the vessels. While this is a minor difference of a few millimeters, it can generate an fault that is dominated past the vessel misalignment rather than the accuracy of the 3D printed phantoms. These differences were likewise observed in the calculation of the percentage stenosis as measured by two observers in Table 2, with phantoms underestimating the stenosis severity. For the cases in which the calcium burden was depression, the percentage differences were moderate or negligible. All the same, in the cases with severe calcification presence, the differences were significant (instance $5 \sim 40 %$ ). These differences tin be attributed to both CT artifacts in the patient data, as well as limitations in the 3D printing procedure and 3D press cloth.

I solution is to print the phantoms with a harder textile, but then we would lose the reward of the soft material that mimics arterial compliance. Based on the geometric measurements, at that place were differences seen in all of the phantoms, with the diverse bore measurements of the phantoms all within 1 mm of the patient images. Still, this is a meaning deviation since the vessels are in the range of 2-5 mm in bore. The variation in the images could be accounted by the following factors. Whatever discrepancies in segmentation of the patient vasculature to create the phantoms could consequence in the inaccurate measurements. Cardiac motility and CT artifacts from the calcification can cause segmentation discrepancies. This mistake would propagate throughout the results for any data regarding the phantoms. The surface roughness inherent to the 3D printing process might exist ane of the factors by assuasive contrast to perfuse into the vessel wall. Another factor might be that the phantoms expanded during flow experimentation equally the polymer used to create them is a soft polymer with a compliance slightly college than that of the coronary vasculature.^xviii And finally, there may take been slight variations among the measurement locations along the vasculature. Intendance was taken to beginning at approximately the same location, only a slight departure in location may explain some of the differences. These are all factors to consider and they can be minimized when 3D printing patient-specific phantoms.

With the successful imaging of our phantoms under a CCTA protocol, nosotros were able to apply these images in a CT-FFR software. There were some differences between the phantom CT-FFR and patient CT-FFR results, as was expected every bit this is the get-go use of our 3D printed patient-specific coronary phantoms within a diagnostic software. This can be attributed to the variation in the geometry that was measured and reported in this paper. Previous conference reports on this research has demonstrated the use of these phantoms for simulating physiological conditions.²² ^, ²⁵ While the phantom CT-FFR results were not in complete agreement with the patient CT-FFR results, we take demonstrated the capability of using 3D printed patient-specific phantoms within an epitome-based diagnostic software. The invasive FFR and patient CT-FFR bear witness a potent positive correlation compared to the correlations with phantom results, indicating the issues arise in the phantom. Segmentation inside the CT-FFR can as well contribute to the variations. While the software does automatically measure out centerlines and contours of the iii coronary arteries, it is sometimes necessary to manually edit these features, especially in situations with high calcification and image artifacts. Nosotros are continuously improving our technique for manufacturing 3D printed patient-specific phantoms and our benchtop system, and with further experimentation, we believe these results will improve.

3D printing offers the ability to take complete control over the flow experiment, from the capability to replicate complex patient anatomy to simulating the compliance of vasculature. Through the use of 3D printing of patient-specific coronary phantoms and our benchtop system of flow experimentation, we were capable of implementing CCTA images of our phantom in a CT-FFR software and assessing the accurateness of these phantoms.

6. Decision

We have expanded upon previous research using 3D printed patient-specific phantoms to develop a system that utilizes these phantoms with physiological flows and pressures that are capable of being successfully imaged under CT to mimic CCTA. Nosotros have assessed the accuracy of our method of creating 3D printed patient-specific phantoms using the CCTA images. Our results showed that on average, the phantoms were within 1 mm diameter of the patient images. We have presented the accuracy of 3D printing patient-specific phantoms using the current state of the art. As the temporal and spatial resolution of CT scanners and the impress resolution of 3D printers continue to advance, we anticipate the accuracy will proceed to improve.

3D press offers a unique solution for benchtop experimentation as patient-specific phantoms can exist created that replicate the mechanical properties of the vasculature. We have demonstrated the adequacy of our patient-specific phantoms to undergo clinical CT protocols and be utilized within a CT-FFR software. While the phantom accuracy and mechanical behavior can continue to exist improved, this is an of import first step toward using 3D printed patient-specific phantoms for software validation. With further improvement, nosotros believe that 3D printed phantoms and this benchtop system tin be used as a standard tool for validation of not only a CT-FFR software but as well any image-based diagnostic software.

Acknowledgments

This enquiry was a continuation of research published in the SPIE Medical Imaging 2018 conference proceedings, titled "CT investigation of patient-specific phantoms with coronary avenue disease."²⁶ The authors would similar to acknowledge Nitant Karkhanis, Mary Lou Scholl, NP, besides as Dr. Sabee Molloi and Logan Hubbard at UC Irvine for their expert advice and contributions in developing the experimental setup. Lauren Shepard, Kelsey Sommer, and Ciprian N. Ionita were partially funded past a grant from Canon Medical Systems USA. Erin Angel is an employee of Catechism Medical Systems USA. Vijay Iyer, Michael F. Wilson, Frank J. Rybicki, Dimitrious Mitsouras, and Sabee Molloi have no fiscal disclosures related to this research.

Biographies

•

Lauren Yard. Shepard is a 2d-year PhD educatee in the grouping of Dr. Ciprian Due north. Ionita at the University at Buffalo, Canon Stroke and Vascular Research Center. This group has been a very active participant at SPIE Medical Imaging for the last twenty years with nearly 100 presentations and posters and has received various awards for scientific achievements. She has given presentations at four international conferences, including SPIE Medical Imaging and the RSNA almanac coming together.

•

Kelsey N. Sommer is a graduate pupil in the group of Dr. Ciprian N. Ionita at the University at Buffalo, Canon Stroke and Vascular Research Eye. She has given presentations at 4 international conferences, including SPIE Medical Imaging and the RSNA annual coming together, and earned a certificate of merit at the latter.

•

Erin Affections is the senior manager of Clinical Collaborations at Canon Medical Systems U.s.a., Inc. She manages enquiry collaborations with academic and clinical institutions with a goal of clinical translation of medical imaging technologies. Her work focuses primarily on medical imaging in the areas of radiation exposure, image quality, and quantitative prototype assay. She received her doctorate and master'due south degrees in biomedical physics from UCLA. She is actively involved in several professional organizations.

•

Vijay Iyer is a cardiologist in Buffalo, New York, U.s.a.. He received his medical degree from the Grant Medical College at the Maharashtra Academy of Health Sciences and his doctorate caste from Drexel University. He is an associate professor of medicine at the University at Buffalo and the director of Structural Heart Interventions at the Gates Vascular Found.

•

Michael F. Wilson is a cardiologist in Buffalo, New York, U.s.a.. He received his medical degree from Perelman School of Medicine at the University of Pennsylvania and has been in practice for more than 60 years. He is a professor emeritus of medicine at the University at Buffalo and the medical manager of Cardiac CT and Nuclear Cardiology at Kaleida Health in Buffalo, New York, The states.

•

Frank J. Rybicki is a professor and chair of the Department of Radiology at the University of Ottawa and chief of medical imaging at the Ottawa Hospital. He is a chairperson for the American College of Radiology (ACR) Metrics Committee, vascular imaging specialty chair for the ACR Ceremoniousness Criteria, and a member of the joint American College of Cardiology (ACC)/ACR Advisable Use Criteria Job Force Oversight Committee and ACR Radiation Dose Executive Committee.

•

Dimitrios Mitsouras is an associate professor in the Faculty of Medicine at the University of Ottawa. He received his doctorate degree from the Massachusetts Institute of Applied science. He is a cofounder of the Applied Imaging Science Lab at Brigham and Women'due south Hospital.

•

Sabee Molloi is a professor of radiological sciences in the School of Medicine at University of California, Irvine, United states of america. He received his doctorate caste from the University of Wisconsin-Madison. He is the primary investigator of the Imaging Physics Laboratory at the University of California, Irvine, USA.

•

Ciprian N. Ionita is an assistant professor in biomedical engineering and neurosurgery at the Academy at Buffalo. He received his doctorate degree from the Academy at Buffalo. He is the director of the Endovascular Devices and Imaging Lab at the Canon Stroke and Vascular Research Centre.

Disclosure

Vijay Iyer, Michael F. Wilson, Frank J. Rybicki, Dimitrious Mitsouras, and Sabee Molloi accept no financial disclosures related to this research.

References

1. Cury R. C., et al. , "Coronary Artery Affliction-Reporting and Information System (CAD-RADS): an expert consensus document of SCCT, ACR and NASCI: endorsed by the ACC," JACC Cardiovasc. Imaging nine(9), 1099–1113 (2016).10.1016/j.jcmg.2016.05.005 [PubMed] [CrossRef] [Google Scholar]

two. Rybicki F. J., et al. , "2015 ACR/ACC/AHA/AATS/ACEP/ASNC/NASCI/SAEM/SCCT/SCMR/SCPC/SNMMI/STR/STS advisable utilization of cardiovascular imaging in emergency department patients with chest pain: a joint document of the American Higher of Radiology Appropriateness Criteria Committee and the American College of Cardiology Appropriate Employ Criteria Task Forcefulness," J. Am. Coll. Cardiol. 67(7), 853–879 (2016).x.1016/j.jacc.2015.09.011 [PubMed] [CrossRef] [Google Scholar]

3. Tonino P. A., et al. , "Fractional flow reserve versus angiography for guiding percutaneous coronary intervention," Due north. Engl. J. Med. 360(3), 213–224 (2009).x.1056/NEJMoa0807611 [PubMed] [CrossRef] [Google Scholar]

4. Pijls N. H., Sels J.-West. E., "Functional measurement of coronary stenosis," J. Am. Coll. Cardiol. 59(12), 1045–1057 (2012).10.1016/j.jacc.2011.09.077 [PubMed] [CrossRef] [Google Scholar]

5. Petraco R., et al. , "Partial period reserve-guided revascularization: practical implications of a diagnostic gray zone and measurement variability on clinical decisions," JACC Cardiovasc. Interventions 6(iii), 222–225 (2013).10.1016/j.jcin.2012.10.014 [PubMed] [CrossRef] [Google Scholar]

6. Qureshi A. I., et al. , "Prevention and treatment of thromboembolic and ischemic complications associated with endovascular procedures: Part I—pathophysiological and pharmacological features," Neurosurgery 46(half dozen), 1344–1359 (2000).10.1097/00006123-200006000-00012 [PubMed] [CrossRef] [Google Scholar]

seven. Qureshi A. I., et al. , "Prevention and treatment of thromboembolic and ischemic complications associated with endovascular procedures: Role 2—clinical aspects and recommendations," Neurosurgery 46(half-dozen), 1360–1376 (2000).ten.1097/00006123-200006000-00014 [PubMed] [CrossRef] [Google Scholar]

eight. Cook C. Thou., et al. , "Diagnostic accuracy of computed tomography-derived fractional flow reserve: a systematic review," JAMA Cardiol. ii(7), 803–810 (2017).x.1001/jamacardio.2017.1314 [PubMed] [CrossRef] [Google Scholar]

ix. Min J. K., et al. , "Diagnostic accuracy of fractional menstruation reserve from anatomic ct angiography," JAMA 308(12), 1237–1245 (2012).x.1001/2012.jama.11274 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]

10. Ko B., et al. , "Novel non-invasive CT-derived fractional catamenia reserve based on structural and fluid assay (CT-FFR) for detection of functionally significant stenosis: a comparison with invasive fractional menstruum reserve," JACC Cardiovasc. Imaging x(vi), 663–673 (2017).ten.1016/j.jcmg.2016.07.005 [PubMed] [CrossRef] [Google Scholar]

11. Ko B. S., et al. , "Noninvasive CT-derived FFR based on structural and fluid analysis: a comparison with invasive FFR for detection of functionally pregnant stenosis," JACC Cardiovasc. Imaging 10(6), 663–673 (2017).10.1016/j.jcmg.2016.07.005 [PubMed] [CrossRef] [Google Scholar]

12. Koo B.-K., et al. , "Diagnosis of ischemia-causing coronary stenoses by noninvasive partial period reserve computed from coronary computed tomographic angiograms: results from the prospective multicenter DISCOVER-Menstruum (diagnosis of ischemia-causing stenoses obtained via noninvasive partial menstruation reserve) report," J. Am. Coll. Cardiol. 58(19), 1989–1997 (2011).x.1016/j.jacc.2011.06.066 [PubMed] [CrossRef] [Google Scholar]

13. Sankaran S., et al. , "Dubiety quantification in coronary blood flow simulations: bear upon of geometry, boundary conditions and blood viscosity," J. Biomech. 49(12), 2540–2547 (2016).x.1016/j.jbiomech.2016.01.002 [PubMed] [CrossRef] [Google Scholar]

14. Chepelev L., et al. , "Radiological Society of North America (RSNA) 3D printing Special Involvement Grouping (SIG): guidelines for medical 3D printing and appropriateness for clinical scenarios," 3D Print. Med. 4(ane), 11 (2018).10.1186/s41205-018-0030-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]

15. Giannopoulos A. A., et al. , "Applications of 3D printing in cardiovascular diseases," Nat. Rev. Cardiol. 13(12), 701–718 (2016).x.1038/nrcardio.2016.170 [PubMed] [CrossRef] [Google Scholar]

16. Kolli K. Chiliad., et al. , "Event of varying hemodynamic and vascular weather condition on partial flow reserve: an in vitro written report," J. Am. Eye Assoc. five(7), e003634 (2016).ten.1161/JAHA.116.003634 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]

17. Russ M., et al. , "Treatment planning for image-guided neuro-vascular interventions using patient-specific 3D printed phantoms," Proc. SPIE 9417, 941726 (2015).10.1117/12.2081997 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]

eighteen. Sommer K., et al. , "Design optimization for accurate flow simulations in 3D printed vascular phantoms derived from computed tomography angiography," Proc. SPIE 10138, 101380R (2017).ten.1117/12.2253711 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

19. Ionita C. N., et al. , "Challenges and limitations of patient-specific vascular phantom fabrication using 3D polyjet printing," Proc. SPIE 9038, 90380M (2014).10.1117/12.2042266 [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]

20. Ionita C., et al. , "TU‐H‐CAMPUS‐IeP2‐03: evolution of 3D printed coronary phantoms for in‐vitro CT‐FFR validation using data from 320‐detector row coronary CT angiography," Med. Phys. 43(6Part37), 3781–3781 (2016).10.1118/1.4957681 [CrossRef] [Google Scholar]

21. Requicha A. A., Voelcker H. B., "Boolean operations in solid modeling: boundary evaluation and merging algorithms," Proc. IEEE 73(1), 30–44 (1985).x.1109/PROC.1985.13108 [CrossRef] [Google Scholar]

22. Sommer Yard. N., et al. , "3D printed cardiovascular patient specific phantoms used for clinical validation of a CT-derived FFR diagnostic software," Proc. SPIE 10578 105780J (2018).10.1117/12.2292736 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Producers G., A Physical Properties of Glycerine and Its Solutions, Glycerine Producers' Association, New York: (1963). [Google Scholar]

24. Ramanathan T., Skinner H., "Coronary blood flow," Standing Educ. Anaesth. Crit. Care Pain 5(2), 61–64 (2005).10.1093/bjaceaccp/mki012 [CrossRef] [Google Scholar]

25. Shepard L., et al. , "Initial faux FFR investigation using menses measurements in patient-specific 3D printed coronary phantoms," Proc. SPIE 10138, 101380S (2017).10.1117/12.2253889 [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]

26. Shepard Fifty. 1000., et al. , "CT investigation of patient-specific phantoms with coronary artery affliction," Proc. SPIE 10573, 105731V (2018).10.1117/12.2292918 [CrossRef] [Google Scholar]

27. Ihdayhid A. R., et al. , "Performance of computed tomography-derived fractional menstruum reserve using reduced-order modelling and static computed tomography stress myocardial perfusion imaging for detection of haemodynamically significant coronary stenosis," Eur. Middle J. Cardiovasc. Imaging 19(11), 1234–1243 (2018).x.1093/ehjci/jey114 [PubMed] [CrossRef] [Google Scholar]

28. Matsuo H., Kawase Y., Kawamura I., "FFR and iFR," Ann. Nucl. Cardiol. 3(1), 53–threescore (2017).10.17996/anc.17-00036 [CrossRef] [Google Scholar]

29. Forest R. P., et al. , "Initial testing of a 3D printed perfusion phantom using digital subtraction angiography," Proc. SPIE, 9417, 94170V (2015).10.1117/12.2081471 [PMC costless article] [PubMed] [CrossRef] [Google Scholar]

thirty. Eriksson R., et al. , "A microcirculation phantom for performance testing of blood perfusion measurement equipment," Eur. J. Ultrasound two(1), 65–75 (1995).x.1016/0929-8266(94)00077-Q [CrossRef] [Google Scholar]

Articles from Journal of Medical Imaging are provided here courtesy of Club of Photo-Optical Instrumentation Engineers