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    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="http://arxiv.org/abs/1402.1310"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1302.1861"/>
	<rdf:li rdf:resource="http://www.technologyreview.com/biomedicine/25919/page2/"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1007.5129"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1007.2467"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1006.1031"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0905.2521"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.0898"/>
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  </channel><item rdf:about="http://arxiv.org/abs/1402.1310">
    <title>[1402.1310] Feasibility-Seeking and Superiorization Algorithms Applied to Inverse Treatment Planning in Radiation Therapy</title>
    <dc:date>2014-12-10T10:53:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1402.1310</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We apply the recently proposed superiorization methodology (SM) to the inverse planning problem in radiation therapy. The inverse planning problem is represented here as a constrained minimization problem of the total variation (TV) of the intensity vector over a large system of linear two-sided inequalities. The SM can be viewed conceptually as lying between feasibility-seeking for the constraints and full-fledged constrained minimization of the objective function subject to these constraints. It is based on the discovery that many feasibility-seeking algorithms (of the projection methods variety) are perturbation-resilient, and can be proactively steered toward a feasible solution of the constraints with a reduced, thus superiorized, but not necessarily minimal, objective function value.
]]></description>
<dc:subject>healthcare radiology planning operations-research multiobjective-optimization nudge-targets algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e80e16a170fd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:healthcare"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
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<item rdf:about="http://arxiv.org/abs/1302.1861">
    <title>[1302.1861] GPU-based Monte Carlo radiotherapy dose calculation using phase-space sources</title>
    <dc:date>2013-03-06T18:51:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.1861</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A novel phase-space source implementation has been designed for GPU-based Monte Carlo dose calculation engines. Due to the parallelized nature of GPU hardware, it is essential to simultaneously transport particles of the same type and similar energies but separated spatially to yield a high efficiency. We present three methods for phase-space implementation that have been integrated into the most recent version of the GPU-based Monte Carlo radiotherapy dose calculation package gDPM v3.0. The first method is to sequentially read particles from a patient-dependent phase-space and sort them on-the-fly based on particle type and energy. The second method supplements this with a simple secondary collimator model and fluence map implementation so that patient-independent phase-space sources can be used. Finally, as the third method (called the phase-space-let, or PSL, method) we introduce a novel strategy to pre-process patient-independent phase-spaces and bin particles by type, energy and position. Position bins located outside a rectangular region of interest enclosing the treatment field are ignored, substantially decreasing simulation time with little effect on the final dose distribution. The three methods were validated in absolute dose against BEAMnrc/DOSXYZnrc and compared using gamma-index tests (2%/2mm above the 10% isodose). It was found that the PSL method has the optimal balance between accuracy and efficiency and thus is used as the default method in gDPM v3.0. Using the PSL method, open fields of 4x4, 10x10 and 30x30 cm2 in water resulted in gamma passing rates of 99.96%, 99.92% and 98.66%, respectively. Relative output factors agreed within 1%. An IMRT patient plan using the PSL method resulted in a passing rate of 97%, and was calculated in 50 seconds (per GPU) compared to 8.4 hours (per CPU) for BEAMnrc/DOSXYZnrc.]]></description>
<dc:subject>radiology algorithms monte-carlo-algorithms tomography multiobjective-optimization nudge-targets operations-research healthcare</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e024acf74902/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:monte-carlo-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tomography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:healthcare"/>
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<item rdf:about="http://www.technologyreview.com/biomedicine/25919/page2/">
    <title>Technology Review: Clear CT Scans with Less Radiation</title>
    <dc:date>2010-08-10T11:50:02+00:00</dc:date>
    <link>http://www.technologyreview.com/biomedicine/25919/page2/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The new algorithm by Yadava and his colleagues goes one step further. It uses a more realistic physics model of the x-ray source, the detectors, and the x-ray beam. Each of these three is assumed to have specific diameters instead of being considered a point or a line, Yadava says. Depending on the type of scan, the technique is better than ASIR at cutting image noise, and thus the x-rays can be even less intense. The researchers got high-quality abdomen scans of a human model using an eighth of the radiation dose of a conventional scan."
]]></description>
<dc:subject>radiology medical-technology nudge-targets image-processing sensors operations-research</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:93f85f0ac04b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sensors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1007.5129">
    <title>[1007.5129] An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network</title>
    <dc:date>2010-08-03T13:00:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1007.5129</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity.…"
]]></description>
<dc:subject>medical-technology nudge-targets image-segmentation image-analysis radiology diagnostics</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:310ee0290803/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:diagnostics"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1007.2467">
    <title>[1007.2467] Parametric Level Set Methods for Inverse Problems</title>
    <dc:date>2010-07-29T12:49:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1007.2467</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["In this paper, a parametric level set method for reconstruction of obstacles in general inverse problems is considered. General evolution equations for the reconstruction of unknown obstacles are derived in terms of the underlying level set parameters. We show that using the appropriate form of parameterizing the level set function results a significantly lower dimensional problem, which bypasses many difficulties with traditional level set methods, such as regularization, re-initialization and use of signed distance function.…"
]]></description>
<dc:subject>image-processing radiology numerical-methods inverse-problems inference nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8f698b215ef1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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<item rdf:about="http://arxiv.org/abs/1006.1031">
    <title>[1006.1031] Multiobjective decomposition of integer matrices: application to radiotherapy</title>
    <dc:date>2010-07-29T12:41:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.1031</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["… The aim is to find efficient decompositions that simultaneously minimize the irradiation time, the cardinality of the decomposition and the setup-time to configure the multi-leaf collimator at each step of the decomposition. We propose for this NP-hard multiobjective combinatorial problem a heuristic, based on the adaptation of the two-phase Pareto local search. Experiments are carried out on different size instances and the results are reported."
]]></description>
<dc:subject>operations-research multiobjective-optimization search-algorithms radiology nudge-targets metaheuristics</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:96ffbda83411/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:search-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0905.2521">
    <title>[0905.2521] Dose calculation algorithm of fast fine-heterogeneity correction for heavy charged particle radiotherapy</title>
    <dc:date>2010-06-19T13:19:43+00:00</dc:date>
    <link>http://arxiv.org/abs/0905.2521</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The beam-splitting method for fine-heterogeneity cor- rection will inevitably multiply beams to transport and thus will slow down dose calculation. With the GDS al- gorithm, the dose convolution is made only once after all the beams have been transported, which minimizes the impact of the beam multiplication on computing time. In fact, for the beams individually split into several tens, the calculation time increased only by several times with the GDS. This algorithmic framework will thus enable fast and accurate treatment planning of heavy charged particle ra- diotherapy in the presence of density heterogeneity finer than the size of intrinsic beam blurring."
]]></description>
<dc:subject>radiation-therapy medical-technology algorithms operations-research radiology nudge-targets heuristics numerical-methods</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:05e68fae279c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiation-therapy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:radiology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1005.0898">
    <title>[1005.0898] Approximated segmentation considering technical and dosimetric constraints in intensity-modulated radiation therapy with electrons</title>
    <dc:date>2010-05-09T14:19:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.0898</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["… The present work introduces new heuristic segmentation algorithms for the following optimization problem: Find a segmentation of an approximated matrix using only allowed fields and minimize the approximation error. Finally, the decomposition algorithms were implemented into an optimization programme in order to examine the assumptions of the algorithms for a clinical example. As a result, identical dose distributions with much fewer segments and a significantly smaller number of monitor units could be achieved using dosimetric constraints. Consequently, the dose delivery is more efficient and less time consuming."
]]></description>
<dc:subject>medical-technology operations-research algorithms radiology nudge-targets tomography</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e61c92013f28/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
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