CT phantom image acquisition
In this study, a Catphan 504 phantom (The Phantom Laboratory, Salem, NY, USA) was used to perform all image quality tests. It is a cylindrical phantom of 20-cm length and 20-cm diameter, containing several test modules: a solid image uniformity module (CTP486), a 21-line pair and point source high resolution module (CTP528), a module for slice width, sensitometry and pixel size evaluation (CTP401), and a low-contrast module (CTP515). In particular, the low contrast CTP515 module contains two sub-regions: the supraslice region with three groups of low-contrast objects, consisting of nine circular objects with diameters in the range of 2–15 mm and contrast of 0.3%, 0.5% and 1.0%, respectively, and a subslice region with three groups of four circular objects each (diameters in the range of 3–9 mm, contrast of 1.0%).
CT scans of the Catphan phantom were acquired with a Philips 256 iCT multi-slice CT unit (Brilliance iCT, Philips, Best, The Netherlands), aligning the phantom main axis with the axis of rotation of the scanner (z-axis). Acquisitions were performed selecting a beam collimation of 128 × 0.625 mm, scanning field of view of 214 mm, scan length of 213 mm, helical acquisition with 0.976 pitch factor, tube voltage of 100 kVp, scan time 2.5 s, rotation time 0.5 s, slice thickness 3 mm and zoom 100%.
The product of tube current and exposure time per rotation (i.e. tube load) was in the range of 15–300 mAs. Image reconstruction was performed using the following reconstruction algorithms: FBP; iDose with levels in the range of 1–6; and IMR with levels in the range of 1–3.
Software for image analysis
For image quality analysis, one software product was used and compared, in terms of numerical results, with an advanced automated quality assurance software service available on the web. For the definition of low-contrast detectability, however, studies based on both human and model observers were performed.
CT image quality parameters were evaluated with two different software resources, CTQA_cp and Catphan QA (Image Owl, Inc., Greenwich, NY, USA), in order to cross-check the obtained results and validate CTQA_cp results with a reference. CTQA_cp (version 0.3.1) is a freeware software package developed to aid CT quality assurance programs and able to automatically produce image quality reports. In particular, the following parameters are analysed with CTQA_cp: slice thickness, pixel size, CT number linearity, uniformity, homogeneity, image noise across detector rows, and modulation transfer function (MTF). A low-contrast resolution analysis tool of the Catphan CP515 module based on a model observer is also available.
Catphan QA executes an automatic analysis of CT Catphan images and produces an image quality report. The following CT imaging performance parameters are evaluated: sensitometry; MTF (i.e. from beads and wires analysis); critical frequency; CT linearity; phantom position; rotation and yaw; slice width; and contrast detectability.
Catphan QA also includes a contrast diameter detail function that returns dimensions of the smallest detectable target for each of the three contrast values and was used in order to obtain image quality low-contrast information.
Image quality parameters were evaluated with CTQA_cp and Catphan QA and on the phantom images acquired with the different CT mAs values and reconstructed with different reconstruction algorithms.
Physical metrics quantification with CTQA_cp and Catphan QA
For each adopted scanning protocol (i.e. different mAs) and reconstruction algorithms, noise, uniformity, and high-contrast spatial resolution were evaluated in order to quantify how the different CT acquisition parameters impact on the physical metrics. Both CTQA_cp and Catphan QA were used and the obtained results were compared.
Noise
Noise was characterised on the images of the Catphan CTP 485 uniform module as the standard deviation of pixel values within a square region of interest (ROI) located at the centre of the phantom module.
Uniformity
Uniformity was calculated in the homogeneous region of the CP486 module as the deviation in CT numbers of the mean value of upper, right, lower, and left circular off-centre ROIs from the mean value of a ROI placed at the centre of the image of the phantom. Position and dimension of the ROIs could change between the two software products. In any case, the closer to unity was the result, the more uniform was the image.
High-contrast spatial resolution
MTF was calculated as the Fourier transform of the point spread function of a region of interest centred on the lower bead point object of the Catphan CTP 528-point source module.
Low-contrast spatial resolution
As described below, empirical and computational methods were evaluated in this study to quantify low-contrast spatial resolution.
Evaluation with the four-alternative forced test
Four-alternative forced-choice (4-AFC) [13] test was executed to evaluate low-contrast spatial resolution by five radiologists with at least 15 years of experience in clinical CT and four experienced radiology technicians [14]. Observers were trained on all technical aspects and objectives of the study and frontal training was performed through examples before the test.
The 4-AFC test was performed in a darkened room with a constant level of low ambient lighting and images were presented on a DICOM-calibrated megapixel colour LCD screen (Radiforce RX320 LCD, EIZO Corporation) with a native resolution of 1536 × 2048. Initial window and level values of 100 and 1090 were suggested, respectively, but observers were free to modify them if necessary. No limitations on viewing distance and time were set and no reference image was provided before the start. Each human observer analysed 543 stacks of four images containing either just background or the 6-mm and 7-mm diameter objects (1% contrast) of the low-contrast supraslice region of the Catphan phantom. To create the stack of images for the 4-AFC test, dedicated macros were created using the freeware software ImageJ (National Institute of Health Image, Bethesda, MD, USA) that automatically executes the following steps: (1) extracts samples of the low-contrast objects (diameters 6–7 mm, 1% contrast) or of the background from low-contrast Catphan module (Fig. 1); (2) generates a series of images each containing four quadrants with low-contrast circular objects or background, randomly chosen from Catphan images acquired at different experimental conditions (i.e. mAs in the range of 30–300) and reconstructed by means of FBP, iDOSE (i.e. levels 1–6) and IMR (i.e. levels 1–3). Sixteen images for each CT protocol modality were overall selected and randomly arranged over the stack of 543 images. One example of images is provided in Fig. 2, each quadrant possibly representing the particular of the Catphan CTP515 low-contrast module shown in Fig. 1; (3) creates a stack of 543 images in a single DICOM image sequence that was loaded to a picture archiving and communication system PACS (Philips IntelliSpace PACS Enterprise 4.4.532.1, Philips Healthcare Informatics, Inc., Foster City, CA, USA) for further evaluation by the observers.
In this study, observers had to identify the presence of one or more quadrants with low-contrast lesions and to indicate their position within the image. In principle, in each one of the 543 images, low-contrast objects were in none, one, two, three, or any quadrant. The percentage of correct answers given by each observer subjected to the 4-AFC experiment was analysed and evaluated. Inter-CT protocol modality (i.e. each combination of mAs and reconstruction algorithms) analysis was performed.
Computational evaluation
The computer model observer provided with CTQA_cp was used to define low-contrast detectability on the Catphan low-contrast supraslice images acquired at different experimental conditions (i.e. mAs in the range of 15–300) and reconstructed by means of FBP, iDOSE (i.e. levels 1–6) and IMR (i.e. levels 1–3). According to the method, which is exhaustively described by Hernandez-Giron et al. [6], output of the software system is the smallest ‘visible’ object size at 1%, 0.5%, and 0.1% contrast. Only objects with 1% contrast were evaluated because 0.5% and 0.1% objects were often not visible during first visual evaluations after phantom CT acquisitions. Catphan QA also includes a function for low-contrast diameter detail evaluation, which returns dimensions of the smallest detectable target for each of the three contrast values. This function is not based on a model observer-based statistical approach, but it is related on the use of an algorithm for image analysis.