Abstract
Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001). In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.
Highlights
Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke
Image noise in cerebral CT data is problematic for assessment of this characteristic feature of ischemic stroke and can aggravate detection of infarcted areas[8,9,10], because the difference in attenuation of normal brain tissue at the gray matter (GM)/white matter (WM) boundary is as low as 5 to 10 Hounsfield Units (HU)[11]
Vessel occlusion in the CT angiography of initial multi-detector CT (MDCT) examinations was identified in 80 patients, perfusion deficits according to CT perfusion were detected in 83 patients
Summary
Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. FBP Filtered back projection FOV Field of view GM Gray matter HU Hounsfield Units κ Cohen’s kappa LD Low dose MDCT Multi-detector CT MRI Magnetic resonance imaging PACS Picture archiving and communication system R1 Reader 1 R2 Reader 2 ROI Region of interest SD Standard dose SNR Signal-to-noise ratio StdDev Standard deviation WM White matter. Non-contrast cerebral computed tomography (CT) is one of the most frequently performed radiological examinations and the first-line diagnostic approach for emergency evaluation of patients with suspected s troke[1,2,3,4] It is recommended by the American Heart Association as the initial emergency modality for investigation[5], which is mostly thanks to the high speed, wide availability, and feasibility of CT in most institutions. Image noise has to be kept as low as possible in order to improve the visualization of the normal GM/WM interface
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