A Positron Emission Tomograph based on LSO-APD modules with a sampling ADC read-out system for a students’ advanced laboratory course

The total synthesis of racemic Rosiglitazone, also known as the anti-diabetes drug Avandia, in six steps is the basis for an advanced undergraduate organic chemistry laboratory course. Several techniques and concepts learned in an introductory organic chemistry laboratory course are reinforced, and include TLC analysis, recrystallization, and reaction work-up. Additional techniques introduced into the organic chemistry laboratory course include phase-transfer catalysis, flash column chromatography, pressurized hydrogenation reactions, and multi-step total synthesis. Students are challenged at several levels, but mostly by an expectation of independent, in-depth spectral analysis of each product and providing bi-weekly experimental reports with journal-style spectral analysis. An advanced undergraduate organic laboratory course centered on the total synthesis of a medicinally important compound provides an overall focus for the semester, as well as greater interest and motivation for students.

The aim of the present study was to propose a comprehensive method for positron emission tomography (PET) scanners image quality assessment, by simulation of a thin layer chromatography (TLC) flood source with a previously validated Monte Carlo model. We used the GATE Monte Carlo package (GEANT4 application for tomographic emission) and the reconstructed images were obtained using the software for tomographic image reconstruction (STIR), with cluster computing. The PET scanner used in this simulation study was the General Electric Discovery-ST (USA). The plane source that was used for the image quality assessment was a TLC plate, consisting of an aluminum (Al) foil, coated with a thin layer of silica and immersed in fluorodeoxyglucose (18F-FDG) bath solution (1 MBq). The influence of different scintillating crystals on PET scanner's image quality, in terms of the modulation transfer function (MTF), the normalized noise power spectrum (NNPS) and the detective quantum efficiency (DQE), were also investigated. Modulation transfer function was estimated from transverse slices of the plane source, whereas the NNPS from the corresponding coronal slices. Images were reconstructed by the commonly used 2D filtered back projection (FBP2D), the Kinahan and Rogers FPB3DRP and the maximum likelihood estimation (MLE)-OSMAPOSL algorithms. Images obtained using the OSMAPOSL algorithm were assessed by using 15 subsets and 3 iterations. The PET scanner configuration, equipped with LuAP crystals, exhibited the optimum MTF values in both 2D and 3D FBP image reconstruction, whereas the corresponding configuration with BGO crystals exhibited the optimum MTF values after the iterative algorithm. The scanner equipped with the BGO crystals was also found to exhibit overall the lowest noise levels and the highest DQE values after algorithms. These finding indicate that the GE Discovery ST PET scanner exhibits the optimum image quality parameters, in terms of MTF, NNPS and DQE, with BGO scintillating crystals. Our new method showed that the imaging performance of PET scanners can be fully characterized and further improved by investigation of the imaging chain components through Monte Carlo methods. To this aim, a TLC based plane source was used during the simulation, in order to assess the impact of the scintillating crystal material on PET image quality, with the application of a previously validated Monte Carlo model. The aforementioned plane source can be also useful for the further development of PET and SPET scanners through GATE simulations, for clinical applications.

List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible solution to enhance the quality of PET image reconstruction. However, the application of deep learning techniques to list-mode PET image reconstruction has not been progressed because list data is a sequence of bit codes and unsuitable for processing by convolutional neural networks (CNN). In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior (DIP) which is the first trial to integrate list-mode PET image reconstruction and CNN. The proposed list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates the regularized list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP) using an alternating direction method of multipliers. We evaluated LM-DIPRecon using both simulation and clinical data, and it achieved sharper images and better tradeoff curves between contrast and noise than the LM-DRAMA, MR-DIP and sinogram-based DIPRecon methods. These results indicated that the LM-DIPRecon is useful for quantitative PET imaging with limited events while keeping accurate raw data information. In addition, as list data has finer temporal information than dynamic sinograms, list-mode deep image prior reconstruction is expected to be useful for 4D PET imaging and motion correction.

Despite its success in many biomedical applications, Positron Emission Tomography (PET) has the drawback of typically having lower spatial resolution and higher noise respect to other medical imaging techniques. The best achievable spatial resolution in PET scanners is limited by factors such as the positron range, non-collinearity and the size of the detector crystals. In this work, we present a novel method that uses series of image reconstructions (super-iterations) to go beyond the expected resolution-noise limits for a given PET acquisition. The image quality improvement is achieved using the projections of the previous image reconstruction to redistribute the measured counts of each line-of-response (LOR) into several subLORs, from which a new activity distribution with better quality is reconstructed. The method was evaluated with data from the preclinical scanner 4R-SuperArgus PET/CT, using the NEMA NU4-2008 image quality phantom, a cold Derenzo phantom, and an in-vivo FDG cardiac study on a rat. Resolution and recovery coefficient (RC) improvement of ~10% was achieved while keeping the same noise level. Qualitative results from the in-vivo study also confirm this improvement in image quality. The proposed method is able to achieve significantly better images at the expense of a modest increase of the computational time, and it could be also applied to other modalities, such as SPECT and Compton Cameras.

An advanced laboratory course for physics seniors is described that uses open-ended projects organized in such a way that all of the students work on each project in turn. The students change every two or three weeks from one project to another, each time being confronted with an ongoing experiment to which they are asked to make their contribution. The laboratory notebooks contain the cumulative total of a group effort. Individual experiments can be carried to considerable depth, but at the same time the individual students are not confined to the limitations of working on just one project.

Small-animal positron emission tomography as a tool for neuropharmacology

Positron Emission Tomography (PET) can measure dynamics of radioactive material ligand and is widely used for observing e.g. metabolism or the spatial densities of neuroreceptors in the brain. It is still an important research topic to improve the signal-to-noise ratio (SNR) of PET images reconstructed from measured sinograms. In this paper, we propose a four dimensional PET image reconstruction method, which applies a constrained non-negative matrix factorization (NMF) technique for the improvement of the SNR. According to the compartment model for pharmacokinetics analysis, a matrix representation of dynamical PET images can be decomposed into two matrices, which are non-negative and low-rank. Experimental results demonstrate that our proposed method improves the SNR compared with conventional methods.

A prototype brain positron emission tomography (PET) scanner using semiconductor detectors and depth of inleraclion (DOI) information has been developed to achieve high spatial resolulion and reduced scalier fraction. At the first step of the development, we created a two-dimensional prololype PET scanner composed of a single-slice full-ring detector unit to confirm the feasibility of the basic technologies that are necessary lo realize a semiconductor PET scanner. Through phantom and small-animal studies, the feasibility of the semiconductor PET was confirmed and the results showed that the semiconductor PET could produce quantitative imaging with high spatial resolution. Based on these achievements, a prototype brain PET scanner was developed to demonstrate the high spatial resolution and quantitative imaging capability required in human imaging.

A breast dedicated positron emission tomography (PET) scanner has been developed based on monolithic LYSO crystals coupled to position sensitive photomultiplier tubes (PSPMTs). In this study, we describe the design of the PET system and report on its performance evaluation. MAMMI is a breast PET scanner based on monolithic LYSO crystals. It consists of 12 compact modules with a transaxial field of view (FOV) of 170 mm in diameter and 40 mm axial FOV that translates to cover up to 170 mm. The patient lies down in a prone position that facilitates maximum breast elongation. Quantitative performance analysis of the calculated method for the attenuation correction specifically developed for MAMMI, and based on PET image segmentation, has also been conducted in this evaluation. In order to fully determine the MAMMI prototype's performance, we have adapted the measurements suggested for National Electrical Manufacturers Association (NEMA) NU 2-2007 and NU 4-2008 protocol tests, as they are defined for whole-body and small animal PET scanners, respectively. Spatial resolutions of 1.6, 1.8, and 1.9 mm were measured in the axial, radial, and tangential directions, respectively. A scatter fraction of 20.8% was obtained and the maximum NEC was determined to be 25 kcps at 44 MBq. The average sensitivity of the system was observed to be 1% for an energy window of (250 keV-750 keV) and a maximum absolute sensitivity of 1.8% was measured at the FOV center. The overall performance of the MAMMI reported on this evaluation quantifies its ability to produce high quality PET images. Spatial resolution values below 3 mm were measured in most of the FOV. Only the radial component of spatial resolution exceeds the 3 mm at radial positions larger than 60 mm. This study emphasizes the need for standardized testing methodologies for dedicated breast PET systems similar to NEMA standards for whole-body and small animal PET scanners.

This paper describes ongoing developments to an advanced laboratory course at Kettering University, which is targeted to students in engineering and engineering physics and emphasizes theoretical, computational, and experimental components in the context of airborne acoustics and modal testing [cf. D. A. Russell and D. O. Ludwigsen, J. Acoust. Soc. Am. 131, 2515-2524 (2012)]. These developments have included a transition to electronic laboratory notebooks and cloud-based computing resources, incorporation of updated hardware and software, and creation and testing of a multiple-choice assessment instrument for the course. When Kettering University suddenly shifted to exclusively remote teaching in March 2020 due to the COVID-19 pandemic, many of these changes proved to be essential for enabling rapid adaptation to a situation in which a laboratory was not available for the course. Laboratory activities were rewritten by crowdsourcing archived data, videos were incorporated to illustrate dynamic phenomena, and computer simulations were used to retain student interactivity. The comparison of multiple measures, including the assessment instrument, team-based grades on project papers, and individual grades on final exams, indicates that most students were successful at learning the course material and adapting to work on team-based projects in the midst of challenging remote learning conditions.

In this experiment, undergraduate students of an advanced laboratory course are introduced to dynamic nuclear magnetic resonance spectrocopy (NMR). The students used pulsed-field gradient methods (PFG-NMR) to measure the diffusion coefficient (D) of components of micelle solutions containing solubilizates (alcohols) with varying alkyl chain length. From the data obtained, the partition coefficient (P) of the alcohols between the aqueous and micelle phases was calculated and the effect of alkyl chain length on P is elucidated. Students used the diffusion coefficients of the micelles to calculate radii and compared the PFG-NMR results with those obtained from dynamic light scattering (DLS) experiments.

The physical performances of two current state-of-the-art scanners dedicated to functional imaging of the brain, one a single-photon emission tomography (SPET) scanner and the other a positron emission tomography (PET) scanner, have been compared under identical conditions. The aim of the study was to compare the capabilities of the devices under conditions resembling the routine clinical environment, as well as to consider other issues such as radiation burden for some common investigations. Both systems have slightly less than 11-cm axial fields of view. The PET system can be operated in a septa-less (3D) mode as well as conventionally with septa (2D). The spatial resolution of both devices was less than 8 mm in all dimensions in scattering media. On average, the PET scanner's resolution was approximately 10%-15% better than the SPET system. Energy resolution on the SPET system was superior due the scintillator used [NaI(Tl)]. Sensitivity in air with a line source on the PET system was found to be approximately 150 times greater in 3D and approximately 25 times greater in 2D than with the SPET system. A normal subject was studied on each system in an attempt to obtain the highest quality data possible for a subjective comparison. It is clear that, while PET retains the advantages of more desirable radiopharmaceuticals and higher sensitivity, the quality obtainable from SPET devices has improved markedly. SPET may prove as useful for many clinical investigations.

An all-digital positron emission tomography (PET) scanner for imaging small animals has been developed at Huazhong University of Science and Technology (HUST). The implementation of block detector with position sensitive photomultiplier (PSPMT) and multi-voltage threshold (MVT) technology is presented in this paper. The detector comprises 12 basic detector modules (BDMs), which can be used to construct various types of PET scanner architecture due to the modularized design. Each BDM is composed of a detector unit (DU) and a digital processing unit (DPU). The scintillation pulses are obtained in DU, from which the time, energy and position can be derived and processed in DPU by using digital signal processing (DSP) algorithm. Then coincidence data and list-mode data can be obtained in soft singles processing unit (SSPU). We arrange two BDMs as a dual-head PET scanner to evaluate the system. Our results show that the average energy resolution is ∼14%@511keV full-width-at-half-maximum (FWHM) and the coincidence time determination is ∼1.38 ns FWHM. Moreover, the reconstructed image of a homemade micro-Derenzo phantom shows that the rods with a diameter of 1.6mm can be clearly distinguished. The results are promising and demonstrate the potentials of the alldigital PET system.

The promise of new PET image reconstruction

This work explores the feasibility and impact of including both the motion correction and the image registration transformation parameters from positron emission tomography (PET) image space to magnetic resonance (MR), or stereotaxic, image space within the system matrix of PET image reconstruction. This approach is motivated by the fields of neuroscience and psychiatry, where PET is used to investigate differences in activation patterns between different groups of participants, requiring all images to be registered to a common spatial atlas. Currently, image registration is performed after image reconstruction which introduces interpolation effects into the final image. Furthermore, motion correction (also requiring registration) introduces a further level of interpolation, and the overall result of these operations can lead to resolution degradation and possibly artifacts. It is important to note that performing such operations on a post-reconstruction basis means, strictly speaking, that the final images are not ones which maximize the desired objective function (e.g. maximum likelihood (ML), or maximum a posteriori reconstruction (MAP)). To correctly seek parameter estimates in the desired spatial atlas which are in accordance with the chosen reconstruction objective function, it is necessary to include the transformation parameters for both motion correction and registration within the system modeling stage of image reconstruction. Such an approach not only respects the statistically chosen objective function (e.g. ML or MAP), but furthermore should serve to reduce the interpolation effects. To evaluate the proposed method, this work investigates registration (including motion correction) using 2D and 3D simulations based on the high resolution research tomograph (HRRT) PET scanner geometry, with and without resolution modeling, using the ML expectation maximization (MLEM) reconstruction algorithm. The quality of reconstruction was assessed using bias-variance and root mean squared error analyses, comparing the proposed method to conventional post-reconstruction registration methods. An overall reduction in bias (for a cold region: from 41% down to 31% (2D) and 97% down to 65% (3D), and for a hot region: from 11% down to 8% (2D) and from 16% down to 14% (3D)) and in root mean squared error analyses (for a cold region: from 43% to 37% (2D) and from 97% to 65% (3D), and for a hot region: from 11% to 9% (2D) and from 16% down to 14% (3D)) in reconstructed regional mean activities (full regions of interest; all with statistical significance: p < 5 × 10−10) is found when including the motion correction and registration in the system matrix of the MLEM reconstruction, with resolution modeling. However, this improvement in performance comes with an extra computational cost of about 40 min. In this context, this work constitutes an important step toward the goal of estimating parameters of interest directly from the raw Poisson-distributed PET data, and hence toward the complete elimination of post-processing steps.