Rationale and Objectives
To investigate the impact of iterative reconstruction in image space (IRIS) on image noise, image quality (IQ), and postprocessing at coronary computed tomography angiography (cCTA) compared to traditional filtered back-projection (FBP).
Materials and Methods
The cCTA results of 50 patients (26 men; 58 ± 15 years, body mass index 31.5 ± 6.7 kg/m²) were investigated using a second-generation dual-source computed tomography system. Scan data were reconstructed with the use of IRIS and FBP algorithms. Two radiologists independently evaluated the reconstructions using automated coronary tree analysis software. Image noise was measured and IQ was rated on a 5-point Likert scale. The number of manual corrections after automated vessel segmentation, the time required to complete segmentation, and the number of missed segments were assessed in both IRIS and FBP reconstructions. Results were compared using paired t-test .
Results
IRIS significantly reduced image noise compared to FBP (23.3 ± 8.8 vs. 33.5 ± 13.5 Hounsfield units, P < .001). Subjective IQ improved with IRIS (IRIS 3.2 ± 1.0 vs. FBP 3.0 ± 1.0, P < .05). IRIS decreased the time needed for coronary segmentation from 111.9 ± 40.5 seconds to 95.2 ± 38.2 seconds with FBP ( P < .01) and required fewer manual corrections (5.7 ± 3.0 vs. 6.8 ± 3.6, P < .01). The number of missed vessel segments was not significantly different (3.6 ± 1.8 vs. 3.8 ± 1.9, P > .05) between IRIS and FBP, respectively.
Conclusions
During cCTA postprocessing, IRIS significantly decreases the time and the number of manual corrections for a complete coronary segmentation compared to FBP. This effect is likely attributable to suppression of image noise by IRIS, which improves the performance of automated vessel segmentation and positively impacts cCTA analysis.
Coronary computed tomography angiography (cCTA) has emerged as a powerful noninvasive modality for the evaluation of coronary artery disease (CAD) in patients with low-to-intermediate pretest likelihood . The thin sections used to evaluate small coronary details at cCTA are inherently more susceptible to increased image noise than are thicker-section routine body examinations. Due to the well-known tradeoffs between radiation dose, image noise, and spatial resolution, low-dose radiation protocols aggravate this relationship and possibly lower diagnostic image quality. .
A mainstay of contemporary cCTA image interpretation is image postprocessing using dedicated analysis software for generating curved multiplanar reformats (cMPR) of the vessel course and for addressing other diagnostic aspects obtainable from cCTA. Inaccurate attenuation classification of voxels due to image noise holds the potential to interfere with successful postprocessing, which requires time-consuming manual adjustments. Many studies have investigated the use of postprocessing technique, reconstruction method, and automated and semiautomated segmentation software across a variety of fields and organ systems and with different modalities to determine the possible benefits that can be achieved .
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Materials and methods
Patients
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Scanning Technique and Image Reconstruction
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Image Analysis
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Statistical Analysis
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Results
Patients and Scan Acquisition Protocol
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Table 1
Patient Demographic and cCTA Acquisition Characteristics
Male 26 (52) Age (years) 58.1 ± 14.8 Weight (kg) 90.8 ± 19.1 Height (cm) 170.0 ± 8.9 BMI (kg/m 2 ) 31.5 ± 6.7 Heart rate (bpm) 70.9 ± 14.6 Tube potential (kVp) 116.8 ± 8.4 Tube current (mAs) 329.5 ± 26.4z -axis coverage (cm) 16.1 ± 3.7 DLP (mGy × cm) 586.9 ± 378.4 CTDIvol (mGy) 36.2 ± 22.1 ED (mSv) 8.2 ± 5.3
BMI, body mass index; cCTA, coronary CT angiography; CTDlvol, computed tomography index volume; DLP, dose–length product; ED, effective dose.
Data are n (%) or mean ± standard deviation unless otherwise indicated.
Table 2
Agatston Calcium Scores and Extent of Coronary Artery Disease in This Patient Cohort
Calcium scores ∗ 0 20 (40) 1–10 3 (6) 11–100 2 (4) 101–400 9 (18) >400 10 (20) Vessel disease No vessel disease 21 (42) Single vessel 6 (12) Two vessel 6 (12) Three vessel 17 (34)
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Image Analysis
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Table 3
Image Noise and CT Attenuation in Four Anatomical Regions of Interest, the Ascending Aorta (AA), Descending Aorta (DA), Interventricular Septum (IVS), and Left Ventricle (LV), in IRIS and FBP Reconstructions
ROI Image Noise (HU) Attenuation (HU) FBP IRIS_t_ Value_P_ Value FBP IRIS_t_ Value_P_ Value AA 27.1 ± 8.9 20.8 ± 8.3 12.315 .001 ∗ 409.6 ± 77.7 406.5 ± 76.2 1.871 .067 DA 33.5 ± 13.5 23.3 ± 8.8 8.994 .001 ∗ 377.5 ± 73.7 376.5 ± 74.4 0.809 .422 IVS 30.6 ± 12.8 23.5 ± 9.6 7.233 .001 ∗ 111.0 ± 18.1 110.2 ± 19.1 0.664 .51 LV 32.9 ± 13.1 25.4 ± 10.6 6.035 .001 ∗ 361.2 ± 81.6 359.1 ± 81.9 1.059 .295
FBP, filtered back-projection; IRIS, iterative reconstruction in image space; ROI, region of interest.
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Table 4
Image Analysis and Coronary Segmentation Analysis
IRIS FBP_t_ Value_P_ Value Image noise (HU) 23.3 ± 8.1 31.0 ± 11.1 10.520 .0001 ∗ Image quality 3.2 ± 1.0 3.0 ± 1.0 2.137 .038 ∗ Time (sec) 95.2 ± 38.2 111.9 ± 40.5 3.117 .003 ∗ Manual adjustments 5.7 ± 3.0 6.8 ± 3.6 2.895 .006 ∗ Segments missing 3.6 ± 1.8 3.8 ± 1.9 1.269 .210
FBP, filtered back-projection; IRIS, iterative reconstruction in image space.
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Coronary Segmentation Analysis
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Discussion
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