TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography

TOP-CT (Trajectory with Overlapping Projections X-ray Computed Tomography) is a new class of CT scanning geometries for high throughput industrial CT scanning. In TOP-CT multiple objects move with a constant spacing over the same trajectory between a stationary X-ray source and detector. The projections of multiple objects can overlap, which provides additional flexibility when designing CT scanning geometries. Reconstruction algorithms were developed to reconstruct objects one by one from the overlapping projection data as soon as the objects move out of the field of view of the scanning setup. This makes it possible to make reconstructions while new objects with overlapping projections keep being added. \n \nThe forward problem of TOP-CT is linear with a band block Toeplitz structure, and the matrix of the forward problem can be constructed from multiple copies of a non-overlapping CT projection matrix, so existing software toolkits can be used for TOP-CT with only a small modification. Simulation experiments and a real life experiment were performed on a U-turn TOP-CT geometry. One experiment showed that reconstructions from an overlapping projection setup have a slightly higher SSIM (0.828 vs 0.811) and similar PSNR (33.50 vs 33.34) compared to a non-overlapping setup, using the same scan time per object and the same reconstruction algorithm (SIRT). Another experiment showed that a reconstruction algorithm making reconstructions one by one using only local projection data performed without loss of quality compared to a baseline reconstruction method using all projection data

Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python

Tomography is a powerful tool for reconstructing the interior of an object from a series of projection images. Typically, the source and detector traverse a standard path (e.g., circular, helical). Recently, various techniques have emerged that use more complex acquisition geometries. Current software packages require significant handwork, or lack the flexibility to handle such geometries. Therefore, software is needed that can concisely represent, visualize, and compute reconstructions of complex acquisition geometries. We present tomosipo, a Python package that provides these capabilities in a concise and intuitive way. Case studies demonstrate the power and flexibility of tomosipo

Characterization of a pneumococcal meningitis mouse model

<p>Abstract</p> <p>Background</p> <p><it>S. pneumoniae </it>is the most common causative agent of meningitis, and is associated with high morbidity and mortality. We aimed to develop an integrated and representative pneumococcal meningitis mouse model resembling the human situation.</p> <p>Methods</p> <p>Adult mice (C57BL/6) were inoculated in the cisterna magna with increasing doses of <it>S. pneumoniae </it>serotype 3 colony forming units (CFU; n = 24, 10⁴, 10⁵, 10⁶and 10⁷CFU) and survival studies were performed. Cerebrospinal fluid (CSF), brain, blood, spleen, and lungs were collected. Subsequently, mice were inoculated with 10⁴CFU <it>S. pneumoniae </it>serotype 3 and sacrificed at 6 (n = 6) and 30 hours (n = 6). Outcome parameters were bacterial outgrowth, clinical score, and cytokine and chemokine levels (using Luminex^®) in CSF, blood and brain. Meningeal inflammation, neutrophil infiltration, parenchymal and subarachnoidal hemorrhages, microglial activation and hippocampal apoptosis were assessed in histopathological studies.</p> <p>Results</p> <p>Lower doses of bacteria delayed onset of illness and time of death (median survival CFU 10⁴, 56 hrs; 10⁵, 38 hrs, 10⁶, 28 hrs. 10⁷, 24 hrs). Bacterial titers in brain and CSF were similar in all mice at the end-stage of disease independent of inoculation dose, though bacterial outgrowth in the systemic compartment was less at lower inoculation doses. At 30 hours after inoculation with 10⁴CFU of <it>S. pneumoniae</it>, blood levels of KC, IL6, MIP-2 and IFN- γ were elevated, as were brain homogenate levels of KC, MIP-2, IL-6, IL-1β and RANTES. Brain histology uniformly showed meningeal inflammation at 6 hours, and, neutrophil infiltration, microglial activation, and hippocampal apoptosis at 30 hours. Parenchymal and subarachnoidal and cortical hemorrhages were seen in 5 of 6 and 3 of 6 mice at 6 and 30 hours, respectively.</p> <p>Conclusion</p> <p>We have developed and validated a murine model of pneumococcal meningitis.</p

Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory

This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is currently a work in progress [Schut 2022]

Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations

Ablation is a medical procedure to treat liver cancer where a needle-like catheter has to be inserted into a tumor, which will then be heated or frozen to destroy the tumor tissue. To guide the catheter, Ultrasound(US) imaging is used which shows the catheter position in real time. However, some tumors are not visible on US images. To make these tumors visible, image fusion can be used between the inter-operative US image and a pre-operative contrast enhanced CT(CECT) scan, on which the tumors are visible. Several methods exist for tracking the motions of the US transducer relative to the CECT scan, but they all require a manual initialization or external tracking hardware to align the coordinate systems of both scans. In this thesis we present a technique for finding an initialization using only the image data. To achieve this, deep learning is used to segment liver vessels and the boundary of the liver in 3D US images. To find the rigid transformation parameters, the SaDE evolutionary algorithm was used to optimize the alignment between the blood vessels and the liver boundary between both scans.Master of Science Double Degree in Computer Science and Electrical EngineeringComputer ScienceElectrical Engineerin

Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory

This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is currently a work in progress [Schut 2022]

Dataset of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples

Summary This dataset is a collection of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples. Description Sample information In 2022, 120 ‘Kanzi’ apples that had been stored under CA conditions (4 °C, 1 kPa O2, 1.5 kPa CO2) for 8 months were obtained from FruitMasters, The Netherlands. The fruit was grown in orchards surrounding Geldermalsen, the Netherlands, and harvested at physiological maturity in 2021. CT acquisition The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020]. A cone beam geometry with a circular trajectory was used to acquire 1440 projection images at an exposure time of 100ms, a tube peak voltage of 90kV, a current of 550uA, and 2 times binning, halving the detector resolution. Volumes were reconstructed with the FDK algorithm and a voxel size of 129.3um. Beam hardening correction was used from the FleXbox package [Kostenko 2020]. To make sure that the grey values could be compared between scans the spectral sensitivity of the scanner was first estimated for each scan individually and the average of these estimates was used for beam hardening correction on all CT scans. All apples were scanned with the stem side on top. Moreover, a line was drawn on all apples from the stem to the calyx. The apples were put in the CT scanner so that the line was facing the X-ray source. The CT volumes are saved as .tiff stacks. All volumes have been cropped to remove the background. Slicing and photograph acquisition One day after CT scanning, the apples were sliced using a modified meat-slicing machine (CaterChef, house brand of EMGA, Mijdrecht, The Netherlands), which is illustrated in the file slicing_machine_labels.png. The sliding surface of the meat-slicing machine was replaced by a transparent acrylic sheet, and a camera was placed behind the slicing surface. While in the machine, each apple was kept in place by a suction cup so that it could not rotate during the slicing. All apples were sliced from the stem end to the calyx end, with a slice thickness of roughly 4mm. Every time before slicing, a picture was taken of the remaining part of the apple through the transparent sliding surface. To ensure that all apples were roughly aligned to the CT scans, the apples were oriented so that the line drawn earlier was on top. The slice photographs are saved as .png files. All photographs have been cropped to remove the background and to center the apple in the image. Visual browning scores After each apple was sliced it was also visually inspected, and a score from one to ten was given to describe the amount of browning in the apple. Research group This dataset was produced in a collaboration between the Computational Imaging group at Centrum Wiskunde & Informatica (CWI), and GREEFA. https://www.cwi.nl/research/groups/computational-imaging https://www.greefa.com/nl/ Contact details dirk [dot] schut [at] cwi [dot] nl Acknowledgments This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory

TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography

TOP-CT (Trajectory with Overlapping Projections X-ray Computed Tomography) is a new class of CT scanning geometries for high throughput industrial CT scanning. In TOP-CT multiple objects move with a constant spacing over the same trajectory between a stationary X-ray source and detector. The projections of multiple objects can overlap, which provides additional flexibility when designing CT scanning geometries. Reconstruction algorithms were developed to reconstruct objects one by one from the overlapping projection data as soon as the objects move out of the field of view of the scanning setup. This makes it possible to make reconstructions while new objects with overlapping projections keep being added. The forward problem of TOP-CT is linear with a band block Toeplitz structure, and the matrix of the forward problem can be constructed from multiple copies of a non-overlapping CT projection matrix, so existing software toolkits can be used for TOP-CT with only a small modification. Simulation experiments and a real life experiment were performed on a U-turn TOP-CT geometry. One experiment showed that reconstructions from an overlapping projection setup have a slightly higher SSIM (0.828 vs 0.811) and similar PSNR (33.50 vs 33.34) compared to a non-overlapping setup, using the same scan time per object and the same reconstruction algorithm (SIRT). Another experiment showed that a reconstruction algorithm making reconstructions one by one using only local projection data performed without loss of quality compared to a baseline reconstruction method using all projection data

Diffuse Cerebral Intravascular Coagulation and Cerebral Infarction in Pneumococcal Meningitis

There is a widely held belief that cerebral infarction after bacterial meningitis is always caused by vasculitis; however, evidence is weak. We hypothesized that diffuse cerebral intravascular coagulation is an additional explanation of cerebral infarction in patients with pneumococcal meningitis. Sixteen brains of adults who died from pneumococcal meningitis were investigated. Clinical data were collected, and brain sections were scored for signs of inflammation and activation of coagulation. Patients with and without cerebral infarction on autopsy were compared. In total, 38% of patients had focal neurological deficits. Patients died at a median of 7 days (range, 0-32 days) after admission. On autopsy, the nine patients (56%) with cerebral infarctions more often had arterial thrombosis (p = 0.04) than patients without infarction. Patients with infarction tended to have more inflammatory infiltrations of brain parenchyma, microvascular proliferation, small vessel vasculitis/endarteritis obliterans, blood clotting/vessel clogging, and venous thrombosis. None of the patients had large vessel vasculitis. Five patients had cerebral infarctions without vasculitis or endarteritis obliterans. Although four patients with cerebral infarctions had small vessel vasculitis or endarteritis obliterans, areas of infarction could not be localized to the blood flow distribution of these vessels. Blood clotting/vessel clogging was seen in all four patients with vasculitis or endarteritis obliterans, but this was also observed in 10 patients without vasculitis or endarteritis obliterans. None of the patients developed disseminated intravascular coagulation. Our results suggest that diffuse cerebral intravascular coagulation is an additional explanation of cerebral infarction complicating pneumococcal meningiti