Comparison of Image Quality and Spatial Resolution Between 18F, 68Ga and 64Cu Phantom Measurements Using a Digital Biograph Vision PET/CT

Background: The PET nuclide and reconstruction method can have a considerable influence on spatial resolution and image quality of PET/CT scans, which can, for example, influence the diagnosis in oncology. The individual impact of the positron energy of 18F, 68Ga and 64Cu on spatial resolution and image quality of PET/CT scans acquired using a clinical, digital scanner was compared. Furthermore, the impact of different reconstruction parameters on image quality and spatial resolution was evaluated for 18F-FDG PET/CT scans acquired with a scanner of the newest generation. Methods: PET/CT scans of a Jaszczak phantom and a NEMA PET body phantom, filled with 18F-FDG, 68Ga-HCl and 64Cu-HCl, respectively, were performed on a Siemens Biograph Vision. Images were assessed using spatial resolution and image quality (Recovery Coefficients (RC), coefficient of variation within the background, Contrast Recovery Coefficient (CRC), Contrast-Noise-Ratio (CNR), and relative count error in lung insert). In a subsequent analysis, the scan of the NEMA PET body phantom filled with 18F-FDG was reconstructed applying different parameters (with/without the application of Point Spread Function (PSF), Time of Flight (ToF) or post-filtering; matrix size). Spatial resolution and quantitative image quality were compared between reconstructions. Results: We found that image quality was comparable between 18F-FDG and 64Cu-HCl PET/CT measurements featuring similar maximal endpoint energy. In comparison, RC, CRC and CNR were worse in 68Ga-HCl data, despite similar count rates. Spatial resolution was up to 18 % worse in 68Ga-HCl compared to 18F-FDG images. Post-filtering of 18F-FDG acquisitions changed image quality the most and reduced spatial resolution by 52 % if a Gaussian filter with 5 mm FWHM was applied. ToF measurements especially improved the recovery of the smallest lesion (RCmean = 1.07 compared to 0.65 without ToF) and improved spatial resolution by 29 %.Conclusions: The positron energy of PET nuclides influences spatial resolution and image quality of digital PET/CT scans. Image quality of 68Ga-HCl PET/CT images was worse compared to 18F-FDG and 64Cu-HCl, respectively, despite similar count rates. Reconstruction parameters have a high impact on image quality and spatial resolution and should be considered when comparing images of different scanners or centers.

Computed Microtomography (Micro-CT) in the Anatomical Study and Identification of Solenogastres (Mollusca)

Solenogastres are vermiform marine molluscs characterised by an aculiferous mantle, a longitudinal ventral pedal groove and a terminal or subterminal pallial cavity. Their classification is based in part on the type of mantle sclerites, but identification to even the family level generally requires the study of internal anatomical characters. Taxonomically important internal characters include those related to radular structure, the type of ventrolateral glandular organs of the pharynx and the reproductive system, among others. In order to study their internal anatomical organisation, according to the classical reconstruction method, serial histological sections of specimens are made, from which the 2D internal anatomy of the specimen can be reconstructed manually. However, this is a time-consuming technique that results in destruction of the specimen. Computed microtomography or micro-CT is a non-destructive technique based on the measurement of the attenuation of X-rays as they pass through a specimen. Micro-CT is faster than histology for studying internal anatomy and it is non-destructive, meaning that specimens may be used for e.g., DNA extraction or retained as intact vouchers. In this paper, the utility of micro-CT for studying taxonomically important internal anatomical structures was assessed. Results of the 3D anatomical study of the soft parts of four specimens of three species using micro-CT are presented: Proneomenia sluiteriHubrecht, 1880, Dorymenia menchuescribanaeGarcía-Álvarez et al., 2000 and Anamenia gorgonophilaKowalevsky, 1880. Micro-CT enabled detailed study of most taxonomically important anatomical characters, precise measurements of structures, and observation of the relative position of organs from a variety of angles. However, it was not possible to observe the radula and some details of the ventral foregut organs could not be discerned. Despite these limitations, results of this study highlight micro-CT as a valuable tool to compliment histology in the study of solenogaster anatomy and in non-destructively identifying animals to the family and even genus-level.

Hyperspectral Acquisition Technology Based on Compressed Sampling in Spatial Domain

In the hyperspectral imaging device, the sensor detects the reflection or radiation intensity of the target at hundreds of different wavelengths, thus forming a spectral image composed of hundreds of continuous bands. The traditional processing method of sampling first and then compressing not only cannot fundamentally solve the problem of huge amount of data, but also causes waste of resources. To solve this problem, a spectral image reconstruction method based on compressed sampling in spatial domain and transform coding in spectral domain is designed by using the sparsity of single-band two-dimensional image and the spectral redundancy of spatial coded data. Based on Bayesian theory, a compressed sensing measurement matrix of adaptive projection is proposed. Combining these two algorithms, an adaptive Grouplet-FBCS algorithm is constructed to reconstruct the image using smooth projection Landweber. Experimental results show that, compared with existing image block compression sensing algorithms, this algorithm can significantly improve the quality of image signal reconstruction.

En bloc resection and vascularized ulnar pedicle graft reconstruction with plate fixation for giant cell tumour of the distal radius

We retrospectively reviewed the medical records of ten patients (five men and five women) who were treated in our unit for Campanacci Grade III giant cell tumour of the distal radius between July 2017 and December 2019. Following en bloc resection of a giant cell tumour of the distal radius, the wrist was reconstructed by transposing a vascularized pedicle graft from the ipsilateral ulnar shaft. The graft was fixed to the radial shaft and proximal carpal row with plates. At a mean follow-up of 23.5 months (range 18 to 31), bony union was achieved in all cases and there were no tumour recurrences. All patients had a good range of pronation and supination, but flexion and extension of the wrist was limited. DASH scores ranged from 5 to 11. This reconstruction method is a safe and effective procedure that provides good aesthetic outcomes, removes the need for microvascular techniques and reduces donor site morbidity. Level of evidence: IV

Time-adaptive Unsupervised Auditory Attention Decoding Using EEG-based Stimulus Reconstruction

The goal of auditory attention decoding (AAD) is to determine to which speaker out of multiple competing speakers a listener is attending based on the brain signals recorded via, e.g., electroencephalography (EEG). AAD algorithms are a fundamental building block of so-called neuro-steered hearing devices that would allow identifying the speaker that should be amplified based on the brain activity. A common approach is to train a subject-specific decoder that reconstructs the amplitude envelope of the attended speech signal. However, training this decoder requires a dedicated 'ground-truth' EEG recording of the subject under test, during which the attended speaker is known. Furthermore, this decoder remains fixed during operation and can thus not adapt to changing conditions and situations. Therefore, we propose an online time-adaptive unsupervised stimulus reconstruction method that continuously and automatically adapts over time when new EEG and audio data are streaming in. The adaptive decoder does not require ground-truth attention labels obtained from a training session with the end-user, and instead can be initialized with a generic subject-independent decoder or even completely random values. We propose two different implementations: a sliding window and recursive implementation, which we extensively validate based on multiple performance metrics on three independent datasets. We show that the proposed time-adaptive unsupervised decoder outperforms a time-invariant supervised decoder, representing an important step towards practically applicable AAD algorithms for neuro-steered hearing devices.

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting illposed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the outperformance of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with quadratic regularization on the CAUG, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.

Dynamic PET Imaging Using Dual Texture Features

Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results.Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM.Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions.Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.