259 research outputs found

    Partially Adaptive STAP Algorithm Approaches to Functional MRI

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    In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis

    Persymmetric Parametric Adaptive Matched Filter for Multichannel Adaptive Signal Detection

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    Artifical intelligence in rectal cancer

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    Data analytical stability of measuring brain activation in fMRI studies

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    Simulation of fMRI data: a statistical approach

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    Magneettikuvauksella ohjattu korkean intensiteetin kohdennettu ultraääniteknologia syöpätautien liitännäishoidoissa ja syöpälääkkeiden annostelussa

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    Ablative hyperthermia (more than 55 °C) has been used as a stand-alone treatment for accessible solid tumors not amenable to surgery, whereas mild hyperthermia (40-45 °C) has been shown effective as an adjuvant for both radiotherapy and chemotherapy. An optimal mild hyperthermia treatment is noninvasive and spatially accurate, with precise and homogeneous heating limited to the target region. High-intensity focused ultrasound (HIFU) can noninvasively heat solid tumors deep within the human body. Magnetic resonance imaging (MRI) is ideal for HIFU treatment planning and monitoring in real time due to its superior soft-tissue contrast, high spatial imaging resolution, and the ability to measure temperature changes. The combination of MRI and HIFU therapy is known as magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU). Low temperature-sensitive liposomes (LTSLs) release their drug cargo in response to heat (more than 40 °C) and may improve drug delivery to solid tumors when combined with mild hyperthermia. MR-HIFU provides a way to image and control content release from imageable low-temperature sensitive liposomes (iLTSLs). This ability may enable spatiotemporal control over drug delivery - a concept known as drug dose painting. The objectives of this dissertation work were to develop and implement a clinically relevant volumetric mild hyperthermia heating algorithm, to implement and characterize different sonication approaches (multiple foci vs. single focus), and to evaluate the ability to monitor and control heating in real time using MR-HIFU. In addition, the ability of MR-HIFU to induce the release of a clinical-grade cancer drug encapsulated in LTSLs was investigated, and the potential of MR-HIFU mediated mild hyperthermia for clinical translation as an image-guided drug delivery method was explored. Finally, drug and contrast agent release of iLTSLs as well as the ability of MR-HIFU to induce and monitor the content release were examined, and a computational model that simulates MR-HIFU tissue heating and drug delivery was validated. The combination of a multifoci sonication approach and the mild hyperthermia heating algorithm resulted in precise and homogeneous heating limited to the targeted region both in vitro and in vivo. Heating was more spatially confined compared to the use of single focus sonication method. The improvement in spatial control suggests that multifoci heating is a useful tool in MR-HIFU mediated mild hyperthermia applications for clinical oncology. Using the mild hyperthermia heating algorithm, LTSL + MR-HIFU resulted in significantly higher tumor drug concentrations compared to free drug and LTSL alone. This technique has potential for clinical translation as an image-guided drug delivery method. MR-HIFU also enabled real-time monitoring and control of iLTSL content release. Finally, computational models may allow quantitative in silico comparison of different MR-HIFU heating algorithms as well as facilitate therapy planning for this drug delivery technique.Ablatiivista hypertermiaa (yli 55 °C) on perinteisesti käytetty leikkauksiin soveltumattomien kasvainten hoitoon. Lievän hypertermian (40-45 °C) on sen sijaan todettu olevan tehokas liitännäishoito syöpätautien säde- ja lääkehoidoille. Suotuisa hypertermiahoito on kajoamatonta ja täsmällisesti kohdistettua. Lämmityksen tulisi lisäksi olla tarkkaa, tasalaatuista ja kohdealueeseen rajoittunutta. Korkean intensiteetin kohdennettu ultraääni (HIFU) -hoito mahdollistaa kasvainten kajoamattoman lämmityksen. Magneettikuvauksen (MK) etuina ovat erinomainen pehmytkudoskontrasti, korkea paikkaresoluutio ja kyky mitata lämpötilan muutoksia. Näin ollen MK soveltuu erinomaisesti HIFU -hoitojen suunnitteluun ja seurantaan. MK:n ja HIFU:n yhdistelmää kutsutaan magneettikuvauksella ohjatuksi korkean intensiteetin kohdennetuksi ultraääniteknologiaksi (MR-HIFU). Lämpötilaherkät liposomit ovat suunniteltuja vapauttamaan lääkeainesisältönsä hieman normaalia ruumiinlämpötilaa korkeammissa lämpötiloissa (yli 40 °C). Yhdessä lievän hypertermian kanssa tämänkaltaiset liposomit voivat mahdollistaa kohdistetun lääkeaineen vapauttamisen. Liposomien sisällön vapautumisen tarkkailu voi myös mahdollistaa tarkan lääkemäärän kohdistetun annostelun kasvaimessa. Väitöskirjatyössä kehitettiin kliinisesti merkittävä lämmitysalgoritmi lievän hypertermian aikaansaamiseksi, toteutettiin usean samanaikaisen kohteen sonikaatio (ultraäänialtistus) menetelmä sekä arvioitiin algoritmin ja menetelmän kykyä kontrolloida kudoksen lämpötilaa käyttäen kliinistä MR-HIFU laitetta. Lisäksi tutkittiin HIFU:n kykyä vapauttaa lääkeaine lämpötilaherkistä liposomeista, karakterisoitiin lääke- ja kontrastiaineen vapautuminen kuvannettavissa olevista lämpötilaherkistä liposomeista sekä tarkasteltiin MR-HIFU:lla aikaansaadun lievän hypertermian potentiaalia kohdentaa lääkeaineen vapautuminen kasvaimeen. Tässä työssä myös validoitiin laskennallinen malli, joka simuloi MR-HIFU:lla aikaansaatua lämmitystä ja siitä johtuvaa lääkeaineen vapautumista, sekä todennettiin MR-HIFU:n sopivuus lämpöablaatioon perustuvaan kohdun pehmytkudoskasvainten hoitomenelmään kliinisessä käytössä. Lievän hypertermian lämmitysalgoritmi yhdessä usean kohteen sonikaatiomenetelmän kanssa tuotti täsmällisen, tasalaatuisen sekä paikallisesti rajoitetun lämmityksen kohdealueessa. Usean kohteen sonikaatiomenetelmä voi siis olla hyödyllinen työkalu MR-HIFU:n lievän hypertermian syöpähoidon sovelluksissa. MR-HIFU yhdessä lämpötilaherkkien liposomien kanssa sai aikaan merkittävästi korkeamman kasvaimen lääkeainekonsentraation verrokkiryhmiin nähden, ja saattaa siten soveltua kliiniseen käyttöön kuvantamisavusteisena lääkehoitona. Liposomien sisällön (lääkeaine + MK-kontrastiaine) vapautumisen kuvannettavuus merkitsee, että MR-HIFU saattaa lisäksi mahdollistaa tarkan lääkeannoksen kohdistetun vapauttamisen

    Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging

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    In der angewandten Statistik können Regressionsmodelle mit hochdimensionalen Koeffizienten auftreten, die sich nicht mit gewöhnlichen Computersystemen schätzen lassen. Dies betrifft unter anderem die Analyse digitaler Bilder unter Berücksichtigung räumlich-zeitlicher Abhängigkeiten, wie sie innerhalb der medizinisch-biologischen Forschung häufig vorkommen. In der vorliegenden Arbeit wird ein Verfahren formuliert, das in der Lage ist, Regressionsmodelle mit hochdimensionalen Koeffizienten und nicht-normalverteilten Zielgrößen unter moderaten Anforderungen an die benötigte Hardware zu schätzen. Hierzu wird zunächst im Rahmen strukturiert additiver Regressionsmodelle aufgezeigt, worin die Limitationen aktueller Inferenzansätze bei der Anwendung auf hochdimensionale Problemstellungen liegen, sowie Möglichkeiten diskutiert, diese zu umgehen. Darauf basierend wird ein Algorithmus formuliert, dessen Stärken und Schwächen anhand von Simulationsstudien analysiert werden. Darüber hinaus findet das Verfahren Anwendung in drei verschiedenen Bereichen der medizinisch-biologischen Bildgebung und zeigt dadurch, dass es ein vielversprechender Kandidat für die Beantwortung hochdimensionaler Fragestellungen ist.In applied statistics regression models with high-dimensional coefficients can occur which cannot be estimated using ordinary computers. Amongst others, this applies to the analysis of digital images taking spatio-temporal dependencies into account as they commonly occur within bio-medical research. In this thesis a procedure is formulated which allows to fit regression models with high-dimensional coefficients and non-normal response values requiring only moderate computational equipment. To this end, limitations of different inference strategies for structured additive regression models are demonstrated when applied to high-dimensional problems and possible solutions are discussed. Based thereon an algorithm is formulated whose strengths and weaknesses are subsequently analyzed using simulation studies. Furthermore, the procedure is applied to three different fields of bio-medical imaging from which can be concluded that the algorithm is a promising candidate for answering high-dimensional problems

    Multiresolution models in image restoration and reconstruction with medical and other applications

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    Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars.

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    This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation accuracy, or throughput, with specific application to inference with synthetic aperture radars (SAR). Sparse scenarios are considered where the interesting element is embedded in a much larger signal space. Policies are examined that adaptively distribute the constrained resources by using observed measurements to inform the allocation at subsequent stages. This thesis studies adaptive allocation policies in three main directions. First, a framework for adaptive search for sparse targets is proposed to simultaneously detect and track moving targets. Previous work is extended to include a dynamic target model that incorporates target transitions, birth/death probabilities, and varying target amplitudes. Policies are proposed that are shown empirically to have excellent asymptotic performance in estimation error, detection probability, and robustness to model mismatch. Moreover, policies are provided with low computational complexity as compared to state-of-the-art dynamic programming solutions. Second, adaptive sensor management is studied for stable tracking of targets under different modalities. A sensor scheduling policy is proposed that guarantees that the target spatial uncertainty remains bounded. When stability conditions are met, fundamental performance limits are derived such as the maximum number of targets that can be tracked stably and the maximum spatial uncertainty of those targets. The theory is extended to the case where the system may be engaged in tasks other than tracking, such as wide area search or target classification. Lastly, these developed tools are applied to tracking targets using SAR imagery. A hierarchical Bayesian model is proposed for efficient estimation of the posterior distribution for the target and clutter states given observed SAR imagery. This model provides a unifying framework that models the physical, kinematic, and statistical properties of SAR imagery. It is shown that this method generally outperforms common algorithms for change detection. Moreover, the proposed method has the additional benefits of (a) easily incorporating additional information such as target motion models and/or correlated measurements, (b) having few tuning parameters, and (c) providing a characterization of the uncertainty in the state estimation process.PHDElectrical Engineering-SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97931/1/newstage_1.pd
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