Rationale and Objectives
The purpose of this study was to evaluate the noise and image quality of images reconstructed with a knowledge-based iterative model reconstruction (knowledge-based IMR) in ultra-low dose cardiac computed tomography (CT).
Materials and Methods
We performed submillisievert radiation dose coronary CT angiography on 43 patients. We also performed a phantom study to evaluate the influence of object size with the automatic exposure control phantom. We reconstructed clinical and phantom studies with filtered back projection (FBP), hybrid iterative reconstruction (hybrid IR), and knowledge-based IMR. We measured effective dose of patients and compared CT number, image noise, and contrast noise ratio in ascending aorta of each reconstruction technique. We compared the relationship between image noise and body mass index for the clinical study, and object size for phantom study.
Results
The mean effective dose was 0.98 ± 0.25 mSv. The image noise of knowledge-based IMR images was significantly lower than those of FBP and hybrid IR images (knowledge-based IMR: 19.4 ± 2.8; FBP: 126.7 ± 35.0; hybrid IR: 48.8 ± 12.8, respectively) ( P < .01). The contrast noise ratio of knowledge-based IMR images was significantly higher than those of FBP and hybrid IR images (knowledge-based IMR: 29.1 ± 5.4; FBP: 4.6 ± 1.3; hybrid IR: 13.1 ± 3.5, respectively) ( P < .01). There were moderate correlations between image noise and body mass index in FBP (r = 0.57, P < .01) and hybrid IR techniques (r = 0.42, P < .01); however, these correlations were weak in knowledge-based IMR (r = 0.27, P < .01).
Conclusion
Compared to FBP and hybrid IR, the knowledge-based IMR offers significant noise reduction and improvement in image quality in submillisievert radiation dose cardiac CT.
Introduction
Attention has recently focused on the potential risks of radiation-induced carcinogenesis from computed tomography (CT) . Although cardiac CT using multidetector CT scanners (MDCT) yields high diagnostic accuracy for the evaluation of coronary artery stenosis and cardiac function, radiation exposure is of concern . According to Hausleiter et al. the mean effective dose (ED) in cardiac CT was 12 mSv; the range was 5–30 mSv about 10 years ago . Low-dose coronary CT angiography (CTA) has recently been performed in several studies ; however, the increased image noise has been a concern, potentially impacting the diagnostic confidence in CTA.
An iterative reconstruction (IR) algorithm was used in the early years of CT but was given up because of the higher computational demands compared to the filtered back projection (FBP) reconstruction algorithms; however, an IR was restored to help reduce the quantum noise at low radiation dose scan . Recent studies suggested the IR technique is well suited for the low-dose cardiac CT . However, these early IR techniques were based on the several simplistic assumptions of the CT system, and the next evolution of the IR technique included a more sophisticated modeling of the real CT system (physical/system models). The knowledge-based iterative model reconstruction (knowledge-based IMR, Philips Healthcare, Cleveland, OH) algorithm is the latest IR technique that includes statistical and physical/system models with a specialized cost function for cardiac, brain, body examinations, and the like. Phantom studies and preliminary clinical studies revealed some promising results from another model-based IR algorithm , but there are only few clinical studies that evaluated the usability of knowledge-based IMR for submillisievert cardiac CT .
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Materials and Methods
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Phantom Experiment
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Patients
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CT Scanning and Contrast Infusion Protocols
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Table 1
Scan Parameters of Low-Dose Cardiac CT
Scan Parameters Beam collimation (mm) 128 × 0.625 Slice thickness (mm) 0.8 Slice intervals (mm) 0.4 Tube voltage (kVp) 100 Tube current (mA) 303 Rotation time (s) 0.27 Effective mAs (mAs/slice) 110 CTDI vol (mGy) 5.2 DLP (mGy*cm) 68 Total amount of contrast medium (mgI/kg) 370 Injection duration (s) 15 Bolus tracking trigger (HU) 110 Scan mode Prospective ECG-triggered axial Scan delay (s) 9
CT, computed tomography; CTDI vol , CT volume dose index; DLP, dose length product; ECG, electrocardiographic; HU, Hounsfield unit.
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CT Image Reconstruction
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Quantitative Image Analysis
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Qualitative Image Analysis
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Statistical Analysis
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Results
Patients
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Table 2
Patient Characteristics and Patient Radiation Dose
Number of patients 43 Mean age (y) 70.6 ± 8.5 Male-to-female ratio 25:18 Mean weight (kg) 57.8 ± 7.4 Mean BMI (kg/m 2 ) 23.4 ± 1.9 EDs (mSv) 4.4 ± 0.4
BMI, body mass index; ED, effective doses.
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Phantom Experiment
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Quantitative Image Analysis
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Table 3
Quantitative Image Analysis
FBP Hybrid IR Knowledge-Based IMR_P_ Value_P_ Value (Pairwise Comparisons) FBP vs Hybrid IR FBP vs Knowledge-Based IMR Hybrid IR vs Knowledge-Based IMR Mean attenuation (HU) 550.6 ± 59.2 550.6 ± 59.3 549.7 ± 59.0 .99 — — — Image noise (HU) 126.7 ± 35.0 48.8 ± 12.7 19.4 ± 3.3 <.01 <0.01 <0.01 <0.01 CNR 4.6 ± 1.3 12.1 ± 3.5 29.1 ± 5.4 <.01 <0.01 <0.01 <0.01
CNR, contrast noise ratio; FBP, filtered back projection; HU, Hounsfield unit; IMR, iterative model reconstruction; IR, iterative reconstruction.
Data are shown as the mean ± standard deviation.
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Qualitative Image Analysis
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Table 4
Results of Qualitative Image Analysis
FBP Hybrid IR Knowledge-Based IMR_P_ Value Kappa_P_ Value (Pairwise Comparisons) FBP vs Hybrid IR FBP vs Knowledge-Based IMR Hybrid IR vs Knowledge-Based IMR Image contrast 3.1 ± 1.0 3.5 ± 0.7 3.6 ± 0.6 .06 0.82 — — — Image noise 1.7 ± 0.6 2.8 ± 0.6 3.9 ± 0.2 <.01 0.76 <0.01 <0.01 <0.01 Image sharpness 1.9 ± 0.7 2.7 ± 0.7 3.8 ± 0.5 <.01 0.61 <0.01 <0.01 <0.01 Image texture 2.8 ± 0.4 2.2 ± 0.5 3.5 ± 0.5 <.01 0.62 <0.01 <0.01 <0.01 Overall image quality 2.0 ± 0.7 2.8 ± 0.5 3.9 ± 0.3 <.01 0.60 <0.01 <0.01 <0.01
FBP, filtered back projection; IMR, iterative model reconstruction; IR, iterative reconstruction.
Data are shown as the mean ± standard deviation.
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Discussion
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