<title>Accurate and efficient calibration method for a selenium flat-panel-detector-based volume tomographic angiography imaging system</title>

Abstract

The flat panel detector (FPD) has become a highly promising candidate for a wide variety of applications. A prototype selenium thin film transistor (STFT) array-based volume tomographic angiography (VTA) imaging system has been constructed for the feasibility study. This experimental set-up uses a 14" x 17" STFT detector with a 2560 x 3072 array of 14 bit pixels. While an STFT detector offers high resolution digital images, there will always be some defects on the detector. These defects will result in severe streaks and ring artifacts, which have been found in reconstructed images of preliminary phantom studies. It is obvious that the stationary noise sources of the FPD are enhanced by the reconstruction procedure. In this paper, an accurate and efficient FPD calibration method for the VTA imaging system is proposed to reduce the artifacts. An improved gain map and a bad pixel detection method with an adaptive threshold are introduced based on statistical models of the FPD. A more efficient localized and sensitive bad pixel detection ability is obtained by sub-dividing the detector array into sub-arrays, classifying bad pixels as different regional patterns, and then optimizing an interpolation scheme for each pattern. The real-time background correction, gain correction, bad-pixel correction, and methods to generate calibration maps are described in detail. The calibration technique is examined through phantom studies and evaluated by comparing the artifacts and noise in reconstructed images. Improvement of image quality is obtained utilizing the calibration technique. It has been clearly verified that the streaks and ring artifacts in reconstructed VTA images are significantly reduced. Finally, the advantages of our method and future works are also discussed.

Cone beam volume CT image artifacts caused by defective cells in x‐ray flat panel imagers and the artifact removal using a wavelet‐analysis‐based algorithm

“…1,[4][5][6][7][8][9][10][11] Recently, the application of the x-ray FPI in cone beam volume CT ͑CBVCT͒ as a 2-D x-ray detector has increasingly drawn the attention of the CBVCT community, and a few preliminary and interesting results have been presented. [12][13][14][15][16][17][18][19][20] Based upon the technology of fabricating an array of hydrogenated amorphous silicon (a-Si:H) thin-film transistors ͑TFTs͒, the currently available x-ray FPIs can be basically categorized into two types. One is known as an indirect x-ray FPI, in which the x-ray photons are converted into visible photons through an overlying phosphor layer prior to being captured by the a-Si:H photodiodes.…”

“…[21][22][23][24][25][26][27], the image quality of the projection image acquired by currently available x-ray FPIs is being degraded by a few performance drawbacks, e.g., the offset ͑dark͒ image, cell gain nonlinearity and gain nonuniformity, image lag, defective cells, etc. A few approaches to compensate for these degrading factors to projection images 11,24,26,27 and CBVCT images 14,15 have also been proposed.…”

“…31 Hence, an approach capable of recognizing the defective cells occurring in both isolated and nonisolated patterns in an x-ray FPI is essential. [14][15] One approach that extracts the gain, the slope of the response as a function of x-ray exposure, of a cell within an x-ray FPI as the feature to recognize a defective cell has been previously proposed. 15 Nevertheless, as illustrated in Sec.…”

Flat-panel detector-based cone beam volume CT breast imaging: detector evaluation

“…Another phantom study with FPD-based CBVCT showed that the FPD-based system has better spatial resolution and near equal low contrast detectability in comparison to helical CT [1][2][3].Our methods and evaluation protocols used in an early non-real time FPD-based system [4] are helpful in the evaluation of a real time FPD-based CBVCTBI system. These preliminary breast phantom and specimen study results obtained from our current FPD-CBVCT system have been published [5][6][7].…”

Calibration model of a dual gain flat panel detector for 2D and 3D x‐ray imaging

“…Both additional effects described above cannot be sufficiently compensated with the conventional method of offset/ gain correction 15,16 for a certain pixel ͑u , v͒ of a twodimensional ͑2D͒ image with height u 0 and width v 0 :…”