Analytic Image Reconstruction Research Articles

A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation.

Sparse-view computed tomography (CT) has been attracting attention for its reduced radiation dose and scanning time. However, analytical image reconstruction methods suffer from streak artifacts due to insufficient projection views. Recently, various deep learning-based methods have been developed to solve this ill-posed inverse problem. Despite their promising results, they are easily overfitted to the training data, showing limited generalizability to unseen systems and patients. In this work, we propose a novel streak artifact reduction algorithm that provides a system- and patient-specificsolution. Motivated by the fact that streak artifacts are deterministic errors, we regenerate the same artifacts from a prior CT image under the same system geometry. This prior image need not be perfect but should contain patient-specific information and be consistent with full-view projection data for accurate regeneration of the artifacts. To this end, we use a coordinate-based neural representation that often causes image blur but can greatly suppress the streak artifacts while having multiview consistency. By employing techniques in neural radiance fields originally proposed for scene representations, the neural representation is optimized to the measured sparse-view projection data via self-supervised learning. Then, we subtract the regenerated artifacts from the analytically reconstructed original image to obtain the final correctedimage. To validate the proposed method, we used simulated data of extended cardiac-torso phantoms and the 2016 NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge and experimental data of physical pediatric and head phantoms. The performance of the proposed method was compared with a total variation-based iterative reconstruction method, naive application of the neural representation, and a convolutional neural network-based method. In visual inspection, it was observed that the small anatomical features were best preserved by the proposed method. The proposed method also achieved the best scores in the visual information fidelity, modulation transfer function, and lung nodulesegmentation. The results on both simulated and experimental data suggest that the proposed method can effectively reduce the streak artifacts while preserving small anatomical structures that are easily blurred or replaced with misleading features by the existing methods. Since the proposed method does not require any additional training datasets, it would be useful in clinical practice where the large datasets cannot becollected.

Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram

Over the last decades, there has been an increasing interest in dedicated preclinical imaging modalities for research in biomedicine. Especially in the case of positron emission tomography (PET), reconstructed images provide useful information of the morphology and function of an internal organ. PET data, stored as sinograms, involve the Radon transform of the image under investigation. The analytical approach to PET image reconstruction incorporates the derivative of the Hilbert transform of the sinogram. In this direction, in the present work we present a novel numerical algorithm for the inversion of the Radon transform based on Chebyshev polynomials of the first kind. By employing these polynomials, the computation of the derivative of the Hilbert transform of the sinogram is significantly simplified. Extending the mathematical setting of previous research based on Chebyshev polynomials, we are able to efficiently apply our new Chebyshev inversion scheme for the case of analytic preclinical PET image reconstruction. We evaluated our reconstruction algorithm on projection data from a small-animal image quality (IQ) simulated phantom study, in accordance with the NEMA NU 4-2008 standards protocol. In particular, we quantified our reconstructions via the image quality metrics of percentage standard deviation, recovery coefficient, and spill-over ratio. The projection data employed were acquired for three different Poisson noise levels: 100% (NL1), 50% (NL2), and 20% (NL3) of the total counts, respectively. In the uniform region of the IQ phantom, Chebyshev reconstructions were consistently improved over filtered backprojection (FBP), in terms of percentage standard deviation (up to 29% lower, depending on the noise level). For all rods, we measured the contrast-to-noise-ratio, indicating an improvement of up to 68% depending on the noise level. In order to compare our reconstruction method with FBP, at equal noise levels, plots of recovery coefficient and spill-over ratio as functions of the percentage standard deviation were generated, after smoothing the NL3 reconstructions with three different Gaussian filters. When post-smoothing was applied, Chebyshev demonstrated recovery coefficient values up to 14% and 42% higher, for rods 1–3 mm and 4–5 mm, respectively, compared to FBP, depending on the smoothing sigma values. Our results indicate that our Chebyshev-based analytic reconstruction method may provide PET reconstructions that are comparable to FBP, thus yielding a good alternative to standard analytic preclinical PET reconstruction methods.

A Realistic Breast Phantom Proposal for 3D Image Reconstruction in Digital Breast Tomosynthesis.

Objectives: Iterative (eg, simultaneous algebraic reconstruction technique [SART]) and analytical (eg, filtered back projection [FBP]) image reconstruction techniques have been suggested to provide adequate three-dimensional (3D) images of the breast for capturing microcalcifications in digital breast tomosynthesis (DBT). To decide on the reconstruction method in clinical DBT, it must first be tested in a simulation resembling the real clinical environment. The purpose of this study is to introduce a 3D realistic breast phantom for determining the reconstruction method in clinical applications. Methods: We designed a 3D realistic breast phantom with varying dimensions (643-5123) mimicking some structures of a real breast such as milk ducts, lobules, and ribs using TomoPhantom software. We generated microcalcifications, which mimic cancerous cells, with a separate MATLAB code and embedded them into the phantom for testing and benchmark studies in DBT. To validate the characterization of the phantom, we tested the distinguishability of microcalcifications by performing 3D image reconstruction methods (SART and FBP) using Laboratory of Computer Vision (LAVI) open-source reconstruction toolbox. Results: The creation times of the proposed realistic breast phantom were seconds of 2.5916, 8.4626, 57.6858, and 472.1734 for 643, 1283, 2563, and 5123, respectively. We presented reconstructed images and quantitative results of the phantom for SART (1-2-4-8 iterations) and FBP, with 11 to 23 projections. We determined qualitatively and quantitatively that SART (2-4 iter.) yields better results than FBP. For example, for 23 projections, the contrast-to-noise ratio (CNR) values of SART (2 iter.) and FBP were 2.871 and 0.497, respectively. Conclusions: We created a computationally efficient realistic breast phantom that is eligible for reconstruction and includes anatomical structures and microcalcifications, successfully. By proposing this breast phantom, we provided the opportunity to test which reconstruction methods can be used in clinical applications vary according to various parameters such as the No. of iterations and projections in DBT.

Tensor Gradient L₀-Norm Minimization-Based Low-Dose CT and Its Application to COVID-19

Methods to recover high-quality computed tomography (CT) images in low-dose cases will be of great benefit. To reach this goal, sparse-data subsampling is one of the common strategies to reduce radiation dose, which is attracting interest among the researchers in the CT community. Since analytic image reconstruction algorithms may lead to severe image artifacts, the iterative algorithms have been developed for reconstructing images from sparsely sampled projection data. In this study, we first develop a tensor gradient L0-norm minimization (TGLM) for low-dose CT imaging. Then, the TGLM model is optimized by using the split-Bregman method. The Coronavirus Disease 2019 (COVID-19) has been sweeping the globe, and CT imaging has been deployed for detection and assessing the severity of the disease. Finally, we first apply our proposed TGLM method for COVID-19 to achieve low-dose scan by incorporating the 3-D spatial information. Two COVID-19 patients (64 years old female and 56 years old man) were scanned by the n}{}{mu }nCT 528 system, and the acquired projections were retrieved to validate and evaluate the performance of the TGLM.

AirNet: Fused Analytical and Iterative Image Reconstruction Method With Deep Learning Regularization for High-Quality Sparse-Data On-Board CBCT

AirNet: Fused Analytical and Iterative Image Reconstruction Method With Deep Learning Regularization for High-Quality Sparse-Data On-Board CBCT

Poisson-noise weighted filter for time-of-flight positron emission tomography

Image reconstruction for list-mode time-of-flight (TOF) positron emission tomography (PET) can be achieved by analytic algorithms. The backprojection filtering (BPF) algorithm is an efficient algorithm for this task. The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter, which does not model the Poisson noise properly. This study proposes a nonstationary filter for Poisson noise control. The filter is implemented in the spatial domain in a form similar to convolution.

Analytic time-of-flight positron emission tomography reconstruction: two-dimensional case

In a positron emission tomography (PET) scanner, the time-of-flight (TOF) information gives us rough event position along the line-of-response (LOR). Using the TOF information for PET image reconstruction is able to reduce image noise. The state-of-the-art TOF PET image reconstruction uses iterative algorithms. Analytical image reconstruction algorithm exits for TOF PET which emulates the iterative Landweber algorithm. This paper introduces such an algorithm, focusing on two-dimensional (2D) reconstruction. The proposed algorithm is in the form of backprojection filtering, in which the backprojection is performed first, and then a 2D filter is applied to the backprojected image. For the list-mode data, the backprojection is carried out in the event-by-event fashion, and a profile function may be used along the projection LOR. The 2D filter depends on the TOF timing resolution as well as the backprojection profile function. In order to emulate the iterative algorithm effects, a Fourier-domain window function is suggested. This window function has a parameter, k, which corresponds to the iteration number in an iterative algorithm.

Ultrasound computed tomography for material inspection: Principles, design and applications

The non-destructive testing based on an ultrasound computed tomography (UCT) technique has attracted the attention of the industrial and medical area due to its excellent capabilities in several applications. The utilization of UCT has provided a prodigious benefit for analyzing and monitoring the condition of material. This paper presents a review of ultrasound computed tomography for its applications in construction, process control, medical, temperature monitoring, agriculture and food industry. The main elements of UCT including ultrasonic sensing modes, types of ultrasonic waves and the design of measurement system are discussed. This paper also presents the mathematical modelling for the ultrasonic parameters employed during the measurement process, such as attenuation coefficient, acoustic impedance, wave velocity, frequency value and wave amplitude. The concept of analytical image reconstruction algorithm and the optimization technique for reconstructing the material image are also expounded in this paper. Selected articles in previous researches are discussed and elaborated. Trends and future outlook for the UCT technique are also presented. This paper fulfills the need of review paper for the principle and applications of ultrasound computed tomography for material inspection. It is envisaged that the information gathered in this paper will be a valuable source of information for researchers working in this topic.

Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this work, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.

A weighted rebinned backprojection-filtration algorithm from partially beam-blocked data for a single-scan cone-beam CT with hybrid type scatter correction.

Scatter contamination constitutes a dominant source of degradation of image quality in cone-beam computed tomography (CBCT). We have recently developed an analytic image reconstruction method with a scatter correction capability from the partially blocked cone-beam data out of a single scan. Despite its easy implementation and its computational efficiency, the developed method may result in additional image artifacts for a large cone angle geometry due to data inconsistency. To improve the image quality at a large cone angle, we propose a weighted rebinned backprojection-filtration (wrBPF) algorithm in conjunction with a hybrid type scatter correction approach. The proposed method uses a beam-blocker array that provides partial data for scatter correction and image reconstruction and that only blocks the beam within a limited cone angle. Thisdesign allows a chance to keep the image quality at larger cone angles by use of data redundancy since the projection data corresponding to larger cone angles are not blocked. However, the scatter correction would not be straightforward. In order to correct for the scatter in the projections at larger cone angles, we propose a novel scatter correction method combining a measurement-based and a convolution-based method. We first estimated the scatter signal using a measurement-based method in the partially beam-blocked regions, and then optimized the fitting parameters of a convolution-kernel that can be used for scatter correction in the projections at larger cone angles. For image reconstruction, we developed a wrBPF with butterfly filtering. We have conducted an experimental study to validate the proposed algorithm for image reconstruction and scatter correction. The experimental results revealed that the developed reconstruction method makes full use of the benefits of partial beam-blocking for scatter correction and image reconstruction and at the same time enhances image quality at larger cone angles by use of an optimized convolution-based scatter correction. The proposed method that enjoys the advantages of both measurement-based and convolution-based methods for scatter correction has successfully demonstrated its capability of reconstructing accurate images out of a single scan in circular CBCT.

Polarized x-ray excitation for scatter reduction in x-ray fluorescence computed tomography.

X-ray fluorescence computer tomography (XFCT) is a new molecular imaging modality which uses x-ray excitation to stimulate the emission of fluorescent photons in high atomic number contrast agents. Scatter contamination is one of the main challenges in XFCT imaging which limits the molecular sensitivity. When polarized x rays are used, it is possible to reduce the scatter contamination significantly by placing detectors perpendicular to the polarization direction. This study quantifies scatter contamination for polarized and unpolarized x-ray excitation and determines the advantages of scatter reduction. The amount of scatter in preclinical XFCT is quantified in Monte Carlo simulations. The fluorescent x rays are emitted isotropically, while scattered x rays propagate in polarization direction. The magnitude of scatter contamination is studied in XFCT simulations of a mouse phantom. In this study, the contrast agent gold is examined as an example, but a scatter reduction from polarized excitation is also expected for other elements. The scatter reduction capability is examined for different polarization intensities with a monoenergetic x-ray excitation energy of 82keV. The study evaluates two different geometrical shapes of CZT detectors which are modeled with an energy resolution of 1keV FWHM at an x-ray energy of 80keV. Benefits of a detector placement perpendicular to the polarization direction are shown in iterative and analytic image reconstruction including scatter correction. The contrast to noise ratio (CNR) and the normalized mean square error (NMSE) are analyzed and compared for the reconstructed images. A substantial scatter reduction for common detector sizes was achieved for 100% and 80% linear polarization while lower polarization intensities provide a decreased scatter reduction. By placing the detector perpendicular to the polarization direction, a scatter reduction by factor up to 5.5 can be achieved for common detector sizes. The image reconstruction showed that for a scatter magnitude decrease by a factor of 2.4, the molecular sensitivity could almost be doubled. Scatter reduction lowers the amount of noise in the projection datasets and reconstructed images which enhance molecular sensitivity at equal dose. The results support the use of linear polarized x rays to reduce scatter in XFCT imaging.

Positron Range Visualization Using the Analytical and Iterative Image Reconstruction Methods From a Preclinical PET Scanner With LYSO Scintillators Coupled to SiPM Detectors: Comparison With Simulated Results

An evaluation of the image quality of the preclinical positron emission tomography scanner consisting of the SensL's SMT C-series silicon photomultiplier (SiPM) arrays coupled with double jaw layer lutetium-yttrium oxyorthosilicate (LYSO) crystals was achieved. The simulated data of a Jaszczak phantom filled with Na-22 and O-15 were compared with reconstructed data of the same phantom when it was filled with Na-22. Two different reconstruction methods were applied in order to visualize any positron range effects that occur within the phantom. The filtered back-projection (FBP) and ordered-subset expectation maximization (OSEM) algorithms provided useful information regarding the spatial resolution of the scanner, showing that these sensors can visually correct the structure of the phantom. The OSEM method appeared to be more reliable than FBP, even when a Compton correction was applied on the data.

Time-resolved C-arm cone beam CT angiography (TR-CBCTA) imaging from a single short-scan C-arm cone beam CT acquisition with intra-arterial contrast injection

Time-resolved C-arm cone-beam CT (CBCT) angiography (TR-CBCTA) images can be generated from a series of CBCT acquisitions that satisfy data sufficiency condition in analytical image reconstruction theory. In this work, a new technique was developed to generate TR-CBCTA images from a single short-scan CBCT data acquisition with contrast media injection. The reconstruction technique enabling this application is a previously developed image reconstruction technique, synchronized multi-artifact reduction with tomographic reconstruction (SMART-RECON). In this new application, the acquired short-scan CBCT projection data were sorted into a union of several sub-sectors of view angles and each sub-sector of view angles corresponds to an individual image volume to be reconstructed. The SMART-RECON method was then used to jointly reconstruct all of these individual image volumes under two constraints: (1) each individual image volume is maximally consistent with the measured cone-beam projection data within the corresponding view angle sector and (2) the nuclear norm of the image matrix is minimized. The difference between these reconstructed individual image volumes is used to generated the desired subtracted angiograms. To validate the technique, numerical simulation data generated from a fractal tree angiogram phantom were used to quantitatively study the accuracy of the proposed method and retrospective in vivo human subject studies were used to demonstrate the feasibility of generating TR-CBCTA in clinical practice.

Joint Statistical Iterative Material Image Reconstruction for Spectral Computed Tomography Using a Semi-Empirical Forward Model.

By acquiring tomographic measurements with several distinct photon energy spectra, spectral computed tomography (spectral CT) is able to provide additional material-specific information compared with conventional CT. This information enables the generation of material selective images, which have found various applications in medical imaging. However, material decomposition typically leads to noise amplification and a degradation of the signal-to-noise ratio. This is still a fundamental problem of spectral CT, especially for low-dose medical applications. Inspired by the success for low-dose conventional CT, several statistical iterative reconstruction algorithms for spectral CT have been developed. These algorithms typically rely on detailed knowledge about the spectrum and the detector response. Obtaining this knowledge is often difficult in practice, especially if photon counting detectors are used to acquire the energy specific information. In this paper, a new algorithm for joint statistical iterative material image reconstruction is presented. It relies on a semi-empirical forward model which is tuned by calibration measurements. This strategy allows to model spatially varying properties of the imaging system without requiring detailed prior knowledge of the system parameters. We employ an efficient optimization algorithm based on separable surrogate functions to accelerate convergence and reduce the reconstruction time. Numerical as well as real experiments show that our new algorithm leads to reduced statistical bias and improved image quality compared with projection-based material decomposition followed by analytical or iterative image reconstruction.

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.

Impact of nonstationary optical illumination on image reconstruction in optoacoustic tomography.

Optoacoustic tomography (OAT), also known as photoacoustic tomography, is a rapidly emerging hybrid imaging technique that possesses great potential for a wide range of biomedical imaging applications. In OAT, a laser is employed to illuminate the tissue of interest and acoustic signals are produced via the photoacoustic effect. From these data, an estimate of the distribution of the absorbed optical energy density within the tissue is reconstructed, referred to as the object function. This quantity is defined, in part, by the distribution of light fluence within the tissue that is established by the laser source. When performing three-dimensional imaging of large objects, such as a female human breast, it can be difficult to achieve a relatively uniform coverage of light fluence within the volume of interest when the position of the laser source is fixed. To circumvent this, researchers have proposed illumination schemes in which the relative position of the laser source and ultrasound probe is fixed, and both are rotated together to acquire a tomographic dataset. A problem with this rotating-illumination scheme is that the tomographic data are inconsistent; namely, the acoustic data recorded at each tomographic view angle (i.e., probe position) are produced by a distinct object function. In this work, the impact of this data inconsistency on image reconstruction accuracy is investigated systematically. This is accomplished by use of computer-simulation studies and application of mathematical results from the theory of microlocal analysis. These studies specify the set of image discontinuities that can be stably reconstructed with a nonstationary optical illumination setup. The study also includes a comparison of the ability of iterative and analytic image reconstruction methods to mitigate artifacts attributable to the data inconsistency.

Analytic image reconstruction from partial data for a single-scan cone-beam CT with scatter correction.

A beam-blocker composed of multiple strips is a useful gadget for scatter correction and/or for dose reduction in cone-beam CT (CBCT). However, the use of such a beam-blocker would yield cone-beam data that can be challenging for accurate image reconstruction from a single scan in the filtered-backprojection framework. The focus of the work was to develop an analytic image reconstruction method for CBCT that can be directly applied to partially blocked cone-beam data in conjunction with the scatter correction. The authors developed a rebinned backprojection-filteration (BPF) algorithm for reconstructing images from the partially blocked cone-beam data in a circular scan. The authors also proposed a beam-blocking geometry considering data redundancy such that an efficient scatter estimate can be acquired and sufficient data for BPF image reconstruction can be secured at the same time from a single scan without using any blocker motion. Additionally, scatter correction method and noise reduction scheme have been developed. The authors have performed both simulation and experimental studies to validate the rebinned BPF algorithm for image reconstruction from partially blocked cone-beam data. Quantitative evaluations of the reconstructed image quality were performed in the experimental studies. The simulation study revealed that the developed reconstruction algorithm successfully reconstructs the images from the partial cone-beam data. In the experimental study, the proposed method effectively corrected for the scatter in each projection and reconstructed scatter-corrected images from a single scan. Reduction of cupping artifacts and an enhancement of the image contrast have been demonstrated. The image contrast has increased by a factor of about 2, and the image accuracy in terms of root-mean-square-error with respect to the fan-beam CT image has increased by more than 30%. The authors have successfully demonstrated that the proposed scanning method and image reconstruction algorithm can effectively estimate the scatter in cone-beam projections and produce tomographic images of nearly scatter-free quality. The authors believe that the proposed method would provide a fast and efficient CBCT scanning option to various applications particularly including head-and-neck scan.

A pencil beam approach to proton computed tomography.

A new approach to proton computed tomography (pCT) is presented. In this approach, protons are not tracked one-by-one but a beam of particles is considered instead. The elements of the pCT reconstruction problem (residual energy and path) are redefined on the basis of this new approach. An analytical image reconstruction algorithm applicable to this scenario is also proposed. The pencil beam (PB) and its propagation in matter were modeled by making use of the generalization of the Fermi-Eyges theory to account for multiple Coulomb scattering (MCS). This model was integrated into the pCT reconstruction problem, allowing the definition of the mean beam path concept similar to the most likely path (MLP) used in the single-particle approach. A numerical validation of the model was performed. The algorithm of filtered backprojection along MLPs was adapted to the beam-by-beam approach. The acquisition of a perfect proton scan was simulated and the data were used to reconstruct images of the relative stopping power of the phantom with the single-proton and beam-by-beam approaches. The resulting images were compared in a qualitative way. The parameters of the modeled PB (mean and spread) were compared to Monte Carlo results in order to validate the model. For a water target, good agreement was found for the mean value of the distributions. As far as the spread is concerned, depth-dependent discrepancies as large as 2%-3% were found. For a heterogeneous phantom, discrepancies in the distribution spread ranged from 6% to 8%. The image reconstructed with the beam-by-beam approach showed a high level of noise compared to the one reconstructed with the classical approach. The PB approach to proton imaging may allow technical challenges imposed by the current proton-by-proton method to be overcome. In this framework, an analytical algorithm is proposed. Further work will involve a detailed study of the performances and limitations of this approach in terms of image quality. The paper shows how to account for the MCS in the reconstruction step with this new approach when an analytical reconstruction algorithm is used.

Update on time-of-flight PET imaging.

Time-of-flight (TOF) PET was initially introduced in the early days of PET. The TOF PET scanners developed in the 1980s had limited sensitivity and spatial resolution, were operated in 2-dimensional mode with septa, and used analytic image reconstruction methods. The current generation of TOF PET scanners has the highest sensitivity and spatial resolution ever achieved in commercial whole-body PET, is operated in fully-3-dimensional mode, and uses iterative reconstruction with full system modeling. Previously, it was shown that TOF provides a gain in image signal-to-noise ratio that is proportional to the square root of the object size divided by the system timing resolution. With oncologic studies being the primary application of PET, more recent work has shown that in modern TOF PET scanners there is an improved tradeoff between lesion contrast, image noise, and total imaging time, leading to a combination of improved lesion detectability, reduced scan time or injected dose, and more accurate and precise lesion uptake measurement. Because the benefit of TOF PET is also higher for heavier patients, clinical performance is more uniform over all patient sizes.

Image Reconstruction Methods for Ultrasonic Transmission Mode Tomography in Bubbly Flow Regime

Image reconstruction from projections plays an important role in monitoring flow regimes by ultrasonic transmission mode tomography (UTMT) system. Fast and more accurate methods are necessary in case of on-line process e.g. bubbly flow regimes. In this work, analytical image reconstruction methods such as linear back projection (LBP), filter back projection (FBP) and convolution back projection (CBP) in bubbly flow regime is investigated and found that CBP is superior to other methods. Furthermore, different filters were applied to CBP to investigate the image quality improvement. Among different types of filters for CBP method, Ram-lack outperforms the others for UTMT. The peak signal to noise ratio (PSNR) of reconstructed images in this particular experiment was improved using Ram-lack in noiseless data.