Examination of Contrast Mechanisms in Optoacoustic Imaging of Thermal Lesions

Examination of Contrast Mechanisms in Optoacoustic Imaging of Thermal Lesions

Examination of Contrast Mechanisms in Optoacoustic Imaging of Thermal Lesions

Examination of Contrast Mechanisms in Optoacoustic Imaging of Thermal Lesions

Shielding development for the VENUS imaging instrument

VENUS is an imaging instrument that will use a broad range of neutron wavelengths, from epithermal to cold, will include enhanced contrast mechanisms, and will offer novel energy-selective imaging techniques that directly connect the structures, properties, and function of complex engineering materials and systems to reveal practical and fundamental answers about their real-world performance. The instrument is to be built at SNS beam line 10 and will face the decoupled poisoned hydrogen moderator. The driving cost for the instrument is the beam line and instrument cave shielding. Initial scoping analyses were performed to estimate thickness and composition of shielding materials for the instrument cave and beam line. In light of the upcoming Proton Power Upgrade (PPU) project, these transport analyses were performed for proton beam on target at 1.3 GeV and 2 MW.

Solving the where problem in neuroanatomy: a generative framework with learned mappings to register multimodal, incomplete data into a reference brain

ABSTRACTMapping information from different brains gathered using different modalities into a common coordinate space corresponding to a reference brain is an aspirational goal in modern neuroscience, analogous in importance to mapping genomic data to a reference genome. While brain-atlas mapping workflows exist for single-modality data (3D MRI or STPT image volumes), generally speaking data sets need to be combined across modalities with different contrast mechanisms and scale, in the presence of missing data as well as signals not present in the reference. This has so far been an unsolved problem. We have solved this problem in its full generality by developing and implementing a rigorous, non-parametric generative framework, that learns unknown mappings between contrast mechanisms from data and infers missing data. Our methodology permits rigorous quantification of the local sale changes between different individual brains, which has so far been neglected. We are also able to quantitatively characterize the individual variation in shape. Our work establishes a quantitative, scalable and streamlined workflow for unifying a broad spectrum of multi-modal whole-brain light microscopic data volumes into a coordinate-based atlas framework, a step that is a prerequisite for large scale integration of whole brain data sets in modern neuroscience.SummaryA current focus of research in neuroscience is to enumerate, map and annotate neuronal cell types in whole vertebrate brains using different modalities of data acquisition. A key challenge remains: can the large multiplicities of molecular anatomical data sets from many different modalities, and at widely different scales, be all assembled into a common reference space? Solving this problem is as important for modern neuroscience as mapping to reference genomes was for molecular biology. While workable brain-to-atlas mapping workflows exist for single modalities (e.g. mapping serial two photon (STP) brains to STP references) and largely for clean data, this is generally not a solved problem for mapping across contrast modalities, where data sets can be partial, and often carry signal not present in the reference brain (e.g. tracer injections). Presenting these types of anatomical data into a common reference frame for all to use is an aspirational goal for the neuroscience community. However so far this goal has been elusive due to the difficulties pointed to above and real integration is lacking.We have solved this problem in its full generality by developing and implementing a rigorous, generative framework, that learns unknown mappings between contrast mechanisms from data and infers missing data. The key idea in the framework is to minimize the difference between synthetic image volumes and real data over function classes of non-parametric mappings, including a diffeomorphic mapping, the contrast map and locations and types of missing data/non-reference signals. The non-parametric mappings are instantiated as regularized but over-parameterized functional forms over spatial grids. A final, manual refinement step is included to ensure scientific quality of the results.Our framework permits rigorous quantification of the local metric distortions between different individual brains, which is important for quantitative joint analysis of data gathered in multiple animals. Existing methods for atlas mapping do not provide metric quantifications and analyses of the resulting individual variations. We apply this pipeline to data modalities including various combinations of in-vivo and ex-vivo MRI, 3D STP and fMOST data sets, 2D serial histology sections including a 3D reassembly step, and brains processed for snRNAseq with tissue partially removed. Median local linear scale change with respect to a histologically processed Nissl reference brain, as measured using the Jacobian of the diffeomorphic transformations, was found to be 0.93 for STPT imaged brains (7% shrinkage) and 0.84 for fMOST imaged brains (16% shrinkage between reference brains and imaged volumes). Shrinkage between in-vivo and ex-vivo MRI for a mouse brain was found to be 0.96, and the distortion between the perfused brain and tape-cut digital sections was shown to be minimal (1.02 for Nissl histology sections). We were able to quantitatively characterize the individual variation in shape across individuals by studying variations in the tangent space of the diffeomorphic transformation around the reference brain. Based on this work we are able to establish co-variation patterns in metric distortions across the entire brain, across a large population set. We note that the magnitude of individual variation is often greater than differences between different sample preparation techniques. Our work establishes a quantitative, scalable and streamlined workflow for unifying a broad spectrum of multi-modal whole-brain light microscopic data volumes into a coordinate-based atlas framework, a step that is a prerequisite for large scale integration of whole brain data sets in modern neuroscience.

Raman Imaging of Small Biomolecules

Imaging techniques greatly facilitate the comprehensive knowledge of biological systems. Although imaging methodology for biomacromolecules such as protein and nucleic acids has been long established, microscopic techniques and contrast mechanisms are relatively limited for small biomolecules, which are equally important participants in biological processes. Recent developments in Raman imaging, including both microscopy and tailored vibrational tags, have created exciting opportunities for noninvasive imaging of small biomolecules in living cells, tissues, and organisms. Here, we summarize the principle and workflow of small-biomolecule imaging by Raman microscopy. Then, we review recent efforts in imaging, for example, lipids, metabolites, and drugs. The unique advantage of Raman imaging has been manifested in a variety of applications that have provided novel biological insights.

Contrast Mechanisms for Tumor Cells by High-frequency Ultrasound

Scanning Acoustic Microscopy (SAM) is a powerful technique for both the non-destructive determination of mechanical and elastic properties of biological specimens and for the ultrasonic imaging at a micrometer resolution. The implication of biomechanical properties during the onset and progression of disease has been established rendering a profound understanding of the relationship between mechanoelastic and biochemical signaling at a molecular level crucial. Computer simulation algorithms were developed for the generation of images and the investigation of contrast mechanisms in high-frequency and ultra-high frequency SAM. Furthermore, we determined the mechanical and elastic properties of HeLa and MCF-7 cells. Algorithms for simulatingV(z)responses were developed based on the ray and wave theory (angular spectrum). Theoretical simulations for high-frequency SAM array designs were performed with the Field II software. In these simulations, we applied phased array beam formation and dynamic apodization and focusing. The purpose of our transducer simulations was to explore volumetric imaging capabilities. The novel transducer arrays designed in this research aim at improving the performance of SAM systems by introducing electronic steering and hence, allowing for the 4D imaging of cells and tissues.

Phase-Contrast and Dark-Field Imaging

Very early, in 1896, Wilhelm Conrad Röntgen, the founding father of X-rays, attempted to measure diffraction and refraction by this new kind of radiation, in vain. Only 70 years later, these effects were measured by Ulrich Bonse and Michael Hart who used them to make full-field images of biological specimen, coining the term phase-contrast imaging. Yet, another 30 years passed until the Talbot effect was rediscovered for X-radiation, giving rise to a micrograting based interferometer, replacing the Bonse–Hart interferometer, which relied on a set of four Laue-crystals for beam splitting and interference. By merging the Lau-interferometer with this Talbot-interferometer, another ten years later, measuring X-ray refraction and X-ray scattering full-field and in cm-sized objects (as Röntgen had attempted 110 years earlier) became feasible in every X-ray laboratory around the world. Today, now that another twelve years have passed and we are approaching the 125th jubilee of Röntgen’s discovery, neither Laue-crystals nor microgratings are a necessity for sensing refraction and scattering by X-rays. Cardboard, steel wool, and sandpaper are sufficient for extracting these contrasts from transmission images, using the latest image reconstruction algorithms. This advancement and the ever rising number of applications for phase-contrast and dark-field imaging prove to what degree our understanding of imaging physics as well as signal processing have advanced since the advent of X-ray physics, in particular during the past two decades. The discovery of the electron, as well as the development of electron imaging technology, has accompanied X-ray physics closely along its path, both modalities exploring the applications of new dark-field contrast mechanisms these days. Materials science, life science, archeology, non-destructive testing, and medicine are the key faculties which have already integrated these new imaging devices, using their contrast mechanisms in full. This special issue “Phase-Contrast and Dark-field Imaging” gives us a broad yet very to-the-point glimpse of research and development which are currently taking place in this very active field. We find reviews, applications reports, and methodological papers of very high quality from various groups, most of which operate X-ray scanners which comprise these new imaging modalities.