Coronary artery disease (CAD) is a condition where there is blood-flow reduction in the coronary artery due to plaque build-up. The current standard to diagnose CAD severity is fractional flow reserve (FFR) using the ratio of distal and proximal stenotic pressure measurements. This work investigated the use of a machine-learning classifier of CAD severity. Sixty-four coronary CT angiographies (CCTA) were collected at 70% through the cardiac R-R cycle. Eight straightened curved planar reformations (SCPRs) were reconstructed from each CCTA considering 45° increments around the coronary artery centerline. FFR measurements were considered ground truth to train a convolutional neural network to predict CAD severity based on the 0.80 FFR threshold. Classification accuracy and area under the receiver operating characteristic curve (AUROC) were used to assess the network’s predictive capacity. SCPR data were optimized using class-activation maps, and the network was re-trained and assessed in the same manner. Subgroup analysis of the network’s performance was carried out considering different coronary artery branches and patient FFR measurements in and out of the FFR grey-zone. Different network input conditions were assessed such as SCPR slice-thickness and SCPR reconstruction using the minimum or average value across the vessel centerline. Network for CAD severity prediction was significantly higher (P<0.05) using thicker SCPR slices. No significant difference was found in network performance using SCPRs from different coronary artery branches, or considering SCPR reconstruction using the minimum or average value. This work indicates that a CNN can predict CAD severity using coronary artery SCPRs.
Purpose: 3D printing has become an accepted radiological tool which allow accurate physical renderings of organs for diagnosis and treatment planning. Use of 3D printed phantoms to replicate blood flow conditions have been reported, however, comprehensive studies comparing in-patients and in-vitro measurements are scarce. We propose to study whether 3D-printed patient specific coronary benchtop models, can be used to study how variations in outflow boundary conditions influence benchtop fractional flow reserve (FFR) measurements and how these compare with a research CT-FFR software. Materials and Methods: Fifty-two CT-derived patient-specific 3D-printed coronary phantoms were used for comprehensive flow experiments and a benchtop-FFR (B-FFR) was evaluated along the diseased arteries. A programmable cardiac pulsatile pump provided six coronary outflow rates equally distributed between normal and hyperemic blood flow conditions (250-500 mL/min). B-FFR results were compared to catheter lab Invasive-FFR (IFFR) measurements and a CT-FFR research software. The effect of coronary outflow changes was compared with catheter lab diagnosis using operator characteristics (ROC) and Area Under ROC (AUROC). Results: The highest AUROC was for B-FFR-500, 0.82 (95% CI: 0.65-0.92,), and gradually decreased as the flow rate decreased to B-FFR-250, 0.79 (95% CI: 0.70-0.87). The CT-FFR AUROC was 0.80 (95% CI: 0.69-0.86). Conclusion: 3D-printed patient specific coronary phantoms and controlled flow experiments demonstrated significant agreement between hyperemic simulated flow B-FFR-500 and I-FFR. We also observed not negligible variations of the B-FFR for small coronary outflow rates changes, implying that slight changes in outflow conditions may results in diagnosis change, especially in the 0.75-0.85 FFR range.
KEYWORDS: 3D modeling, Arteries, 3D metrology, Computational fluid dynamics, Computed tomography, Gold, Computer simulations, 3D printing, 3D imaging standards, Angiography
Purpose: Various CT-FFR methods are being proposed as a non-invasive method to estimate cardiac disease severity. 3D printed patient specific cardiovascular models with high geometric accuracy can be used to simulate blood flow conditions and perform precise and repeatable benchtop flow experiments for validation of such methods. Materials and Methods: Twelve patient-specific 3D printed cardiac phantoms were created from CT Angiography (CTA) scans using a compliant 3D printing material. Pressure sensors were connected to the aortic root and distal ends of the three main coronary arteries to measure benchtop pressure gradients for each stenosed vessel. The patient geometries were used in Canon Medical Systems 1D fluid dynamics algorithm to calculate the CT- FFR. In addition, a 3D computational fluid dynamics simulation was done using ANSYS to estimate pressure gradients across the coronary arteries. Experimental data and 1D and 3D flow simulations were compared to the standard catheter lab FFR measurement (Invasive-FFR). Results: The average percent difference in Benchtop FFR/Invasive FFR, CT-FFR/Invasive FFR, and CFDFFR/Invasive FFR was 0.05, 0.06, and 0.07 respectively. The average time it took for the CT-FFR simulation was ~35 minutes and it took ~15 hours for the CFD-FFR simulation but can vary based on the number of iterations the user defines the software to run. Conclusions: Benchtop FFR proved to be highly accurate when compared to both 1D and 3D CFD software and therefore, 3D printing of patient specific coronary phantoms is a quality tool for CT-FFR software validation.
We developed three-dimensionally (3D) printed patient-specific coronary phantoms that are capable of sustaining physiological flow and pressure 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 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 flow 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 data from the phantoms were input to a CT-FFR research software (Canon Medical Systems) and compared to those derived from the clinical data, along with comparisons between image 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 4.34% and Pearson correlation of 0.95. We have demonstrated the capabilities of 3D printed patient-specific phantoms in a diagnostic software.
Purpose: 3D printed patient specific vascular models provide the ability to perform precise and repeatable benchtop experiments with simulated physiological blood flow conditions. This approach can be applied to CT-derived patient geometries to determine coronary flow related parameters such as Fractional Flow Reserve (FFR). To demonstrate the utility of this approach we compared bench-top results with non-invasive CT-derived FFR software based on a computational fluid dynamics algorithm and catheter based FFR measurements.
Materials and Methods: Twelve patients for whom catheter angiography was clinically indicated signed written informed consent to CT Angiography (CTA) before their standard care that included coronary angiography (ICA) and conventional FFR (Angio-FFR). The research CTA was used first to determine CT-derived FFR (Vital Images) and second to generate patient specific 3D printed models of the aortic root and three main coronary arteries that were connected to a programmable pulsatile pump. Benchtop FFR was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers.
Results: All 12 patients completed the clinical study without any complication, and the three FFR techniques (Angio-FFR, CT-FFR, and Benchtop FFR) are reported for one or two main coronary arteries. The Pearson correlation among Benchtop FFR/ Angio-FFR, CT-FFR/ Benchtop FFR, and CT-FFR/ Angio-FFR are 0.871, 0.877, and 0.927 respectively.
Conclusions: 3D printed patient specific cardiovascular models successfully simulated hyperemic blood flow conditions, matching invasive Angio-FFR measurements. This benchtop flow system could be used to validate CTderived FFR diagnostic software, alleviating both cost and risk during invasive procedures.
Purpose: To develop coronary phantoms that mimic patient geometry and coronary blood flow conditions for CT imaging optimization and software validation. Materials and Methods: Five patients with varying degrees of coronary artery disease underwent 320-detector row coronary CT angiography (Aquilion ONE, Canon Medical Systems). The aorta and coronary arteries were segmented using a Vitrea Workstation (Vital Images). Patient anatomy was manipulated in Autodesk Meshmixer and 3D printed in Tango+, a flexible polymer, using an Eden260V printer (Stratasys). Phantoms were connected to a pump that simulates physiologic pulsatile flow waveforms, correlated with a simulated ECG signal. Distal resistance was optimized for all three coronary vessels until physiologically accurate flow rates and pressure were observed. Phantoms underwent coronary CT Angiography (CTA) using a standard acquisition protocol and contrast mixed in the flow loop. Image data from the phantoms were input to a CT-FFR research software and compared to those derived from the clinical data. Results: All five patient-specific phantoms were successfully imaged with CTA and the images were analyzed by the CTFFR software. The phantom CT-FFR results had a mean difference of -5.4% compared to the patient CT-FFR results. Patient and phantom CT-FFR agreed for all three coronary vessels, with Pearson correlations r = 0.83, 0.68, 0.62 (LAD, LCX, RCA). Conclusions: 3D printed patient-specific phantoms can be manipulated through material properties, flow regulations, and a pulsatile waveform to create accurate flow conditions for CT based experimentation.
KEYWORDS: Resistance, Data modeling, Medical research, 3D modeling, Arteries, Computed tomography, Image segmentation, Sensors, 3D image processing, Angiography
Purpose: Accurate patient-specific phantoms for device testing or endovascular treatment planning can be 3D printed. We
expand the applicability of this approach for cardiovascular disease, in particular, for CT-geometry derived benchtop
measurements of Fractional Flow Reserve, the reference standard for determination of significant individual coronary
artery atherosclerotic lesions.
Materials and Methods: Coronary CT Angiography (CTA) images during a single heartbeat were acquired with a
320x0.5mm detector row scanner (Toshiba Aquilion ONE). These coronary CTA images were used to create 4 patientspecific
cardiovascular models with various grades of stenosis: severe, <75% (n=1); moderate, 50-70% (n=1); and mild,
<50% (n=2). DICOM volumetric images were segmented using a 3D workstation (Vitrea, Vital Images); the output was
used to generate STL files (using AutoDesk Meshmixer), and further processed to create 3D printable geometries for flow
experiments. Multi-material printed models (Stratasys Connex3) were connected to a programmable pulsatile pump, and
the pressure was measured proximal and distal to the stenosis using pressure transducers. Compliance chambers were used
before and after the model to modulate the pressure wave. A flow sensor was used to ensure flow rates within physiological
reported values.
Results: 3D model based FFR measurements correlated well with stenosis severity. FFR measurements for each stenosis
grade were: 0.8 severe, 0.7 moderate and 0.88 mild.
Conclusions: 3D printed models of patient-specific coronary arteries allows for accurate benchtop diagnosis of FFR.
This approach can be used as a future diagnostic tool or for testing CT image-based FFR methods.
KEYWORDS: 3D printing, Surgery, Sodium, Manufacturing, 3D modeling, 3D image processing, Medical devices, Medical research, Image segmentation, Arteries, Neodymium, Printing, Medical imaging
Complex vascular anatomies can cause the failure of image-guided endovascular procedures. 3D printed patient-specific
vascular phantoms provide clinicians and medical device companies the ability to preemptively plan surgical treatments,
test the likelihood of device success, and determine potential operative setbacks. This research aims to present advanced
mesh manipulation techniques of stereolithographic (STL) files segmented from medical imaging and post-print surface
optimization to match physiological vascular flow resistance. For phantom design, we developed three mesh
manipulation techniques. The first method allows outlet 3D mesh manipulations to merge superfluous vessels into a
single junction, decreasing the number of flow outlets and making it feasible to include smaller vessels. Next we
introduced Boolean operations to eliminate the need to manually merge mesh layers and eliminate errors of mesh self-intersections
that previously occurred. Finally we optimize support addition to preserve the patient anatomical geometry.
For post-print surface optimization, we investigated various solutions and methods to remove support material and
smooth the inner vessel surface. Solutions of chloroform, alcohol and sodium hydroxide were used to process various
phantoms and hydraulic resistance was measured and compared with values reported in literature. The newly mesh
manipulation methods decrease the phantom design time by 30 - 80% and allow for rapid development of accurate
vascular models. We have created 3D printed vascular models with vessel diameters less than 0.5 mm. The methods
presented in this work could lead to shorter design time for patient specific phantoms and better physiological
simulations.
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