1,664 research outputs found
Bayesian modelling of organ deformations in radiotherapy
Moderne strÄlebehandling mot kreft er skreddarsydd for Ä gje ein hÞg strÄledose tilpassa svulsten (mÄlvolumet), mens sÄ lite dose som mogleg vert gitt til det friske vevet omkring. Den totale dosen vert levert over nokre veker i daglege "fraksjonar", noko som reduserer biverknader. Under og mellom desse fraksjonane rÞrer dei indre organa pÄ seg heile tida pÄ grunn av pust, fylling av blÊra, tarmar si rÞrsle og ekstern pÄverknad. Likevel vert posisjonen til mÄlvolumet og relevante risikoorgan bestemt pÄ grunnlag av eit statisk 3D-skann som er tatt fÞr behandlinga startar. Den vanlege mÄten Ä sikre seg mot konsekvensar av denne rÞrsla er Ä legge til marginar rundt svulsten. Slik sikrar ein Ä treffe mÄlvolumet, men til gjengjeld fÄr det friske vevet meir dose. Marginane sin storleik er fastsett ved hjelp av statistikk over tidlegare behandla pasienter. Dei statistiske metodane som vert brukte er ofte enkle, og tek berre omsyn til rigid rÞrsle, altsÄ at heile kroppen rÞrer seg i eitt. Dessutan vert det ikkje teke omsyn til rÞrsla til risikoorgan. For Ä berekne dose til risikoorgana er det vanleg Ä anta at forma til organa i planleggingsskannet er representative for forma deira under behandling.
Arbeidet i denne avhandlinga handlar om Ä bruka teknikkar frÄ Bayesiansk statistikk for Ä modellere korleis organ rÞrer og deformerer seg mellom fraksjonane. MÄlet er Ä estimere nÞyaktig den statistiske fordelinga av rÞrsle for eit eller fleire organ til ein pasient. Fordelinga gjev innsikt i korleis organa forandre seg medan behandlinga gÄr for seg. Denne innsikta er nyttig for evaluering av strÄleterapiplanar, statistisk prediksjon av biverknader, sÄkalla robust planlegging og Ä berekna stÞrrelsen pÄ marginar. Metodane som vert presentert er evaluerte for endetarmen (rektum) sine rÞrsler hjÄ prostatakreftpasientar. For desse pasientane er rektum eit viktig risikoorgan, som kan bli ramma bÄde av akutte og seine biverknader, som lekkasje, blÞding og smerter.
Samanlikna med eksisterande metodar har den Bayesianske tilnÊrminga to fordelar: For det fÞrste gir kombinasjonen av populasjonsstatistikk og individuelle data meir nÞyaktige anslag av den pasientspesifikke fordelinga. For det andre estimerer dei nye metodane den sÄkalla systematiske feilen i tillegg til variasjonar frÄ fraksjon til fraksjon.
Den systematiske feilen er forskjellen mellom den estimerte forma pÄ organet under planlegging, og gjennomsnittsforma til organet under bestrÄling. Denne typen feil var tema for artikkel I. Her fekk vi til Ä redusere den systematiske feilen til rektum hjÄ 33 av 37 prostatakreftpasientar ved Ä bruke ein metode som kombinerer forma pÄ rektum under planlegginga og gjennomsnittsforma i populasjonen. Vi vurderte og om denne forbetringa hadde pÄverknad pÄ estimering av summert dose til rektum. Metoden gav ikkje signifikant forbetring for to antatt relevante parametrar (ekvivalent uniform dose og D5%), men gav signifikant reduksjon av bias pÄ det estimerte dose-volum-histogrammet i intervallet 52.5 Gy til 65 Gy.
Hovudarbeidet i dette prosjektet er publisert i artikkel II. Der presenterer vi to modellar for organrÞrsle basert pÄ Bayesianske metodar. Inndata til desse metodane er organformer som er henta frÄ 3D-skanningar. Metodane kan ta ulikt tal slike former, og produserer meir nÞyaktige resultat jo fleire former dei fÄr. Dei gjev anslag av gjennomsnittsforma og kor stor uvissa om denne forma er, i tillegg til anslag av fordelinga av variasjon av former frÄ fraksjon til fraksjon. Vi evaluerte metodane etter kor godt dei kunne berekne "dekningssannsyn", altsÄ sannsynet for at organet skal dekke eit gitt punkt i pasientkoordinatsystemet til ei gitt tid. For denne berekninga mÄtte titusenvis av organformer gjerast om til sÄkalla binÊrmasker, som er 3D-matriser av punkter i pasient-koordinatsystemet der verdien til eit punkt er 1 dersom punktet er inne i organet, og 0 elles. Denne berekninga var mogleg pÄ grunn av programvare som blei implementert for dette prosjektet, og som er presentert i artikkel III.
OgsÄ her var det prostatakreftpasientar sitt rektum som vart brukt til evaluering. Berekningane til dei nye metodane var likare det sanne dekningssannsynet enn tilsvarande berekningar frÄ tidlegare metodar, i signifikant grad, i alle fall opp til tre input. Forskjellen mellom dei to nye algoritmane er i hovudsak kompleksiteten og nÞyaktigheita, og valet mellom algoritmane i ein gitt bruk vil vere ei avveging mellom desse faktorane.
Vi viste ein mÄte modellane kan verte brukte i artikkel IV, som handlar om pasientar som fÄr re-bestrÄling for tilbakefall av prostatakreft. Her brukte vi modellane til Ä berekne forventa akkumulert dose til rektum frÄ dei to behandlingane, og ogsÄ uvissa rundt den forventa dosen. Metoden er basert pÄ representative former" av rektum, altsÄ former som rektum kan ta som er sannsynlege, men lite fordelaktige. Desse formene kan brukast som visuell hjelp for onkologar og doseplanleggjarar, og metoden kan implementerast ved hjelp av eksisterande funksjonar i programvaren for behandlingsplanlegging.
Overordna gir denne avhandlinga nye lÞysingar for den sentrale utfordringa med Ä redusere konsekvensar av organrÞrsle i strÄleterapi. Dei presenterte modellane er dei fÞrste som utnyttar statistikk for populasjonen og data frÄ den enkelte pasienten samstundes, og som tar omsyn til bÄde systematiske og tilfeldige feil.Modern radiotherapy tends to be highly conformal, meaning that a high and uniform dose is delivered to the target volume and as little dose as possible to the surrounding normal tissue. The total radiation dose is delivered across several smaller daily fractions, typically spanning several weeks. During and between these fractions, internal organs are constantly in motion due to factors such as breathing, changes to bladder filling state, intestinal movement and external influences. Nevertheless, the position of the target and relevant organs at risk (OARs) are determined based on a static 3D scan acquired before start of treatment. A common safeguard which is used to take such motion into account is the addition of margins around the target. These margins reduce the chance of missing parts of the target, yet increases dose to the healthy tissue surrounding the target. The margin size is based on statistics from previous patients. However, for the most part, the statistical methods used are very simple, and typically based on an assumption of rigid patient motion. Similarly, motion of the OARs is commonly neglected. For estimation of dose to the OARs, it is common to assume that the organ shape at the static scan is representative for its shape during treatment.
The work in this thesis concerns the use of techniques from Bayesian statistics for modelling inter-fraction organ motion and deformation. The goal is to estimate accurately the statistical distribution of shapes for one or more organs for a given patient. The distribution provides knowledge of how the patient's organs might move and deform during the radiotherapy course. This information is useful for the evaluation of radiotherapy plans, prediction of adverse effects, so-called motion-robust radiotherapy planning, the generation of margins and more. The methods presented in this thesis have been evaluated for predicting deformations of the rectum of prostate cancer patients. For these patients, the rectum is a crucial OAR that is affected by both early and late side effects including leakage, bleeding and pain.
Compared to existing methods, the Bayesian approach developed and implemented in this thesis offers two advantages: first, combining population statistics and individual data leads to more accurate estimates of the patient-specific distribution. Secondly, the new methods estimate the distribution of the so-called systematic error in addition to variations from fraction to fraction.
The systematic error is the difference between the estimated shape/position of an organ at the planning stage and its average shape/position during therapy, and was the subject of paper I. Here, we were able to reduce the systematic error of the rectum in 33 out of 37 prostate cancer patients using a straightforward method to combine the shape of the rectum at the planning CT with the population mean shape. We also evaluated the impact of this improvement on the estimation of dose to the rectum. We found no significant improvement on the estimation of two presumably relevant dose parameters (equivalent uniform dose and D5%). However, we did find significant reduction in the bias of the estimated dose-volume histogram in the range from 52.5 Gy to 65 Gy.
Paper II contains the central work of this project. It presents two organ deformation models based on Bayesian methods. The input data to these algorithms are organ shapes derived from 3D scans. The methods can take a varying number of such inputs from a given patient, and will produce more accurate results the more inputs they are given. They provide an estimate of the mean shape of the organ, as well as the uncertainty of this mean, in addition to the distribution of the variation of shapes from fraction to fraction. The methods were evaluated in the task of estimating coverage probabilities, i.e. the probability that the organ will cover a certain point in the patient coordinate system, for the rectum of prostate cancer patients. For this evaluation, tens of thousands of organ shapes needed to be converted to so-called binary masks, which are 3D arrays of points in the patient coordinate system where the value of each point is 1 if the point is inside the organ and 0 if it is outside. This was enabled by the highly efficient point-in-polyhedron software presented in paper III, which was developed for this project.
The models were given varying number of scans, from 1 to 10, as input, and compared to two existing (non-Bayesian) models. The estimates of the coverage probability produced by the new models were significantly more similar to the ground truth than those produced by the existing models, at least up to three input scans. The main differences between the two new algorithms are their of conceptual complexity and accuracy, and the choice of method in a given application will therefore come down to a trade-off between these qualities.
An application for the models derived in paper II, concerning patients receiving re-irradiation for recurrent prostate cancer, is presented in paper IV. We introduce a way of estimating the expectation and uncertainty of the accumulated dose to the rectum from the two treatment courses. The method is based on "representative shapes" of the rectum, that is, shapes that are probable and also particularly favourable or unfavourable in terms of dose. The advantage is that these shapes can be used as a visual aid for the oncologist or dose planner, and that the method can be implemented using existing features of treatment planning systems.
Overall, this thesis provides novel solutions to the central challenge of organ motion mitigation in RT. The presented models are the first to simultaneously exploit population and patient specific organ motion and addressing both systematic and random errors.Doktorgradsavhandlin
3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.
Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Perceptual justification in the Bayesian brain: A foundherentist account
Preprint artykuĆu zaakceptowanego do druku w czasopiĆmie SyntheseIn this paper, I use the predictive processing (PP) theory of perception to tackle the
question of how perceptual states can be rationally involved in cognition by justifying other mental states. I put forward two claims regarding the epistemological implications of PP. First, perceptual states can confer justification on other mental states because the perceptual states are themselves rationally acquired. Second, despite being inferentially justified rather than epistemically basic, perceptual states can still be epistemically responsive to the mind-independent world. My main goal is to elucidate the epistemology of perception already implicit in PP. But I also hope to show how it is possible to peacefully combine central tenets of foundationalist and coherentist accounts of the rational powers of perception while avoiding the well-recognized pitfalls of either
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