Information For Image Reconstruction Research Articles

Parametric study of blind-label acoustic subwavelength imaging

There is a long-existing tradeoff between the imaging resolution and the penetration depth in imaging systems caused by the diffraction limit. We developed a “blind label” approach to tackle this problem, which significantly improves the practicality of acoustic subwavelength imaging in biomedical ultrasound imaging, non-destructive testing, and other acoustic sensing and communication applications. The “blind labels” in our system refer to randomly distributed acoustic scatterers with deep-subwavelength sizes whose exact locations and trajectories are not necessary information in image reconstruction. Our imaging framework is composed of two parts: (1) spatial mixing: a physical process that converts the originally evanescent components in the scattered waves from the object to propagating components that can reach the far-field detector and (2) computational reconstruction. In this talk, we will mainly report our quantitative investigation of the system parameters’ impact on the performance of the blind-label subwavelength imaging system, providing guidance to future system setups in various applications.

A threshold optimization strategy for reducing the noise of co-planar array capacitive imaging

Co-planar array capacitive imaging is a non-destructive testing technique based on the sensing of dielectric properties. As the number of independent electrodes in the sensor grows, handling noise becomes more and more critical. In this paper, numerical models and practical tests were used to explore the noise analysis and processing of the output signals of a 12-electrode co-planar array capacitive sensor. The effects of different geometric placements and distances on the output signal strength and sensitivity of electrode pairs were thoroughly assessed, revealing that distal interpolar spacing electrode pairs were particularly vulnerable to noise. As a consequence, a threshold optimization strategy was presented for decreasing the noise. Results revealed that threshold optimization efficiently reduced noise and emphasized the useful information for image reconstruction. The artifacts and deviations of the reconstructed images were reduced, and the structural similarity and Pearson product-moment correlation coefficients were boosted. The methodologies and findings may provide a reference for signal processing in co-planar array capacitive imaging.

Evaluation of a Hamamatsu TOF-PET Detector Module With 3.2-mm Pitch LFS Scintillators and a 256-Channel SiPM Array

Using time-of-flight (TOF) information in image reconstruction improves image signal-to-noise ratio and quantitative accuracy in positron emission tomography (PET). A new TOF-PET detector module with a 3.2-mm pitch lutetium fine silicate (LFS) scintillator array one-to-one coupled to a 16×16 (256-ch) silicon photomultiplier (SiPM) array was made commercially available (C13500 series, Hamamatsu Photonics K. K.). In this study, as a candidate detector module for the next-generation brain-dedicated PET, we changed the scintillator length of 20 to 10 mm according to our helmet-type PET geometry and investigated the basic performance of the PET detector module, including its energy resolution, sensitivity, coincidence response function (CRF), and coincidence timing resolution (CTR). All performance values were compared with those of the 4.2-mm pitch 144-ch detector module which was selected for our current helmet-type PET prototype. The energy resolution was 12% at 511 keV. The sensitivity was about 10% lower compared with those of the 4.2-mm pitch module. The average full width at half maximum (FWHM) of the CRFs was 1.9 mm. The CTRs had values of 235-241 ps with various energy windows after applying timing calibration. The CTR was not significantly changed (<; 10 ps) at the single count rate of 412 kcps or lower.

Images compression by using cubic spline-functions methods

The object of research is image compression algorithms based on mathematical methods. The main problem with image compression is loss of quality during recovery. The approach is proposed in which the user can determine the quality of the reconstructed image itself. This is achieved due to the use of the spline interpolation method, which allows to set the compression ratio, thus controlling the quality of the decoded image.The use of the spline function for image compression makes it possible to reduce the processing time of files due to the simplicity of the mathematical model of the algorithm. Given the accuracy of the restored image, the algorithm determines the size of the compressed file, depending on the color scale.As a result of the analysis of the proposed development, the compression coefficients are shown, which show that the size of the compressed image can be smaller than the original image by 50–70 %. The decoding is performed using known spline function coefficients. The result is compared with the original file. The difference between the intensity of the points of the source and decoded images determines the quality of the restoration.An algorithm is obtained that allows one to specify the accuracy of the reconstructed image. This result depends on the weighting coefficients of the spline function, which affect the accuracy of the construction of the approximating polynomial. A feature of the proposed approach is the ability of the user to specify the accuracy and quality of the image after decoding. This is achieved due to the fact that points close in intensity value are restored with a small error.In this paper, let's propose an approach involving the sequential extraction of blocks of points of equal intensity. For the selected blocks, an approximating polynomial is constructed based on the spline function, and the coefficients of the polynomial are transferred to a file containing information for image reconstruction. So it is possible to obtain large compression ratios by building a polynomial for blocks containing points that are close in intensity.

Analytical calculation of the lower bound on timing resolution for PET scintillation detectors comprising high-aspect-ratio crystal elements

Excellent timing resolution is required to enhance the signal-to-noise ratio (SNR) gain available from the incorporation of time-of-flight (ToF) information in image reconstruction for positron emission tomography (PET). As the detector’s timing resolution improves, so does SNR, reconstructed image quality, and accuracy. This directly impacts the challenging detection and quantification tasks in the clinic. The recognition of these benefits has spurred efforts within the molecular imaging community to determine to what extent the timing resolution of scintillation detectors can be improved and develop near-term solutions for advancing ToF-PET. Presented in this work, is a method for calculating the Cramér–Rao lower bound (CRLB) on timing resolution for scintillation detectors with long crystal elements, where the influence of the variation in optical path length of scintillation light on achievable timing resolution is non-negligible. The presented formalism incorporates an accurate, analytical probability density function (PDF) of optical transit time within the crystal to obtain a purely mathematical expression of the CRLB with high-aspect-ratio (HAR) scintillation detectors. This approach enables the statistical limit on timing resolution performance to be analytically expressed for clinically-relevant PET scintillation detectors without requiring Monte Carlo simulation-generated photon transport time distributions. The analytically calculated optical transport PDF was compared with detailed light transport simulations, and excellent agreement was found between the two. The coincidence timing resolution (CTR) between two mm LYSO:Ce crystals coupled to analogue SiPMs was experimentally measured to be ps FWHM, approaching the analytically calculated lower bound within 6.5%.

Design Optimization of a Time-Of-Flight, Breast PET Scanner

A dedicated breast positron emission tomography (PET) scanner with limited angle geometry can provide flexibility in detector placement around the patient as well as the ability to combine it with other imaging modalities. A primary challenge of a stationary limited angle scanner is the reduced image quality due to artifacts present in the reconstructed image leading to a loss in quantitative information. Previously it has been shown that using time-of-flight (TOF) information in image reconstruction can help reduce these image artifacts arising due to missing angular projections. Our goal in this work is to optimize the TOF, breast scanner design by performing studies for estimating image uniformity and lesion activity uptake as a function of system timing resolution, scanner angular coverage and shape. Our results show that (i) 1.5 × 1.5 × 15 mm3 lutetium oxy-orthosilicate (LSO) crystals provide a high spatial resolution and system sensitivity relative to clinical scanners, (ii) 2/3 angular coverage scanner design with TOF timing resolution less than 600 ps is appropriate for providing a tomographic image with fewer artifacts and good lesion uptake estimation relative to other partial ring designs studied in this work, (iii) a flat scanner design with 2/3 angular coverage is affected more by larger parallax error than a curved scanner geometry with the same angular coverage, but provides more uniform lesion contrast estimate over the imaging field-of-view (FOV), (iv) 2/3 angular coverage, flat, 300 ps TOF scanner design (for short, practical scan times of ≤ 5 mins per breast) provides similar precision of contrast recovery coefficient (CRC) values to a full curved, non-TOF scanner, and (v) employing depth-of-interaction (DOI) measuring detector and/or implementing resolution modeling (RM) in image reconstruction lead to improved and more uniform spatial resolution and lesion contrast over the whole FOV.

Model-based image reconstruction by means of a constrained least-squares solution

The problem of the optimal use of object model information in image reconstruction is addressed. A closed-form solution for the estimated object spectrum is derived with the Lagrange multiplier technique, which assumes a measured image, knowledge of the optical transfer function, statistical information about the measurement noise, and a model of the object. This reconstruction algorithm is iterative in nature because the optimal Lagrange multiplier is not generally known at the start of the problem. We derive the estimator, describe one technique for determining the optimal Lagrange multiplier, demonstrate a stopping criterion based on the mean-square error between a noise-free image and the photon-limited version of the image, and show representative results for both filled- and sparse-aperture imaging applications.

A mixed-annealing algorithm for edge preserving image reconstruction using a limited number of projections

The use of a priori information in image reconstruction has been shown effective in improving the quality of the solutions, especially when a small set of noisy data is available. Many authors have shown the advantages of considering the image discontinuities explicitly in order to reduce the excessively smooth appearance of the reconstructions produced by global smoothness constraints. The use of Markov Random Field models means that it is possible to describe the local behaviour of the image and, in particular, to enforce constraints on possible configurations of its discontinuities. In a Bayesian setting, additional knowledge in the form of Gibbs priors is combined with the observed data and the reconstructed image is computed as the mode of the resulting posterior. Due to the large dimensions and the non-convexity of the problem, algorithms based on simulated annealing techniques should be used; these algorithms have an enormous computational load and several techniques have been proposed in order to reduce the computational costs. A particular annealing schedule is proposed here that finds the solution iteratively by means of a sequence in which deterministic steps alternate with probabilistic ones. The algorithm is suitable for a parallel implementation in a hybrid architecture made up of a grid of digital processors interacting with a linear neural network which supports most of the computational costs. The proposed method has been applied to the problem of tomographic reconstruction from projections. It is shown to give good solutions even when a limited number of noisy projections are available.

An experimental evaluation of the effect of time-of-flight information in image reconstructions for the Scanditronix/PETT Electronics SP-3000 positron emission tomograph-preliminary results

The effects of detector energy thresholds and time-of-flight information on image quality were investigated for the SP-3000 positron emission tomograph. Phantom studies were performed with different energy discriminator settings. A software preprocessor allowed images to be reconstructed from list mode data with different time-of-flight windows. In general, the inclusion of time-of-flight information in image reconstruction improved SNR (signal to-noise-ratio) over conventional filtered back-projection techniques. The data indicate that setting the energy discriminator level higher than the 170 keV currently used in the machine could be beneficial. The results demonstrated the need for good quality assurance testing for time-of-flight offsets and gains, and they indicate that the current look-ahead coincidence system may need to be modified. >