Accurate determination of CT point-spread-function with high precision.

The measurement of modulation transfer functions (MTFs) in computed tomography (CT) is often performed by scanning a point source phantom such as a thin wire or a microbead. In these methods the region of interest (ROI) is generally placed on the scanned image to crop the point source response. The aim of the present study was to examine the effect of ROI size on MTF measurement, and to optimize the ROI size. Using a 4 multidetector-row CT, MTFs were measured by the wire and bead methods for three types of reconstruction kernels designated as 'smooth', 'standard', and 'edge-enhancement' kernels. The size of a square ROI was changed from 30 to 50 pixels (approximately 2.9 to 4.9 mm). The accuracies of the MTFs were evaluated using the verification method. The MTFs measured by the wire and bead methods were dependent on ROI size, particularly in MTF measurement for the 'edge-enhancement' kernel. MTF accuracy evaluated by the verification method changed with ROI size, and we were able to determine the optimum ROI size for each method (wire/bead) and for each kernel. Using these optimal ROI sizes, the MTF obtained by the wire method was in strong agreement with the MTF obtained by the bead method in each kernel. Our data demonstrate that the difficulties in obtaining accurate MTFs for some kernels such as edge-enhancement can be overcome by incorporating the verification method into the wire and bead methods, allowing optimization of the ROI size to accurately determine the MTF.

[Electronic portal imaging device (EPID) portal image filtering for simplifying registration on radiation therapy].

A simple method for improving the quality of electronic portal imaging device (EPID) portal images was proposed for the reduction of the burden on the registration between digital reconstruction radiography (DRR) and EPID portal images in radiation therapy. Conventional image filtering techniques in the spatial-frequency domain are applied to the proposed method. While a band-pass filter (BPF) is employed to extract spatial-frequency components included in the bone edge, a high-pass filter (HPF) is employed to obtain the effect corresponding to the general dynamic range compression. The band-pass filtered image is weighted by a parameter for adjusting the bone edge enhancement, and is added to the high-pass filtered image. This method was applied to the portal images in the neck region. In the image obtained by the proposed filtering, the bone edge was clearly observed. In addition, soft tissue structures were identified in the same display settings (window level/width; WL/WW) as the bone edge observation; that is, the adjustment of the display settings was not required for the observation of each object. These results suggested that both bone edge enhancement and dynamic range compression would be achieved successfully. It was estimated that the images obtained by the proposed method were more appropriate for the registration than conventional portal images, in 47 times registrations of 50 times in total (the registrations by five radiological technologists in ten patients). The proposed method was concluded to be useful for improving the quality of portal images, enabling the efficient registration.

Image filtering as an alternative to the application of a different reconstruction kernel in CT imaging: feasibility study in lung cancer screening.

PURPOSE: While the acquisition of projection data in a computed tomography (CT) scanner is generally cqrried out once, the projection data is often removed from the system, making further reconstruction with a different reconstruction filter impossible. The reconstruction kernel is one of the most important parameters. To have access to all the reconstructions, either prior reconstructions with multiple kernels must be performed or the projection data must be stored. Each of these requirements would increase the burden on data archiving. This study aimed to design an effective method to achieve similar image quality using an image filtering technique in the image space, instead of a reconstruction filter in the projection space for CT imaging. The authors evaluated the clinical feasibility of the proposed method in lung cancer screening. METHODS: The proposed technique is essentially the same as common image filtering, which performs processing in the spatial-frequency domain with a filter function. However, the filter function was determined based on the quantitative analysis of the point spread functions (PSFs) measured in the system. The modulation transfer functions (MTFs) were derived from the PSFs, and the ratio of the MTFs was used as the filter function. Therefore, using an image reconstructed with a kernel, an image reconstructed with a different kernel was obtained by filtering, which used the ratio of the MTFs obtained for the two kernels. The performance of the method was evaluated by using routine clinical images obtained from CT screening for lung cancer in five subjects. RESULTS: Filtered images for all combinations of three types of reconstruction kernels ("smooth," "standard," and "sharp" kernels) showed good agreement with original reconstructed images regarded as the gold standard. On the filtered images, abnormal shadows suspected as being lung cancers were identical to those on the reconstructed images. The standard deviations (SDs) for the difference between filtered images and reconstructed images ranged from 1.9 to 23.5 Hounsfield units for all kernel combinations; these SDs were much smaller than the noise SDs in the reconstructed images. CONCLUSIONS: The proposed method has good performance and is clinically feasible in lung cancer screening. This method can be applied to images reconstructed on any scanner by measuring the PSFs in each system.

[Simulation study for artifacts on Gd-EOB-DTPA-enhanced liver dynamic MR imaging in arterial dominant phase].

We performed a simulation for artifacts on liver dynamic MR imaging with the contrast agent gadolinium-ethoxybenzyl (Gd-EOB)-DTPA. The signal enhancement of the image by the contrast agent in the arterial dominant phase was assumed, and the time-enhancement curve was numerically generated. The data in k-space was obtained by the Fourier transform of a liver image. By assuming the scan timing and duration in the time-enhancement curve, the data set of each phase-encoding step in k-space was increased in proportion to the corresponding intensity in the time-enhancement curve. We obtained the simulated image by the Fourier transform of the k-space data, and investigated artifacts in the image. Assuming the use of the centric k-space filling scheme, blurring in the image is found when the scan timing is delayed. When the scan is started in an early timing, we observe the effect of edge enhancement in the image. These artifacts of blurring and edge enhancement are decreased by shortening the scan duration. Assuming the use of the sequential k-space filling scheme, those artifacts are not prominent. The use of the sequential scheme would be effective for the purpose of avoiding the artifacts. It is known that the contrast enhancement would not be sufficient without optimal scan timing; in addition, artifacts should be noted. For basic study of the contrast enhancement and artifacts, our simulation technique based on the time-enhancement curve would be useful.

Determination of point spread function in computed tomography accompanied with verification.

A method for verifying the point spread function (PSF) measured by computed tomography has been previously reported [Med. Phys. 33, 2757-2764 (2006)]; however, this additional PSF verification following measurement is laborious. In the present study, the previously described verification method was expanded to PSF determination. First, an image was obtained by scanning a phantom. The image was then two-dimensionally deconvolved with the object function corresponding to the phantom structure, thus allowing the PSF to be obtained. Deconvolution is implemented simply by division of spatial frequencies (corresponding to inverse filtering), in which two parameters are used as adjustable ones. Second, an image was simulated by convolving the object function with the obtained PSF, and the simulated image was compared to the above-measured image of the phantom. The difference indicates the inaccuracy of the PSF obtained by deconvolution. As a criterion for evaluating the difference, the authors define the mean normalized standard deviation (SD) in the difference between simulated and measured images. The above two parameters for deconvolution can be adjusted by referring to the subsequent mean normalized SD (i.e., the PSF is determined so that the mean normalized SD is decreased). In this article, the parameters were varied in a fixed range with a constant increment to find the optimal parameter setting that minimizes the mean normalized SD. Using this method, PSF measurements were performed for various types of image reconstruction kernels (21 types) in four kinds of scanners. For the 16 types of kernels, the mean normalized SDs were less than 2.5%, indicating the accuracy of the determined PSFs. For the other five kernels, the mean normalized SDs ranged from 3.7% to 4.8%. This was because of a large amount of noise in the measured images, and the obtained PSFs would essentially be accurate. The method effectively determines the PSF, with an accompanying verification, after one scanning of a phantom.