Volumetric additive manufacturing (VAM) is an emerging approach to photo polymerbased 3D printing that produces complex 3D structures in a single step, rather than from layer-by-layer assembly. [1] This paradigm holds promise because it overcomes many of the drawbacks of layerbased fabrication, such as long build times and rough surfaces. VAM also augurs a broadening of the materials available for photopolymer 3D printing, having fewer constraints on viscosity and reactivity compared to layerwise printing. Indeed, though VAM has been demonstrated with extremely soft hydrogels, [2,3] it has relied until now almost exclusively on acrylate-based chemistry. [4] This is natural, because the oxygen inhibition of acrylate polymerization provides the threshold behavior required for VAM. However, acrylate chemistry is in general limiting due to the brittle and glassy properties of the resulting materials. Accordingly, extensive efforts have been made to identify and target specific soft, elastic acrylate formulations. [5-9] Introducing alternative crosslinking chemistries to the VAM realm, as well as AM more broadly, is highly desirable as an alternative method to gain access to a wider range of mechanical, thermal, and optical performance. [10-14] Thiol-ene-based polymers are one class of materials that have drawn significant attention owing to their controllable, tunable mechanical properties. [15-17] This is generally attributed to more uniform molecular networks in thiol-ene materials, resulting from the step-growth mechanism of the polymerization reaction. [18,19] Thiol-ene materials have already shown promise for applications including use in adhesives, electronics, and as biomaterials. [20,21] This work expands the versatility of volumetric AM by introducing a new class of VAM-compatible thiol-ene resins. We demonstrate the formulation of thiol-ene resins with the nonlinear threshold-type kinetics required for VAM and show bulk-equivalent performance in the resulting 3D printed parts, confirming the advantage of the layerless whole-part process. In our volumetric AM system, a 3D distribution of light energy is delivered to the resin vat by superimposing exposures from multiple angles, a method termed computed axial litho graphy (CAL) (Figure 1a). [2] The exposures are a sequence of projections calculated from 3D CAD models using algorithms from computed Volumetric additive manufacturing (VAM) forms complete 3D objects in a single photocuring operation without layering defects, enabling 3D printed polymer parts with mechanical properties similar to their bulk material counterparts. This study presents the first report of VAM-printed thiol-ene resins. With well-ordered molecular networks, thiol-ene chemistry accesses polymer materials with a wide range of mechanical properties, moving VAM beyond the limitations of commonly used acrylate formulations. Since free-radical thiol-ene polymerization is not inhibited by oxygen, the nonlinear threshold response required in VAM is introduced by incorporating 2,2,6,6-tetrameth...

Tomographic breast imaging techniques can potentially improve detection and diagnosis of cancer in women with radiodense and/or fibrocystic breasts. We have developed a high-resolution positron emission mammography/tomography imaging and biopsy device (called PEM/PET) to detect and guide the biopsy of suspicious breast lesions. PET images are acquired to detect suspicious focal uptake of the radiotracer and guide biopsy of the area. Limited-angle PEM images could then be used to verify the biopsy needle position prior to tissue sampling. The PEM/PET scanner consists of two sets of rotating planar detector heads. Each detector consists of a 4 x 3 array of Hamamatsu H8500 flat panel position sensitive photomultipliers (PSPMTs) coupled to a 96 x 72 array of 2 x 2 x 15 mm(3) LYSO detector elements (pitch = 2.1 mm). Image reconstruction is performed with a three-dimensional, ordered set expectation maximization (OSEM) algorithm parallelized to run on a multi-processor computer system. The reconstructed field of view (FOV) is 15 x 15 x 15 cm(3). Initial phantom-based testing of the device is focusing upon its PET imaging capabilities. Specifically, spatial resolution and detection sensitivity were assessed. The results from these measurements yielded a spatial resolution at the center of the FOV of 2.01 +/- 0.09 mm (radial), 2.04 +/- 0.08 mm (tangential) and 1.84 +/- 0.07 mm (axial). At a radius of 7 cm from the center of the scanner, the results were 2.11 +/- 0.08 mm (radial), 2.16 +/- 0.07 mm (tangential) and 1.87 +/- 0.08 mm (axial). Maximum system detection sensitivity of the scanner is 488.9 kcps microCi(-1) ml(-1) (6.88%). These promising findings indicate that PEM/PET may be an effective system for the detection and diagnosis of breast cancer.

Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180°view of the object. However, obtaining a full-view is not always feasible, such as when scanning irregular objects that limit flexibility of scanner rotation. The resulting limited angle sinograms are known to produce highly artifact-laden reconstructions with existing techniques. In this paper, we propose to address this problem using CTNet -a system of 1D and 2D convolutional neural networks, that operates directly on a limited angle sinogram to predict the reconstruction. We use the x-ray transform on this prediction to obtain a "completed" sinogram, as if it came from a full 180°view. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation on a challenging real world dataset that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by CTNet. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction also preserves the 3D structure of objects better than existing solutions.

Glioblastoma multiforme (GBM) is a class of primary brain tumours characterized by their ability to rapidly proliferate and diffusely infiltrate surrounding brain tissue. The aggressive growth of GBM leads to the development of regions of low oxygenation (hypoxia), which can be clinically assessed through [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET) imaging. Building upon the success of our previous mathematical modelling efforts, we have expanded our model to include the tumour microenvironment, specifically incorporating hypoxia, necrosis and angiogenesis. A pharmacokinetic model for the FMISO-PET tracer is applied at each spatial location throughout the brain and an analytical simulator for the image acquisition and reconstruction methods is applied to the resultant tracer activity map. The combination of our anatomical model with one for FMISO tracer dynamics and PET image reconstruction is able to produce a patient-specific virtual PET image that reproduces the image characteristics of the clinical PET scan as well as shows no statistical difference in the distribution of hypoxia within the tumour. This work establishes proof of principle for a link between anatomical (magnetic resonance image [MRI]) and molecular (PET) imaging on a patient-specific basis as well as address otherwise untenable questions in molecular imaging, such as determining the effect on tracer activity from cellular density. Although further investigation is necessary to establish the predictive value of this technique, this unique tool provides a better dynamic understanding of the biological connection between anatomical changes seen on MRI and biochemical activity seen on PET of GBM in vivo.

We present a new decomposition approach for dual-energy computed tomography (DECT) called SIRZ that provides precise and accurate material description, independent of the scanner, over diagnostic energy ranges (30 to 200 keV). System independence is achieved by explicitly including a scanner-specific spectral description in the decomposition method, and a new X-ray-relevant feature space. The feature space consists of electron density, , and a new effective atomic number, , which is based on published X-ray cross sections. Reference materials are used in conjunction with the system spectral response so that additional beam-hardening correction is not necessary. The technique is tested against other methods on DECT data of known specimens scanned by diverse spectra and systems. Uncertainties in accuracy and precision are less than 3% and 2% respectively for the ( , ) results compared to prior methods that are inaccurate and imprecise (over 9%).