Region Of Interest Reconstruction Research Articles

Enhancing Finite Element-Based Photoacoustic Tomography by Localized Reconstruction Method

Iterative reconstruction algorithm based on finite element (FE) modeling is a powerful approach in photoacoustic tomography (PAT). However, an iterative inverse algorithm using conventional FE meshing of the entire imaging zone is computationally demanding, which hinders this powerful tool in applications where quick image acquisition and/or a large image matrix is needed. To address this challenge, parallel computing techniques are proposed and implemented in the field. Here, we present an alternative approach for 2D PAT, which locoregionally reconstructs the region of interest (ROI) instead of the full imaging zone. Our simulated and phantom experimental results demonstrate that this ROI reconstruction algorithm can produce almost the same image quality as the conventional full zone-based reconstruction algorithm; however, the computation time can be significantly reduced without any additional hardware cost by more than two orders of magnitude (100-fold). This algorithm is further applied and validated in an in vivo study. The major vessel structures in a rat’s brain can be imaged clearly using our ROI-based approach, coupled with a mesh of 11,801 nodes. This novel algorithm can also be parallelized using MPI or GPU acceleration techniques to further enhance the reconstruction performance of FE-based PAT.

Incomplete PET projection data reconstruction method and simulation experiment

In positron emission tomography (PET) imaging, the whole body reconstruction is not required if the region of interest (ROI) is only a part of the object. This paper researched a local image reconstruction algorithm based on the PI-lines, and proposed the derivative-backprojection-Hilbert (DBH) algorithm which can use the incomplete PET projection data to achieve accurate reconstruction of ROI. We designed simulation experiments to evaluate the performance of the DBH algorithm with thorax model as the object of PET image reconstruction, and compared root mean square error (RMSE) of DBH reconstruction and RMSE of filtered backprojection (FBP) reconstruction from simulations data sets with and without noise. The result shows that DBH algorithm has advantages over traditional FBP algorithm, the RMSE decreases by 49.07% and 51.19% with the coincidence events number of 20 million and 40 million, respectively. Compared with traditional maximum-likelihood expectation-maximization (ML-EM), the local iteration algorithm which uses DBH reconstruction as the initial image produces a better reconstructed image at fewer iterations with the coincidence events number of 60 million. DBH algorithm is expected to be applied to fast two-dimensional local image reconstruction and optimize the local iterative reconstruction algorithm in clinical diagnosis.

Iterative micro-tomography of biopsy samples from truncated projections with quantitative gray values

Three-dimensional reconstruction from truncated two-dimensional projections cannot be solved analytically without prior knowledge of the sample or access to the non-truncated projections. To suppress reconstruction artifacts in region of interest (ROI) or local tomography, an iterative algorithm has been devised based on back-projection and re-projection, assuming an approximately cylindrical shape of the entire sample of general homogeneity, which is especially applicable for micro-tomography of biopsy samples from biological tissue. Tomographic reconstruction is iteratively refined by minimizing the mismatch between an empty ROI and the reconstruction from the difference between measured sinogram and forward projected ROI reconstruction. By numerical simulation and experimental demonstration, it is shown that the algorithm is not only able to reconstruct quantitative gray values, but also to reduce artifacts of peripheral glow, and may lead to increased image sharpness. The method is particularly suitable for examination of biopsy/autopsy-samples of soft tissue by micro/nano-computed tomography.

Photon Allocation Strategy in Region-of-Interest Tomographic Imaging

Photon counting detection is a promising approach toward effectively reducing the radiation dose in x-ray computed tomography (CT). Full CT reconstruction from a fraction of the detected photons required by scintillation-based detectors has been demonstrated. Current efforts in photon-counting CT have focused mainly on reconstruction techniques. In medical and industrial x-ray computed tomography (CT) applications, truncated projection from the region-of-interest (ROI) is another effective way of dose reduction, as information from the ROI is usually sufficient for diagnostic purpose. Projection truncation poses an ill-conditioned inverse problem, which can be improved by including projections from the exterior region. However, this trade-off between the interior reconstruction quality and the additional exterior measurement (extra dose) has not been studied. In this manuscript, we explore the number of detected x-ray photons as a new dimension for measurement engineering. Specifically, we design a flexible, photon-efficient measurement strategy for ROI reconstruction by incorporating the photon statistics at extremely low flux level (~10 photons per pixel). The optimized photon-allocation strategy shows 10 ~ 15-fold lower ROI reconstruction error than truncated projections, and 2-fold lower than whole-volume CT scan. Our analysis in few-photon interior tomography could serve as a new framework for dose-efficient, task-specific x-ray image acquisition design.

Accuracy improvement of interior region of interest reconstruction using field modulation CT acquisition

Region-of-interest (ROI) imaging is considered an effective method to reduce the exposure dose. We propose ROI-based field modulation acquisition to improve image accuracy inside the ROI and restore information outside the ROI. In this study, we compared the interior image quality of a field modulation CT image with that of a conventional ROI reconstruction method image. A prototype of the CT system (TVX-IL1500H, GERI, Korea), which had a 1016 × 760 pixel flat panel detector, was used. The source-to-detector and the source-to-center of the rotation distance were 1178 mm and 905 mm, respectively. A scout-view reconstruction method was used as a comparative interior ROI reconstruction method. A total of 720 projection data were obtained, from which full-view CT reconstructed images were obtained as reference images using filtered back projection. Interior ROI CT images were reconstructed using the 720 truncated projection data. Scout-view reconstruction was also conducted with the 720 truncated projection data, and we corrected the truncation using six scout views that used the full-size projection data from the previous scan. The proposed field modulation CT image was reconstructed using 714 truncated and 6 full-size projection data from the same scan. Using field modulation CT imaging, it was possible to obtain a better interior reconstructed image compared with scout-view reconstruction. For a quantitative comparison with the existing ROI imaging techniques, we calculated the structural similarity, contrast to noise ratio, and figure of merit values. In conclusion, we have verified the possibility of applying field modulation acquisition in CT imaging systems. In addition, we have also quantitatively compared the proposed field modulated CT images with other images obtained by conventional ROI reconstruction methods.

An Efficient Design Approach of ROI Based DWT Using Vedic and Wallace Tree Multiplier on FPGA Platform

<span lang="EN-US">In digital image processing, the compression mechanism is utilized to enhance the visual perception and storage cost. By using hardware architectures, reconstruction of medical images especially Region of interest (ROI) part using Lossy image compression is a challenging task. In this paper, the ROI Based Discrete wavelet transformation (DWT) using separate Wallace- tree multiplier (WM) and modified Vedic Multiplier (VM) methods are designed. The Lifting based DWT method is used for the ROI compression and reconstruction. The 9/7 filter coefficients are multiplied in DWT using Wallace- tree multiplier (WM) and modified Vedic Multiplier (VM). The designed Wallace tree multiplier works with the parallel mechanism using pipeline architecture results with optimized hardware resources, and 8x8 Vedic multiplier designs improves the ROI reconstruction image quality and fast computation. To evaluate the performance metrics between ROI Based DWT-WM and DWT-VM on FPGA platform, The PSNR and MSE are calculated for different Brain MRI images, and also hardware constraints include Area, Delay, maximum operating frequency and power results are tabulated. The proposed model is designed using Xilinx platform using Verilog-HDL and simulated using ModelSim and Implemented on Artix-7 FPGA device.</span>

Interior x-ray diffraction tomography with low-resolution exterior information

X-ray diffraction tomography (XDT) resolves spatially-variant XRD profiles within macroscopic objects, and provides improved material contrast compared to the conventional transmission-based computed tomography (CT). However, due to the small diffraction cross-section, XDT suffers from long imaging acquisition time, which could take tens of hours for a full scan using a table-top x-ray tube. In medical and industrial imaging applications, oftentimes only the XRD measurement within a region-of-interest (ROI) is required, which, together with the demand to reduce imaging time and radiation dose to the sample, motivates the development of interior XDT systems that scan and reconstruct only an internal region within the sample. The interior problem does not have a unique solution, and a direct inversion on the truncated projection data often leads to large reconstruction errors in ROI. To reduce the truncation artifacts, conventional attenuation-based interior reconstruction problems rely on a known region or piecewise constant constraint within the ROI. Here we propose a quasi-interior XDT scheme that incorporates a small fraction of projection information from the exterior region to assist ROI reconstruction. In the phantom simulation, a small amount (17% of exterior region) of added exterior projection data improves the reconstruction quality by ~50%. The addition of exterior samplings in the experiment demonstrates improved spatial and XRD profile reconstructions compared to total-variation-based reconstruction or sinogram extrapolation. We expect our quasi-interior XDT to obviate the requirement on prior knowledge of the object or its support, and to allow the ROI reconstruction to be performed with the fast, widely-used filtered back-projection algorithm for easy integration into real-time XDT imaging modules.

Reconstructing a 3D heart surface with stereo-endoscope by learning eigen-shapes.

An efficient approach to dynamically reconstruct a region of interest (ROI) on a beating heart from stereo-endoscopic video is developed. A ROI is first pre-reconstructed with a decoupled high-rank thin plate spline model. Eigen-shapes are learned from the pre-reconstructed data by using principal component analysis (PCA) to build a low-rank statistical deformable model for reconstructing subsequent frames. The linear transferability of PCA is proved, which allows fast eigen-shape learning. A general dynamic reconstruction framework is developed that formulates ROI reconstruction as an optimization problem of model parameters, and an efficient second-order minimization algorithm is derived to iteratively solve it. The performance of the proposed method is finally validated on stereo-endoscopic videos recorded by da Vinci robots.

Motion Tracking for Beating Heart Based on Sparse Statistic Pose Modeling.

A novel region-based method to track beating heart is proposed. Sparse statistical pose modeling is used to reconstruct the region of interest (ROI) on beating heart surface. Firstly, a high-complexity thin plate spline is employed to pre-reconstructed the ROI of a series of frames. The 3D pose data of the ROI from the pre-reconstructed results are extracted to train a low-complexity model based on the sparse statistical analysis. The new trained low-complexity model is robust and efficient for ROI reconstruction of the following frames. The proposed model significantly reduces the redundant degrees of freedom to fit the surface of the heart. A constraint item is added to the objective function which describes the 3D tracking problem to avoid erroneous convergence of the efficient second-order minimization (ESM) optimization algorithm. The new proposed method is evaluated on the phantom heart video and the in vivo video obtained by the da Vinci surgical system.

Optimization-based region-of-interest reconstruction for X-ray computed tomography based on total variation and data derivative

Region-of-interest (ROI) and interior reconstructions for computed tomography (CT) have drawn much attention and can be of practical value for potential applications in reducing radiation dose and hardware cost. The conventional wisdom is that the exact reconstruction of an interior ROI is very difficult to be obtained by only using data associated with lines through the ROI. In this study, we propose and investigate optimization-based methods for ROI and interior reconstructions based on total variation (TV) and data derivative. Objective functions are built by the image TV term plus the data finite difference term. Different data terms in the forms of L1-norm, L2-norm, and Kullback–Leibler divergence are incorporated and investigated in the optimizations. Efficient algorithms are developed using the proximal alternating direction method of multipliers (ADMM) for each program. All sub-problems of ADMM are solved by using closed-form solutions with high efficiency. The customized optimizations and algorithms based on the TV and derivative-based data terms can serve as a powerful tool for interior reconstructions. Simulations and real-data experiments indicate that the proposed methods can be of practical value for CT imaging applications.

Limited-Range Few-View CT: Using Historical Images for ROI Reconstruction in Solitary Lung Nodules Follow-up Examination.

Repeated CT scans are known to increase the risk of cancer; thus, it is paradoxical to use multiple follow-up CT scans to monitor the development of a lung nodule and conduct early treatment of the nodule. In the case of a solitary lung nodule, regional scanning and region of interest (ROI) reconstruction are likely to restore the internal area at the nodule. A limited-range few-view CT is proposed in this paper for lung nodule follow-ups with extremely reduced X-radiation. For a planned scanning of an ROI, where a solitary lung nodule is positioned, a limited-range few-view CT can be employed, and thus, less tissue is exposed to X-radiation per view. An ROI reconstruction method is also proposed that makes full use of the former standard lung scan. The experimental results show that the nodule size and shape are preserved. In the case of a 40-mm ROI, the number of exposed X-rays can be reduced by 99.6% for a circular scan and 99.9% for a 3-D scan.

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.

Accurate Transaxial Region-of-Interest Reconstruction in Helical CT?

In conventional helical computed tomography (CT), the field-of-view is a cylinder centered on the axis of the helix. Here, we consider the situation where all measurement lines are blocked except those intersecting a small cylindrical region-of-interest (ROI) not necessarily centered on the axis of the system. We address the question of image reconstruction inside the ROI. The patient boundary is assumed known, and we avoid the “interior problem” by assuming that the ROI includes part of the patient boundary. By applying analytic image reconstruction theory, we show that the entire cylindrical ROI can be reconstructed provided the pitch of the helix does not violate the well-known Tam–Danielsson detector condition. Using an iterative algorithm, we performed ROI reconstruction from simulated phantom data and from real patient data, and compared the results with full-field reconstructions. Visually, the ROI reconstructed images perfectly matched the full-field reconstructions. However, there were small quantitative discrepancies near the interior boundaries of the ROIs, which we attribute to the known reduced stability at one side of the inverse truncated Hilbert transform. In conclusion, we have demonstrated mathematically that accurate transverse ROI reconstruction is possible for helical CT, although care must be taken near the interior boundary to achieve quantitative accuracy.

Iterative Region-of-Interest Reconstruction from Limited Data Using Prior Information

In practice, computed tomography and computed laminography applications suffer from incomplete data. In particular, when inspecting large objects with extremely different diameters in longitudinal and transversal directions or when high resolution reconstructions are desired, the physical conditions of the scanning system lead to restricted data and truncated projections, also known as the interior or region-of-interest (ROI) problem. To recover the searched-for density function of the inspected object, we derive a semi-discrete model of the ROI problem that inherently allows the incorporation of geometrical prior information in an abstract Hilbert space setting for bounded linear operators. Assuming that the attenuation inside the object is approximately constant, as for fibre reinforced plastics parts or homogeneous objects where one is interested in locating defects like cracks or porosities, we apply the semi-discrete Landweber–Kaczmarz method to recover the inner structure of the object inside the ROI from the measured data resulting in a semi-discrete iteration method. Finally, numerical experiments for three-dimensional tomographic applications with both an inherent restricted source and ROI problem are provided to verify the proposed method for the ROI reconstruction.

On stable region-of-interest reconstruction in tomography: examples of non-existence of bounded inverse

In applications of computerized tomography (CT) it is often of interest to reconstruct a function in a proper subset, the region of interest (ROI), of its support from a proper subset of a full CT-scan. In several recent works it has been shown that stable ROI reconstruction is possible in certain limited data situations. In this paper we investigate the limits of what is possible in that direction by proving that reconstruction in an interior ROI is severely unstable for a certain set of data that contains integrals over all lines that intersect the ROI and is large enough for the unknown function to be uniquely determined.

Application of a dual-resolution voxellation scheme to small ROI reconstruction in iterative CBCT for the reduction of computational cost

In iterative methods for cone-beam computed tomography (CBCT) reconstruction, the use of a huge system matrix is the primary computational bottleneck and is still an obstacle to the more widespread use of these methods in practice. In this paper, to put iterative methods to practical applications, we propose a pragmatic idea, the-so-called dual-resolution voxellation scheme, for a small region-of-interest (ROI) reconstruction in CBCT in which voxels outside the ROI are binned with a double resolution such as 2×2×2, 4×4×4, 8×8×8, 16×16×16, etc., and the voxel sizewithin the ROI remains unchanged. In some situations of medical diagnosis, physicians are interested only in a small ROI containing a target diagnosis from the examined structure. We implemented an efficient compressed-sensing (CS)-based reconstruction algorithm with the proposed voxellation scheme incorporated and performed both simulation and experimental works to investigate the imaging characteristics. Our results indicate that the proposed voxellation scheme seems to be effective in reducing the computational cost considerably for a small ROI reconstruction in iterative CBCT, with the image quality inside the ROI not being noticeably impaired.

Scaling calibration in region of interest reconstruction with the 1D and 2D ATRACT algorithm

Recently, a reconstruction algorithm for region of interest (ROI) imaging in C-arm CT was published, named Approximate Truncation Robust Algorithm for Computed Tomography (ATRACT). Even in the presence of substantial data truncation, the algorithm is able to reconstruct images without the use of explicit extrapolation or prior knowledge. However, the method suffers from a scaling and offset artifact in the reconstruction. Hence, the reconstruction results are not quantitative. It is our goal to reduce the scaling and offset artifact so that Hounsfield unit (HU) values can be used for diagnosis. In this paper, we investigate two variants of the ATRACT method and present the analytical derivations of these algorithms in the Fourier domain. Then, we propose an empirical correction measure that can be applied to the ATRACT algorithm, to effectively compensate the scaling and offset issue. The proposed method is evaluated on ten clinical datasets in the presence of different degrees of artificial truncation. With the proposed correction approach, we achieved an average relative root-mean-square error (rRMSE) of 2.81% with respect to non-truncated Feldkamp, Davis, and Kress reconstruction, even for severely truncated data. The rRMSE is reduced to as little as 10% of the image reconstructed without the scaling calibration. The reconstruction results show that ROI reconstruction of high accuracy can be achieved since the scaling and offset artifact are effectively eliminated by the proposed method. With this improvement, the HU values may be used for post-processing operations such as bone or soft tissue segmentation if some tolerance is accepted.

Top-level design and pilot analysis of low-end CT scanners based on linear scanning for developing countries.

The goal is to develop new architectures for computed tomography (CT) which are at an ultra-low-cost for developing countries, especially in rural areas. The proposed general scheme is inspired by the recently developed compressive sensing and interior tomography techniques, where the data acquisition system targets a region of interest (ROI) to acquire limited and truncated data. Similar to linear tomosynthesis, the source and detector are translated in opposite directions but in contrast to conventional tomosynthesis, our proposal is for either ROI reconstruction with one or more localized linear scans or global reconstruction by combining multiple ROI reconstructions. In other words, the popular slip ring is replaced by a translation based setup, and the instrumentation cost is reduced by a relaxation of the imaging speed requirement. The various translational scanning modes are theoretically analyzed, and the scanning parameters are optimized. The numerical simulation results from different numbers of linear scans confirm the feasibility of the proposed scheme, and suggest two preferred low-end systems for horizontal and vertical patient positions respectively. Ultra-low-cost x-ray CT is feasible with our proposed combination of linear scanning, compressive sensing, and interior tomography. The proposed architecture can be tailored into permanent, movable, or reconfigurable systems as desirable. Advanced image registration and spectral imaging features can be included as well.

Null-space function estimation for the interior problem

In single-photon emission computed tomography (SPECT), projection data can be truncated when the camera's field of view is smaller than the object to be imaged. Using truncated projections to reconstruct a region of interest (ROI) is a reality we must face if small detectors are used. The truncated data result in an underdetermined system of imaging equations, which may lead to non-unique solutions. Data sampling and photon attenuation may also affect the solution uniqueness and stability. The uniqueness of the solutions in the ROI can be investigated by studying the null-space functions in the ROI. This paper uses an iterative algorithm to estimate the null-space image, to determine the sampling conditions under which a stable ROI reconstruction is possible with truncated data and to investigate whether attenuation can influence the ROI reconstruction bias. This iterative algorithm is validated by the singular value decomposition method. We show that if the ROI is sufficiently sampled, the null-space image is close to zero inside the ROI, and any almost-zero offset is insignificant in SPECT, because the noise is a much more dominating degradation factor.

New approaches to region of interest computed tomography

In classical x-ray CT, the diameter of the field of measurement (FOM) must not fall below the transversal diameter of the patient or specimen. Thereby, the ratio of the diameter of FOM and the number of transversal detector elements typically defines the spatial resolution. The authors aim at improving the spatial resolution within a region of interest (ROI) by a factor of 10-100 while maintaining artifact-free CT image reconstruction inside and outside the ROI. Two novel methods are proposed for artifact-free reconstruction of the truncated ROI scan (data weighting method and data filtering method) and compared with the gold standard (data completion method) for this problem. First, an overview scan with low spatial resolution and a large FOM that exceeds the object transversally is performed. Second, a high-resolution scan is performed, where the scanner's magnification is changed such that the FOM matches the ROI at the cost of laterally truncated projection data. The gold standard is forward projecting the low-resolution scan on the rays missing in the high-resolution scan. The authors propose the data filtering method, which uses the low-resolution reconstruction and calculates a high frequency correction term from the high-resolution scan, and the data weighting method, which reconstructs the truncated high-resolution data and calculates a detruncation image from the low-resolution data. The methods are compared using a simulation of the Forbild head phantom and a measurement of a spinal disk implant. The results of the data weighting method and the data completion method show the same image quality. The data filtering method yields slightly inferior image quality that may still be sufficient for many applications. Both new methods considerably outperform the data completion method regarding the computational load. The new ROI reconstruction methods are superior to the gold standard regarding the computational load. Comparing the image quality with the gold standard, the data filtering method is slightly inferior and the data weighting method yields equal quality.