Background

3D printing is a promising technique that may have applications in medicine, and there is expanding interest in the use of patient-specific 3D models to guide surgical interventions.

Objective

To determine the feasibility of using cardiac CT to print individual models of the aortic root complex for transcatheter aortic valve replacement (TAVR) planning as well as to determine the ability to predict paravalvular aortic regurgitation (PAR).

Methods

This retrospective study included 16 patients (9 with PAR identified on blinded interpretation of post-procedure trans-thoracic echocardiography and 7 age, sex, and valve size-matched controls with no PAR). 3D printed models of the aortic root were created from pre-TAVR cardiac computed tomography data. These models were fitted with printed valves and predictions regarding post-implant PAR were made using a light transmission test.

Results

Aortic root 3D models were highly accurate, with excellent agreement between annulus measurements made on 3D models and those made on corresponding 2D data (mean difference of -0.34 mm, 95% limits of agreement: ± 1.3 mm). The 3D printed valve models were within 0.1 mm of their designed dimensions. Examination of the fit of valves within patient-specific aortic root models correctly predicted PAR in 6 of 9 patients (6 true positive, 3 false negative) and absence of PAR in 5 of 7 patients (5 true negative, 2 false positive).

Conclusions

Pre-TAVR 3D-printing based on cardiac CT provides a unique patient-specific method to assess the physical interplay of the aortic root and implanted valves. With additional optimization, 3D models may complement traditional techniques used for predicting which patients are more likely to develop PAR.

Background

Single photon emission computed tomography (SPECT) has limited ability to identify multivessel and microvascular coronary artery disease. Gamma cameras with cadmium zinc telluride detectors allow the quantification of absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR). However, evidence of its accuracy is limited, and of its reproducibility is lacking. We aimed to validate ^99mTc-sestamibi SPECT MBF and MFR using standard and spline-fitted reconstruction algorithms compared with ¹³N-ammonia positron emission tomography in a cohort of patients with known or suspected coronary artery disease and to evaluate the reproducibility of this technique.

Methods

Accuracy was assessed in 34 participants who underwent dynamic ^99mTc-sestamibi SPECT and ¹³N-ammonia positron emission tomography and reproducibility in 14 participants who underwent 2 ^99mTc-sestamibi SPECT studies, all within 2 weeks. A rest/pharmacological stress single-day SPECT protocol was performed. SPECT images were reconstructed using a standard ordered subset expectation maximization (OSEM) algorithm with (N=21) and without (N=30) application of spline fitting. SPECT MBF was quantified using a net retention kinetic model' and MFR was derived as the stress/rest MBF ratio.

Results

SPECT global MBF with splines showed good correlation with ¹³N-ammonia positron emission tomography (r=0.81, P<0.001) and MFR estimates (r=0.74, P<0.001). Correlations were substantially weaker for standard reconstruction without splines (r=0.61, P<0.001 and r=0.34, P=0.07, for MBF and MFR, respectively). Reproducibility of global MBF estimates with splines in paired SPECT scans was good (r=0.77, P<0.001), while ordered subset expectation maximization without splines led to decreased MBF (r=0.68, P<0.001) and MFR correlations (r=0.33, P=0.3). There were no significant differences in MBF or MFR between the 2 reproducibility scans independently of the reconstruction algorithm (P>0.05 for all).

Conclusions

MBF and MFR quantification using ^99mTc-sestamibi cadmium zinc telluride SPECT with spatiotemporal spline fitting improved the correlation with ¹³N-ammonia positron emission tomography flow estimates and test/retest reproducibility. The use of splines may represent an important step toward the standardization of SPECT flow estimation.