Region-of-interest Image Reconstruction Research Articles

Region of Interest Image Reconstruction for Compton Imaging Using 3-D Position Sensing CdZnTe

Iterative methods, such as list-mode maximum likelihood expectation maximization (LM-MLEM) provide high signal-to-noise ratio reconstructions for Compton imaging. However, MLEM reconstructions can be computationally expensive when reconstructing a high-resolution image over the full field of view (FOV). When employing MLEM for high-resolution imaging, a vast number of data pixels would be needed. This work proposes a region of interest MLEM algorithm (ROI-MLEM) for Compton imaging, using 3-D position sensing CdZnTe, that allows for image reconstruction to take place within a fixed ROI based on prior knowledge of the approximate location of the source. ROI-MLEM is demonstrated by reconstructing simulated and experimental <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">137</sup> Cs source measurements. A preliminary study of the ROI-MLEM performance in high-energy imaging is shown by reconstructing the Compton image of the 4.44 MeV photopeak from a PuBe source. ROI-MLEM shows a 44% improvement in the signal-to-noise ratio (SNR) compared to standard MLEM for simulated source reconstructions in a truncated FOV. The experimental results show the capabilities of estimating the source location with submillimeter pixel resolution at a 30-cm source-to-detector distance, resulting in an average error of 1.4 mm in source location estimation.

Image compression algorithm of floral canopy based on mask hybrid coding for ROI

To achieve high-quality image compression of a floral canopy, a region of interest (ROI) mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the ROI to obtain priority of coding for the ROI-set partitioning in hierarchical trees (ROI-SPIHT) coding. The embedded zerotree wavelet (EZW) coding was conducted for the background (BG) region of the image and a relatively more low-frequency wavelet coefficient was obtained using a relatively small amount of coding. Through the weighing factor r of the ROI coding amount, the proportion of the ROI and BG coding amount was dynamically adjusted to generate embedded, truncatable bit streams. Despite the location of truncation, the image information and ROI mask information required by the decoder can be guaranteed to achieve high-quality compression and reconstruction of the image ROI. The results indicated that under the same bit rate, the larger the r value is, the larger the peak-signal-to-noise ratio (PSNR) for the ROI reconstructed image and the smaller the PSNR for the BG reconstructed image. In the range of 0.07-1.09 bpp, the PSNR of the ROI reconstructed image was 42.65% higher on average than that of the BG reconstructed image, 43.95% higher on average than that of the composite image of the ROI and BG (ALL), and 16.84% higher on average than that of the standard SPIHT reconstructed image. Additionally, the mean square error of the quality evaluation index and similarity for the ROI reconstructed image were both better than those for the BG, ALL, and standard SPIHT reconstructed images. The texture distortion of the ALL image was smaller than that of the SPIHT reconstructed image, indicating that the image compression algorithm based on the mask hybrid coding for ROI (ROI-MHC) is capable of improving the reconstruction quality of an ROI image. When the weighing factor r is a fixed value, as the proportion of ROI (a) increases, the quality of ROI image reconstruction gradually decreases. Therefore, upon the application of the ROI-MHC image compression algorithm, high-quality reconstruction of the ROI image can be achieved through dynamically configuring r according to a. Under the same bit rate, the quality of the ROI-MHC image compression is higher than that of current compression algorithms of same classes and offers promising application opportunities. Keywords: floral canopy, ROI, mask hybrid coding, image compression, algorithm, wavelet transform DOI: 10.25165/j.ijabe.20171005.2772 Citation: Sun G X, Wang X C, Ding Y Q, Li Y H, Zhang B H, Li Y B, et al. Image compression algorithm of floral canopy based on mask hybrid coding for ROI. Int J Agric & Biol Eng, 2017; 10(5): 166–176.

A two-step filtering-based iterative image reconstruction method for interior tomography.

The optimization-based method that utilizes the additional sparse prior of region-of-interest (ROI) image, such as total variation, has been the subject of considerable research in problems of interior tomography reconstruction. One challenge for optimization-based iterative ROI image reconstruction is to build the relationship between ROI image and truncated projection data. When the reconstruction support region is smaller than the original object, an unsuitable representation of data fidelity may lead to bright truncation artifacts in the boundary region of field of view. In this work, we aim to develop an iterative reconstruction method to suppress the truncation artifacts and improve the image quality for direct ROI image reconstruction. A novel reconstruction approach is proposed based on an optimization problem involving a two-step filtering-based data fidelity. Data filtering is achieved in two steps: the first takes the derivative of projection data; in the second step, Hilbert filtering is applied in the differentiated data. Numerical simulations and real data reconstructions have been conducted to validate the new reconstruction method. Both qualitative and quantitative results indicate that, as theoretically expected, the proposed method brings reasonable performance in suppressing truncation artifacts and preserving detailed features. The presented local reconstruction method based on the two-step filtering strategy provides a simple and efficient approach for the iterative reconstruction from truncated projections.

Reconstruction of Region-of-Interest Image in Narrow-Field-of-View Computed Tomography without Prior Constraints

This paper presents a numerical method for reconstructing a tomographic image for a region of interest (RoI) within a section using narrowed radiation beams to reduce radiation exposure. The RoI image reconstruction is formulated as a discrete problem to avoid the truncated (incomplete) problem associated with the conventional analytic filtered backprojection method. This in turn allows local reconstruction of RoI images without any prior information or constraints. A coarse image of the entire section is first reconstructed with the aid of a modified convex maximum likelihood (MCML) algorithm. The coarse image is then used to account for the effect of the RoI surroundings. With RoI-specific projections, an RoI image is then reconstructed, with either the MCML or conjugate gradient methods, at the desired pixel size. The proposed method is evaluated using an anthropomorphic FORBILD head phantom, showing an image quality comparable to that of conventional computed tomography, but with a ~69% reduction in radiation exposure. Unlike existing approaches that require some prior knowledge of a segment of the image, this approach does not require any prior image information and imposes no constraints on the solution.

Conceptual Design and Simulation Study of an ROI-Focused Panel-PET Scanner

Positron emission tomography (PET) is an important imaging modality for clincial use. Conventionally, the PET scanner is generally built to provide a roomy enough transverse field-of-view (FOV) for imaging most adults’ torsos. However, in many cases, the region-of-interest (ROI) for imaging is usually a small area inside the human body. Therefore, to fulfill a PET system which provides an FOV comparable in size to the target ROI seems appealing and more cost effective. Meanwhile, such a PET system has the potential for portable or bedside application with the reduced system size. In this work, we have investigated the feasibility of using dual-headed panel-detectors to build an ROI-focused PET scanner. A novel windowed list-mode ordered subset expectation maximization method was developed to perform the ROI image reconstruction. With this method, the ROI of the object can be reconstructed from the coincidences whose position determined by time-of-flight (TOF) measurements was inside the ROI. Monte Carlo simulation demonstrates the feasibility of detecting lesions not less than 1 cm in diameter, with a 300 ps full width at half maximum timing resolution. As a critical system performance, the impact of TOF information on image quality has been studied and the required TOF capability was assessed. With enhanced timing resolution, the distortions and artifacts were reduced effectively. The further improved TOF capability also shows a noticeable improvement of detection performance for low uptake lesions, as well as the recovery speed of lesion contrast, which is of practical significance in the lesion detection task.

Refining a region-of-interest within an available CT image

This paper describes a numerical method for refining the image of a region-of-interest (RoI) within an existing tomographic slice, provided that projection data are stored along with the image. Using the attributes of the image, projection values (ray-sums) are adjusted to compensate for the material outside the RoI. Advantage is taken of the high degree of overdetermination of common computed tomography systems to reconstruct an RoI image over smaller pixels. The smaller size of a region-of-interest enables the use of iterative methods for RoI image reconstruction, which are less prone to error propagation. Simulation results are shown for an anthropomorphic head phantom, demonstrating that the introduced approach enhances both the spatial resolution and material contrast of RoI images; without the need to acquire any additional measurements or to alter existing imaging setups and systems.

A Local Region of Interest Imaging Method for Electrical Impedance Tomography with Internal Electrodes

Electrical Impedance Tomography (EIT) is a very attractive functional imaging method despite the low sensitivity and resolution. The use of internal electrodes with the conventional reconstruction algorithms was not enough to enhance image resolution and accuracy in the region of interest (ROI). We propose a local ROI imaging method with internal electrodes developed from careful analysis of the sensitivity matrix that is designed to reduce the sensitivity of the voxels outside the local region and optimize the sensitivity of the voxel inside the local region. We perform numerical simulations and physical measurements to demonstrate the localized EIT imaging method. In preliminary results with multiple objects we show the benefits of using an internal electrode and the improved resolution due to the local ROI image reconstruction method. The sensitivity is further increased by allowing the surface electrodes to be unevenly spaced with a higher density of surface electrodes near the ROI. Also, we analyse how much the image quality is improved using several performance parameters for comparison. While these have not yet been studied in depth, it convincingly shows an improvement in local sensitivity in images obtained with an internal electrode in comparison to a standard reconstruction method.

Local tomography based on grey model

This paper studied region-of-interest (ROI) problem of computed tomography. It is necessary to use almost local projection data to reduce radiation exposure or time in medical imaging when only a small part of the patient's body needs to be viewed. Improving the quality of reconstructed ROI and reducing radiation exposure are our aims. However, the traditional local tomography algorithm has difficulty in reconstructing the ROI due to significant truncation artifacts. In this paper, a new grey model based method is reported for ROI image reconstruction from truncated projection data. By using grey model, the proposed method can extrapolate the truncated projection data, and reconstruct the ROI from a set of its projections. As a result, about 75% of full projection data are saved, as compared with other traditional approachs, in reconstructing a local region of 32 pixels in radius in an image of 256×256 pixels. Experimental results show that the proposed method exhibits more saving in exposure as compared with other local tomography algorithms and results in better quality of the reconstructed image.

SPECT region of interest reconstruction with truncated transmission and emission data

The aim of this article is to propose an exact SPECT region of interest (ROI) reconstruction method using truncated transmission and truncated emission data. Recently, the authors published two articles in Physics in Medicine and Biology with two results in SPECT ROI emission image reconstruction. The first result states that if the transmission data are truncated but the emission data are not truncated, the emission image can be exactly reconstructed, provided the entire emission image is inside the region where the transmission data are not truncated. The second result states that if the transmission data are not truncated, the emission ROI can be exactly reconstructed with truncated emission data. This article combines these two results and obtains a new result that the emission ROI can be exactly reconstructed if both transmission and emission data are truncated. Computer simulations are performed to verify the proposed ROI image reconstruction algorithm. Exact SPECT ROI image reconstruction is possible using truncated transmission and emission projections with some prior information about the attenuator and the emission distribution.

A local region of interest image reconstruction via filtered backprojection for fan-beam differential phase-contrast computed tomography

Recently, x-ray differential phase contrast computed tomography (DPC-CT) has been experimentally implemented using a conventional source combined with several gratings. Images were reconstructed using a parallel-beam reconstruction formula. However, parallel-beam reconstruction formulae are not directly applicable for a large image object where the parallel-beam approximation fails. In this note, we present a new image reconstruction formula for fan-beam DPC-CT. There are two major features in this algorithm: (1) it enables the reconstruction of a local region of interest (ROI) using data acquired from an angular interval shorter than 180°+ fan angle and (2) it still preserves the filtered backprojection structure. Numerical simulations have been conducted to validate the image reconstruction algorithm.

Quantitative reconstruction from truncated projections in classical tomography

We prove that some kinds of truncation are entirely admissible for region-of-interest (ROI) reconstructions in classical tomography, contrary to the long-standing folklore that two-dimensional tomography is "all or nothing". The proof is based on a link between the Hilbert transforms of parallel beam and fanbeam projections, which was recently used by Noo et al. to achieve ROI image reconstruction from fanbeam data on less than a short scan, assuming no fanbeam truncation occurs. We extend the use of this link to achieve quantitative ROI reconstruction in the presence of truncated projections. Our results are illustrated with a specific example of a parallel-hole detector of length 24 cm and an elliptical object of 15- and 30-cm axes. The detector rotates 180/spl deg/ about a point on the long axis at 11.25 cm from the right-hand-side boundary. The right half of the ellipse, the ROI in this case, is not truncated, although the left side is truncated in many views. The ROI can be quantitatively reconstructed, as is verified by our simulation. We also show that under certain conditions, ROI reconstruction is possible with truncation on both sides of the field of view. These results bring new understanding to the fundamental mechanisms of tomography. Benefits of this new understanding can be anticipated in many classical tomography applications, particularly when the projections of the object are too wide for the available detector.