Evaluation of image quality (IQ) in Computed Tomography (CT) is important to ensure that diagnostic questions are correctly answered, whilst keeping radiation dose to the patient as low as is reasonably possible. The assessment of individual aspects of IQ is already a key component of routine quality control of medical x-ray devices. These values together with standard dose indicators can be used to give rise to 'figures of merit' (FOM) to characterise the dose efficiency of the CT scanners operating in certain modes. The demand for clinically relevant IQ characterisation has naturally increased with the development of CT technology (detectors efficiency, image reconstruction and processing), resulting in the adaptation and evolution of assessment methods. The purpose of this review is to present the spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach. When combined together with a dose indicator, a generalised dose efficiency index can be explored in a framework of system and patient dose optimisation. We will focus on the IQ methodologies that are required for dealing with standard reconstruction, but also for iterative reconstruction algorithms. With this concept the previously used FOM will be presented with a proposal to update them in order to make them relevant and up to date with technological progress. The MO that objectively assesses IQ for clinically relevant tasks represents the most promising method in terms of radiologist sensitivity performance and therefore of most relevance in the clinical environment.
Purpose: The purpose of this study was to assess the clinical value of ultra–low-dose computed tomography (ULDCT) compared with chest x-ray radiography (CXR) for diagnosing chest pathology. Materials and Methods: A total of 200 patients referred for CXR by outpatient clinics or general practitioners were enrolled prospectively. They underwent CXR (posteroanterior and lateral) and ULDCT (120 kV, 3 mAs) on the same day. In-room time and effective dose were recorded for each examination. Studies were categorized whether they were diagnostic or not, relevant radiologic diagnostic findings were reported, and confidence for diagnosis was recorded by a Likert scale. Differences in diagnostic confidence and effect on management decision were compared. Results: In-room time was <2 minutes for CXR and <3 minutes for ULDCT. Effective dose was 0.040 mSv for CXR and 0.071 mSv for ULDCT. CXR was considered diagnostic in 98% and ULDCT in 100%. The mean perceived confidence for diagnosis was 88±12% with CXR and 98±2% with ULDCT ( P <0.0001), whereas discrepant findings between CXR and ULDCT were found in 101 of 200 patients. As compared with CXR, ULDCT had added value for management decisions in 40 of 200 patients. Conclusions: ULDCT provided added value to the radiologist by improved perceived confidence with a reduction in false-positive and false-negative CXR investigations that had management implications in 20% of patients. The effective dose of ULDCT will not be a limiting factor for introducing ULDCT of the chest on a broad scale in clinical practice.
The aim of the guideline presented in this article is to unify the test parameters for image quality evaluation and radiation output in all types of cone-beam computed tomography (CBCT) systems. The applications of CBCT spread over dental and interventional radiology, guided surgery and radiotherapy. The chosen tests provide the means to objectively evaluate the performance and monitor the constancy of the imaging chain. Experience from all involved associations has been collected to achieve a consensus that is rigorous and helpful for the practice. The guideline recommends to assess image quality in terms of uniformity, geometrical precision, voxel density values (or Hounsfield units where available), noise, low contrast resolution and spatial resolution measurements. These tests usually require the use of a phantom and evaluation software. Radiation output can be determined with a kerma-area product meter attached to the tube case. Alternatively, a solid state dosimeter attached to the flat panel and a simple geometric relationship can be used to calculate the dose to the isocentre. Summary tables including action levels and recommended frequencies for each test, as well as relevant references, are provided. If the radiation output or image quality deviates from expected values, or exceeds documented action levels for a given system, a more in depth system analysis (using conventional tests) and corrective maintenance work may be required.
PurposeVascular remodeling is a significant pathological feature of various pulmonary diseases, which may be assessed by quantitative computed tomography (CT) imaging. The purpose of this study was therefore to develop and validate an automatic method for quantifying pulmonary vascular morphology in CT images.MethodsThe proposed method consists of pulmonary vessel extraction and quantification. For extracting pulmonary vessels, a graph‐cuts‐based method is proposed which considers appearance (CT intensity) and shape (vesselness from a Hessian‐based filter) features, and incorporates distance to the airways into the cost function to prevent false detection of airway walls. For quantifying the extracted pulmonary vessels, a radius histogram is generated by counting the occurrence of vessel radii, calculated from a distance transform‐based method. Subsequently, two biomarkers, slope α and intercept β, are calculated by linear regression on the radius histogram. A public data set from the VESSEL12 challenge was used to independently evaluate the vessel extraction. The quantitative analysis method was validated using images of a three‐dimensional (3D) printed vessel phantom, scanned by a clinical CT scanner and a micro‐CT scanner (to obtain a gold standard). To confirm the association between imaging biomarkers and pulmonary function, 77 scleroderma patients were investigated with the proposed method.ResultsIn the independent evaluation with the public data set, our vessel segmentation method obtained an area under the receiver operating characteristic (ROC) curve of 0.976. The median radius difference between clinical and micro‐CT scans of a 3D printed vessel phantom was 0.062 ± 0.020 mm, with interquartile range of 0.199 ± 0.050 mm. In the studied patient group, a significant correlation between diffusion capacity for carbon monoxide and the biomarkers, α (R = −0.27, P = 0.018) and β (R = 0.321, P = 0.004), was obtained.ConclusionIn conclusion, the proposed method was validated independently using a public data set resulting in an area under the ROC curve of 0.976 and using a 3D printed vessel phantom data set, showing a vessel sizing error of 0.062 mm (0.16 in‐plane pixel units). The correlation between imaging biomarkers and diffusion capacity in a clinical data set confirmed an association between lung structure and function. This quantification of pulmonary vascular morphology may be helpful in understanding the pathophysiology of pulmonary vascular diseases.
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