Image quality in CT: From physical measurements to model observers

KUVAN LAATU TIETOKONETOMOGRAFIASSA: FYSIKAALISISTA MITTAUKSISTA MATEMAATTISEEN KUVA-ANALYYSIIN Tietokonetomografia (TT)-kuvantamisessa kuvan laadun arviointi on tärkeää jotta saavutetaan diagnostiikan asettamat vaatimukset ja samaan aikaan tulisi potilaan säteilyannos pitää mahdollisimman pienenä. Kuvan laatuun vaikuttavien yksittäisten tekijöiden arviointi on tärkeä osa lääketieteellisten röntgenlaitteiden laaduntarkkailua. Kyseiset laatutekijät muodostavat yhdessä tavanomaisten annosindikaattoreiden kanssa tunnusluvut (’figures of merit’, FOM), joiden avulla voidaan määritellä TT-laitteiden optimaalinen annostaso. Tietokonekuvantamisessa tapahtunut kehitys (detektoreiden tehokkuus, kuvankäsittely ja -prosessointi) ovat luonnollisesti lisänneet kliinisen kuvanlaadun vaatimuksia, jotka puolestaan ovat johtaneet arviointimenetelmien sopeuttamiseen ja kehittämiseen. Tämä kirjallisuuskatsaus esittelee erilaisia TT-kuvantamisen laadunarviointi-menetelmiä: fysikaalisiin parametreihin perustuvista mittauksista aina kliinisiin lähestymistapoihin (esim. matemaattiset kuva-analyysit) mukaan lukien ihmisen itse tekemä havainnointi. Työssä tuodaan esille lähinnä standardikuvantamiseen liittyviä kuvanlaatumenetelmiä. Työn tuloksena esitetään tunnuslukujen päivittämistä nykyteknologian vaatimusten mukaiseksi

Image quality in CT: From physical measurements to model observers

Evaluation of image quality (IQ) in Computed Tomography (CT) is important to ensure that diagnostic questions are correctly answered, whilst keeping radiation dose to the patient as low as is reasonably possible. The assessment of individual aspects of IQ is already a key component of routine quality control of medical x-ray devices. These values together with standard dose indicators can be used to give rise to 'figures of merit' (FOM) to characterise the dose efficiency of the CT scanners operating in certain modes. The demand for clinically relevant IQ characterisation has naturally increased with the development of CT technology (detectors efficiency, image reconstruction and processing), resulting in the adaptation and evolution of assessment methods. The purpose of this review is to present the spectrum of various methods that have been used to characterise image quality in CT: from objective measurements of physical parameters to clinically task-based approaches (i.e. model observer (MO) approach) including pure human observer approach. When combined together with a dose indicator, a generalised dose efficiency index can be explored in a framework of system and patient dose optimisation. We will focus on the IQ methodologies that are required for dealing with standard reconstruction, but also for iterative reconstruction algorithms. With this concept the previously used FOM will be presented with a proposal to update them in order to make them relevant and up to date with technological progress. The MO that objectively assesses IQ for clinically relevant tasks represents the most promising method in terms of radiologist sensitivity performance and therefore of most relevance in the clinical environment.publisher: Elsevier articletitle: Image quality in CT: From physical measurements to model observers journaltitle: Physica Medica articlelink: http://dx.doi.org/10.1016/j.ejmp.2015.08.007 content_type: article copyright: Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Associazione Italiana di Fisica in Medicinastatus: publishe

A Comparison of Denoising Algorithms for Effective Edge Detection in X-Ray Fluoroscopy

X-ray fluoroscopy provides various diagnosis and is widely used in interventional radiology. However, the low-dose involved in fluoroscopy generates an intense Poisson-distributed quantum noise. Object recognition and tracking help in many fluoroscopic applications. Edge-detection is essential, but common derivative operators require noise suppression to provide reliable results. Moreover, homoscedasticity of noise is generally assumed, but is not the case of fluoroscopic images. However, the Anscombe transform can stabilize the quantum noise variance. This study presents a comparison of two denoising algorithms to evaluate their performance in edge-detection for real fluoroscopic sequences. VBM4D is one of best video-processing method for Additive White Gaussian Noise (AWGN), while Noise Variance Conditioned Average (NVCA) is a recent, real-time, algorithm specifically tailored for fluoroscopy. Some real fluoroscopic sequences screening the motion of lumbar spine were processed. Noise parameters were estimated using image sequences of a static scene: the relationship between the luminance and the noise variance was obtained. Generalised Anscombe transform and its inverse were applied to use the VBM4D algorithm. Edge-detection was performed by means of the Sobel operator. The Anscombe transform resulted able to stabilise the noise variance and consequently allow the use of algorithms designed for AWGN. The results show that both approaches provide effective identification of object contours (i.e. vertebral bodies). Despite of its simplicity the NVCA algorithm shows better performances than VBM4D on delineation of boundaries of examined spine fluoroscopic scenes. Furthermore, the NVCA algorithm can be realized in hardware and can offer real-time fluoroscopic processing