Clinical application of four-dimensional noise reduction filtering with a similarity algorithm in dynamic myocardial computed tomography perfusion imaging.

We aimed to evaluate the effects of four-dimensional noise reduction filtering using a similarity algorithm (4D-SF) on the image quality and hemodynamic parameter of dynamic myocardial computed tomography perfusion (CTP). Sixty-eight patients who underwent dynamic myocardial CTP for the assessment of coronary artery disease were enrolled. Dynamic CTP was performed using a 320-row CT with low tube voltage scan (80 kVp). Two different datasets of dynamic CTP were reconstructed using iterative reconstruction (IR) alone and a combination of IR and 4D-SF. Qualitative (5-grade scale) and quantitative image quality scores were assessed, and the CT-derived myocardial blood flow (CT-MBF) was quantified. These results were compared between the two different CTP images. The qualitative image quality in CTP images reconstructed with IR and 4D-SF was significantly higher than that with IR alone (noise score: 4.7 vs. 3.4, p < 0.05). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in CTP images reconstructed with IR and 4D-SF were significantly higher than those with IR alone (SNR: 20.6 vs. 9.7; CNR: 7.9 vs. 3.9, respectively; p < 0.05). There was no significant difference in mean CT-MBF between the two sets of CTP images (3.01 vs. 3.03 mL/g/min, p = 0.1081). 4D-SF showed incremental value in improving image quality in combination with IR without altering CT-MBF quantification in dynamic myocardial CTP imaging with a low tube potential.