ABSTRACT
Background: Coronary artery disease (CAD) is a frequent comorbidity in patients undergoing transcatheter aortic valve implantation (TAVI). If significant CAD can be excluded on coronary CT-angiography (cCTA), invasive coronary angiography (ICA) may be avoided. However, a high plaque burden may make the exclusion of CAD challenging, particularly for less experienced readers. The objective was to analyze the ability of machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) to correctly categorize cCTA studies without obstructive CAD acquired during pre-TAVI evaluation and to correlate recategorization to image quality and coronary artery calcium score (CAC). Methods: In total, 116 patients without significant stenosis (≥50% diameter) on cCTA as part of pre-TAVI CT were included. Patients were examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the torso. Patients were re-evaluated with ML-based CT-FFR (threshold = 0.80). The standard of reference was ICA. Image quality was assessed quantitatively and qualitatively. Results: ML-based CT-FFR was successfully performed in 94.0% (109/116) of patients, including 436 vessels. With CT-FFR, 76/109 patients and 126/436 vessels were falsely categorized as having significant CAD. With CT-FFR 2/2 patients but no vessels initially falsely classified by cCTA were correctly recategorized as having significant CAD. Reclassification occurred predominantly in distal segments. Virtually no correlation was found between image quality or CAC. Conclusions: Unselectively applied, CT-FFR may vastly increase the number of false positive ratings of CAD compared to morphological scoring. Recategorization was virtually independently from image quality or CAC and occurred predominantly in distal segments. It is unclear whether or not the reduced CT-FFR represent true pressure ratios and potentially signifies pathophysiology in patients with severe aortic stenosis.
ABSTRACT
RATIONALE AND OBJECTIVES: To evaluate the image quality and suitability of Dual-Source Dual-Energy CT venography (DSDE-CTV) with asynchronous virtual monoenergetic images (VMI+) of the entire lower extremity in the context of pre-surgical assessment of complex cases prior to coronary bypass graft as a feasibility study. MATERIALS AND METHODS: Fifteen consecutive patients, consisting of 5 females and 10 males with an average age of 52 ± 17 years underwent DSDE-CTV from the pubic symphysis to the ankles after intravenous injection of an iodinated contrast medium. DSDE-CTV was acquired with tube voltages of 80 kVp and sn140 kVp. Single spectrum images (A - 80 kVp; B - 140 kVp) as well as a linearly blended mixed data set (M_0.6) were reconstructed. By postprocessing, an VMI+ dataset at 40 keV was generated. Objective image quality parameters of the deep and superficial veins of thigh, knee, and calves were measured separately for each location. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Additionally, subjective image quality was assessed independently by two radiologists. RESULTS: Mean vascular attenuation was 73.9 ± 17.8 HU at B, 113.7 ± 42.2 HU at M_0.6, 119.4 ± 45.5 HU at A, and 201.0 ± 89.7 HU at VMI+. Mean CNR was 6.7 ± 2.0 at 140 keV, 9.25 ± 2.3 in the M_0.6 datasets, 8.7 ± 3.0 at 80 keV, and 12.9 ± 4.3 at 40 keV. Attenuation values were approximately doubled when compared to the reference standard (M_0.6) with significantly improved SNR and CNR (p < 0.05). Subjective image quality scores were highest for VMI+ datasets (4.1 ± 0.5) and lowest for B datasets (2.3 ± 0.37), however differences between VMI+ datasets and M_0.6 datasets did not reach statistical significance. CONCLUSION: Postprocessing of dual-energy CTV with VMI+ significantly increases attenuation of veins and markedly improves SNR and CNR values, thereby improving the diagnostic quality of CTV for the evaluation of deep and superficial veins of the entire lower limb prior to coronary bypass graft.
