235 research outputs found

    Junior Recital

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    Fabricating PFPE Membranes for Capillary Electrophoresis

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    A process has been developed for fabricating perfluoropolyether (PFPE) membranes that contain microscopic holes of precise sizes at precise locations. The membranes are to be incorporated into laboratory-on-a-chip microfluidic devices to be used in performing capillary electrophoresis. The present process is a modified version of part of the process, described in the immediately preceding article, that includes a step in which a liquid PFPE layer is cured into solid (membrane) form by use of ultraviolet light. In the present process, one exploits the fact that by masking some locations to prevent exposure to ultraviolet light, one can prevent curing of the PFPE in those locations. The uncured PFPE can be washed away from those locations in the subsequent release and cleaning steps. Thus, holes are formed in the membrane in those locations. The most straightforward way to implement the modification is to use, during the ultraviolet-curing step, an ultraviolet photomask similar to the photomasks used in fabricating microelectronic devices. In lieu of such a photomask, one could use a mask made of any patternable ultraviolet-absorbing material (for example, an ink or a photoresist)

    Fabricating PFPE Membranes for Microfluidic Valves and Pumps

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    A process has been developed for fabricating membranes of a perfluoropolyether (PFPE) and integrating them into valves and pumps in laboratory-on-achip microfluidic devices. Membranes of poly(tetrafluoroethylene) [PTFE] and poly(dimethylsilane) [PDMS] have been considered for this purpose and found wanting. By making it possible to use PFPE instead of PTFE or PDMS, the present process expands the array of options for further development of microfluidic devices for diverse applications that could include detection of biochemicals of interest, detection of toxins and biowarfare agents, synthesis and analysis of proteins, medical diagnosis, and synthesis of fuels

    Supporting Peer Help and Collaboration in Distributed Workplace Environments

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    Special Issue on Computer Supported Collaborative LearningIncreasingly, organizations are geographically distributed with activities coordinated and integrated through the use of information technology. Such organizations face constant change and the corresponding need for continual learning and renewal of their workers. In this paper we describe a prototype system called PHelpS (Peer Help System) that facilitates workers in carrying out such "life long learning". PHelpS supports workers as they perform their tasks, offers assistance in finding peer helpers when required, and mediates communication on task-related topics. When a worker runs into difficulty in carrying out a task, PHelpS provides a list of other workers who are ready, willing and able to help him or her. The worker then selects a particular helper with PHelpS supporting the subsequent help interaction. The PHelpS system acts as a facilitator to stimulate learning and collaboration, rather than as a directive agent imposing its perspectives on the workers. In this way PHelpS facilitates the creation of extensive informal peer help networks, where workers help one another with tasks and opens up new research avenues for further exploration of AI-based computer-supported collaborative learning. (http://aied.inf.ed.ac.uk/members98/archive/vol_9/greer/full.html

    Junior Recital

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    Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy

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    Purpose: There are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry. Methods and Materials: Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT. Results: The median percentage dose difference between the CT and sCT was &lt;1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at −0.03% (IQR 0.13, −0.31) and bulk density assignment resulted in the greatest difference at −0.73% (IQR −0.10, −1.01). The mean 3D gamma dose agreement at 3%/2 mm among all sCT methods was 99.8%. The highest agreement at 1%/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation. Conclusions: All methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.</p

    Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

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    Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure

    A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

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    Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

    Optimisation and validation of an integrated magnetic resonance imaging-only radiotherapy planning solution

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    Background and purpose: Magnetic resonance imaging (MRI)-only treatment planning is gaining in popularity in radiation oncology, with various methods available to generate a synthetic computed tomography (sCT) for this purpose. The aim of this study was to validate a sCT generation software for MRI-only radiotherapy planning of male and female pelvic cancers. The secondary aim of this study was to improve dose agreement by applying a derived relative electron and mass density (RED) curve to the sCT. Method and materials: Computed tomography (CT) and MRI scans of forty patients with pelvic neoplasms were used in the study. Treatment plans were copied from the CT scan to the sCT scan for dose comparison. Dose difference at reference point, 3D gamma comparison and dose volume histogram analysis was used to validate the dose impact of the sCT. The RED values were optimised to improve dose agreement by using a linear plot. Results: The average percentage dose difference at isocentre was 1.2% and the mean 3D gamma comparison with a criteria of 1%/1 mm was 84.0% ± 9.7%. The results indicate an inherent systematic difference in the dosimetry of the sCT plans, deriving from the tissue densities. With the adapted REDmod table, the average percentage dose difference was reduced to −0.1% and the mean 3D gamma analysis improved to 92.9% ± 5.7% at 1%/1 mm. Conclusions: CT generation software is a viable solution for MRI-only radiotherapy planning. The option makes it relatively easy for departments to implement a MRI-only planning workflow for cancers of male and female pelvic anatomy.</p
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