Introduction 1 6 7 9 10 11 12 13 14 17 18 20 MSCT plaque assessment is based upon the variable X-ray attenuation of the tissue components. However, several parameters, such as lumen attenuation, convolution filtering, body mass index of the patient and contrast-to-noise ratio (CNR) of the images, are able to modify the attenuation values that are being used to define the composition of coronary plaques. The aim of the present study was to investigate the effect of increasing convolution filtering on plaque components with varying coronary attenuation in an ex vivo coronary model. Material and methods Specimens 1 Fig. 1  CX  LAD  LM  The specimens were prepared and examined separately. Each coronary artery was fitted with two cannulas fixed with surgical thread in the proximal (in the left main) and distal ends (in the LAD). The circumflex artery had been closed earlier at its end with thread. Only the LAD was used because of the major length (segments considered with a mean length of 7.57 cm) and higher prevalence of atherosclerotic disease and for technical reasons (ability to fix a surgical thread in the left main, without hampering the evaluation of the proximal LAD). Contrast material Two solutions were used: a saline and a 1/50 dilution of contrast material (400 mgI/ml Iomeprol; Bracco, Italy). The attenuation values (Hounsfield Units, HU) of the two solutions, measured in a 10-ml syringe after dilution, were 15.9 ± 1.8 HU (1/∞; defined as saline; no contrast material was diluted) and 414.8 ± 5.6 HU (1/50; defined as contrast solution). We preferred to use a 1/50 dilution to achieve a mean coronary in-lumen density >300 HU – which is not typically achieved in in vivo cardiac computed tomography angiography (CTA) studies – in order to emphasize the difference with the saline solution. Our study benefits from two consecutive scans of the same vessel (e.g. with and without contrast media) for a comparative evaluation of plaques, which is not truly feasible during in vivo studies due to high radiation exposure. Experimental settings A box was filled with olive oil. Prior to positioning the specimen in the oil, saline was injected through the sheaths to wash out the air bubbles in the lumen as much as possible. Once the specimen had sunk into the oil, which simulated epicardial fat, the solution was instilled through the sheaths using a 10-ml syringe from the proximal end of the specimen. The injection was finished when the solution was observed leaking out of the distal end of specimen. The leaking solution was removed from the specimen using a syringe. The same procedure was used to fill the LAD with both solutions. The specimen was kept for all investigations in the same longitudinal position (head-LM-to feet-distal end of LAD ) to obtain direct cross-section images. Scan parameters A MSCT scan (Somatom Sensation 16; Siemens, Germany) was performed following the intra-coronary injection of two solutions. Scans were performed at the following parameters: slices/collimation, 16/0.75 mm; rotation time, 375 ms; feed per rotation 3.0 mm (pitch: 0.25 mm); 120 kv; 400 mAs; effective slice thickness, 1 mm; reconstruction increment, 0.5 mm; field of view (FOV), 100 mm. Four convolution filters were used: b30f (smooth), b36f (medium smooth), b46f (medium) and b60f (sharp). Based on our clinical experience in routine evaluation, b30f was used instead of b20f because of a better delineation of the coronary arteries and atherosclerotic lesions. The scan geometry was based on a retrospective electrocardiogram (ECG)-gated protocol (the same used for the in vivo examination). This protocol is based on a low pitch that allows a retrospective reconstruction of multiple phases within the cardiac cycle. A demo-ECG was switched on and a heart rate of 71 occurred during the scan. The half-rotation reconstruction algorithm brought the effective temporal resolution down to 187 ms. Data collection and analysis 21 2 2 Statistical analysis The attenuation values are presented as means and standard deviations. The signal-to-noise ratio (SNR) was calculated as the mean attenuation value of the ROI/standard deviation of the surrounding oil attenuation value. The standard deviation HU value of the surrounding air is often used for the estimation of noise. However, we did not include the air in our images. The standard deviation of the surrounding oil can estimate the noise because of its reliable signal. SNRs are presented as means for each kernel. The contrast-to-noise ratio (CNR) was calculated as (mean attenuation A – mean attenuation B)/image noise (defined as the standard deviation of the surrounding oil). CNRs of (lumen – noncalcified plaque), (noncalcified plaque – surrounding), (calcified plaque – noncalcified plaque) and (calcified plaque – lumen) were calculated and presented as average for each kernel and solution. SPSS  p t Results n 1 p p r r r r r Table 1 Summary of the attenuation values measured in each structure in the two solutions (e.g. saline and contrast solutions) with increasing convolution filter   b30f b36f b46f b60f Mean SD SNR Mean SD SNR Mean SD SNR Mean SD SNR Lumen Saline 58.1 40.4 29.9 52.1 40.5 9.4 48.2 36.0 19.1 23.5 6.0 1.8 Contrast 329.1 93.7 209.8 312.6 85.5 44.0 334.1 91.7 169.7 331.3 138.3 17.8 Noncalcified Plaque Saline 20.8 39.1 8.0 14.2 35.8 2.4 14.0 32.0 4.0 3.2 32.4 0.3 Contrast 74.7 66.6 39.3 68.2 63.3 9.2 66.3 66.5 25.9 48.5 60.0 2.4 Calcified Plaque  Saline 740.5 392.0 327.0 758.5 360.3 133.6 785.7 388.5 294.9 1145.8 517.4 87.8 Contrast 795.4 333.8 498.0 838.7 364.2 116.0 885.6 382.6 488.7 1194.8 520.7 61.0 Surrounding Saline 127.1 2.7 −54.7 −128.0 3.1 −22.6 −128.7 2.7 −47.6 −134.8 11.3 −10.5 Contrast −128.2 2.6 −84.5 −126.8 4.3 −17.6 −128.5 2.4 −7.2 −123.7 10.0 −6.4 2 Fig. 2 Mean attenuation of the four structures in the two solutions (e.g. saline and contrast) with increasing convolution filters. Whereas the convolution filter does not significantly affect the attenuation values of the lumen and the surrounding, sharper filters decrease the attenuation of the noncalcified plaque and, conversely, increase the attenuation of the calcified plaque with both solutions  3 Fig. 3 The SNR values appear to have a common trend with increasing convolution filtering and both solutions (e. g. saline and contrast). However, the best SNR is achieved with b30 and b46 filters. Noncalcified plaque values follow the lumen values, while the surrounding and the calcified plaque patterns are similar  2 Table 2 Summary of CNR values calculated between structures with increasing convolution filters and different solutions   b30f b36f b46f b60f Saline Lumen – noncalcified plaque 21.8 7.1 15.1 1.5 Non – calcified plaque-surrounding 62.7 25.0 51.6 10.8 Calcified plaque – noncalcified plaque 318.9 131.3 290.8 87.4 Calcified plaque – lumen 297.1 124.2 275.7 85.9 Contrast solution Lumen – noncalcified plaque 170.6 34.8 143.8 15.4 Noncalcified plaque – surrounding 123.7 26.9 97.1 8.8 Calcified plaque – noncalcified plaque 458.7 106.7 462.8 58.5 Calcified plaque – lumen 288.2 71.9 319.0 43.1 p r  r r r Discussion 14 20 14 15 17 16 22 18 20 Nevertheless, several aspects (i.e. the impact of lumen attenuation, convolution filters, body mass index of the patient, CNR of the images and coronary calcification) of the methodology need to be better addressed in order to validate the accuracy of attenuation values measured within the coronary plaques using MSCT. 23 24 25 26 4 Fig. 4 Example of plaque with increasing filtering using a saline and contrast solution  27 28 3 29 29 30 28 31 The pathological correlation has not been provided since the aim of our study was not to compare pathology with MSCT, but to assess the influence of convolution filtering on plaque attenuation measurement. In conclusion, the use of different convolution filtering significantly modified plaque attenuation values. Therefore, convolution filtering should be reported when attenuation values are shown in order to provide a standardization of the methodology. Sharper convolution kernels increased the attenuation of the calcium within the coronary plaques and reduced the attenuation of soft plaque tissues. Improved SNR and CNR were achieved by using the b30f and b46f filters. A smooth filter (e.g. b30f) may be used for clinical routine evaluation, while a medium filter (e.g. b46f) may be considered the best choice for the assessment of highly calcified or stented vessels. The use of a proper filtering according to plaque type could give a more reliable assessment of plaque attenuation values in terms of HU.