Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation.
Lauren M ShepardKelsey N SommerErin AngelVijay IyerMichael F WilsonFrank J RybickiDimitrios MitsourasSabee MolloiCiprian N IonitaPublished in: Journal of medical imaging (Bellingham, Wash.) (2019)
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.
Keyphrases
- image quality
- dual energy
- computed tomography
- contrast enhanced
- coronary artery disease
- coronary artery
- deep learning
- healthcare
- magnetic resonance imaging
- ejection fraction
- aortic stenosis
- magnetic resonance
- positron emission tomography
- pulmonary artery
- randomized controlled trial
- data analysis
- case report
- aortic valve
- minimally invasive
- prognostic factors
- convolutional neural network
- aortic dissection
- patient reported outcomes
- pet ct