Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images

We develop an image processing pipeline on Magnetic Resonance Imaging (MRI) sequences to identify features of Focal Cortical Dysplasia (FCD) in patients with MRIvisible FCD. We aim to use a computer-aided diagnosis system to identify epileptogenic lesions with a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences. The model will be validated on paediatric subjects. Preliminary morphometric analysis explored the use of computational models of established MRI features of FCD in aiding identification of subtle FCD on MRI-positive subjects. Following this, classification techniques were considered. The 2-Step Naive Bayes classifier was found to produce 100% subjectwise specificity and 94% subjectwise sensitivity (with 75% lesional specificity, 63% lesional sensitivity). Thus it correctly rejected 13/13 healthy subjects and colocalized lesions in 29/31 of the FCD cases with MRI visible lesions, with 63% coverage of the complete extent of the lesion using supplied lesional labels

Scalp HFO rates are higher for larger lesions

High frequency oscillations (HFO) in scalp EEG are a new and promising non-invasive epilepsy biomarker, providing added prognostic value, particularly in pediatric lesional epilepsy. However, it is unclear if lesion characteristics, such as lesion volume, depth, type, and localization, impact scalp HFO rates. We analyzed scalp EEG from 13 children and adolescents with focal epilepsy associated with focal cortical dysplasia (FCD), low-grade tumors, or hippocampal sclerosis. We applied a validated automated detector to determine HFO rates in bipolar channels. We identified the lesion characteristics in MRI. Larger lesions defined by MRI volumetric analysis corresponded to higher cumulative scalp HFO rates (p=0.01) that were detectable in a higher number of channels (p=0.05). Both superficial and deep lesions generated HFO detectable in the scalp EEG. Lesion type (FCD vs. tumor) and lobar localization (temporal vs. extratemporal) did not affect scalp HFO rates in our study. Our observations support that all lesions may generate HFO detectable in scalp EEG, irrespective of their characteristics, whereas larger epileptogenic lesions generate higher scalp HFO rates over larger areas that are thus more accessible to detection. Our study provides crucial insight into scalp HFO detectability in pediatric lesional epilepsy, facilitating their implementation as an epilepsy biomarker in a clinical setting

Multiple classifier fusion and optimization for automatic focal cortical dysplasia detection on magnetic resonance images

In magnetic resonance (MR) images, detection of focal cortical dysplasia (FCD) lesion as a main pathological cue of epilepsy is challenging because of the variability in the presentation of FCD lesions. Existing algorithms appear to have sufficient sensitivity in detecting lesions but also generate large numbers of false-positive (FP) results. In this paper, we propose a multiple classifier fusion and optimization schemes to automatically detect FCD lesions in MR images with reduced FPs through constructing an objective function based on the F-score. Thus, the proposed scheme obtains an improved tradeoff between minimizing FPs and maximizing true positives. The optimization is achieved by incorporating the genetic algorithm into the work scheme. Hence, the contribution of weighting coefficients to different classifications can be effectively determined. The resultant optimized weightings are applied to fuse the classification results. A set of six typical FCD features and six corresponding Z-score maps are evaluated through the mean F-score from multiple classifiers for each feature. From the experimental results, the proposed scheme can automatically detect FCD lesions in 9 out of 10 patients while correctly classifying 31 healthy controls. The proposed scheme acquires a lower FP rate and a higher F-score in comparison with two state-of-the-art methods

Clinical Applicability of MRI Texture Analysis

Radiologisten kuvien tulkinta on perinteisesti perustunut asiantuntijan näköhavaintoihin. Tietokoneavusteisten menetelmien käyttö lisääntyy radiologisessa diagnostiikassa. Tekstuuria eli kuviorakennetta on käytetty erottelevana ominaisuutena kudoksia luokiteltaessa ja karakterisoitaessa. Kuvan kuviorakenteen ominaisuuksia kuvaavia tekstuuriparametreja voidaan laskea erilaisilla matemaattisilla ja signaalinkäsittelymenetelmillä. Tekstuurianalyysi on antanut lupaavia tuloksia magneettikuvien tarkastelussa. Sen avulla on voitu määrittää sekä pieniä hajanaisia että suurempia paikallisia muutoksia. Menetelmällä on mahdollista havaita ihmissilmälle näkymättömiä sekä näkyviä muutoksia. Menetelmää tulisi tutkia edelleen, koska kliinisen menetelmän kehittämistä varten tarvitaan lisätietoa sen soveltuvuudesta erilaisille aineistoille sekä analyysimenetelmän eri vaiheiden optimoimisesta. Tämän väitöstutkimuksen tavoite oli selvittää magneettikuvauksen tekstuurianalyysin kliinistä käytettävyyttä eri kannoilta. Tutkimusaineisto koostui kolmesta potilasmateriaalista ja yhdestä terveiden urheilijoiden joukosta sekä heidän verrokeistaan. Aineisto kerättiin osina Tampereen yliopistollisessa sairaalassa toteutettuja laajempia tutkimusprojekteja, ja mukaan otettiin yhteensä 220 osallistujaa. Ensimmäisessä osatyössä tarkasteltiin pehmytkudoskuvantamista, non-Hodgkin-lymfooman hoitovasteen arviointia tekstuurianalyysilla. Kaksi seuraavaa osatyötä käsitteli keskushermoston kuvantamista: lieviä aivovammoja sekä MS-tautia. Viimeisessä osatyössä arvioitiin liikunnan vaikutusta urheilijoiden ja verrokkien reisiluun kaulan luurakenteeseen. Kudosten ja muutosten vertailuissa oli edustettuna sekä ympäröivästä kudoksesta visuaalisella tarkastelulla erottumattomia että selkeästi erottuvia rakenteita. Lisäksi tutkimuksessa selvitettiin mielenkiintoalueen käsityönä tehtävän rajaamisen ja magneettikuvaussekvenssin valinnan vaikutusta analyysiin. Yhteenvetona todetaan, että tekstuurimenetelmällä on mahdollista havaita ja karakterisoida tutkimukseen valikoidun aineiston edustamia etiologialtaan erilaisia muutoksia kliinisistä 1.5 Teslan magneettikuvista. Tutkimuksessa käsitellyt yksityiskohdat MRI-kuvasarjojen valinnasta sekä mielenkiintoalueiden piirtämisestä antavat pohjaa kliinisen protokollan kehittämiseen. Osa tutkimusaineistoista oli kokeellisia, ja niiden tulokset tulisi vahvistaa laajemmilla kliinisillä tutkimuksilla.The usage of computerised methods in radiological image interpretation is becoming more common. Texture analysis has shown promising results as an image analysis method for detecting non-visible and visible lesions, with a number of applications in magnetic resonance imaging (MRI). Although several recent studies have investigated this topic, there remains a need for further analyses incorporating different clinical materials and taking protocol planning for clinical analyses into account. The purpose of this thesis was to determine the clinical applicability of MRI texture analysis from different viewpoints. This study is based on three patient materials and one collection of healthy athletes and their referents. A total of 220 participants in wider on-going study projects at Tampere University Hospital were included in this thesis. The materials include a study on non-Hodgkin lymphoma, representing soft tissue imaging with malignant disease treatment monitoring; and two studies on central nervous system diseases, mild traumatic brain injury and multiple sclerosis. A musculoskeletal imaging study investigated load-associated physiological changes in healthy participants? bones. Furthermore, manual Region of Interest (ROI) definition methods and the selection of MRI sequences for analyses of visible and non-visible lesions were evaluated. In summary, this study showed that non-visible lesions and physiological changes as well as visible focal lesions of different aetiologies could be detected and characterised by texture analysis of routine clinical 1.5 T scans. The details of MRI sequence selection and ROI definition in this study may serve as guidelines for the development of clinical protocols. However, these studies are partly experimental and need to be validated with larger sample sizes

Quantitative and textural analysis of magnetization transfer and diffusion images in the early detection of brain metastases

Purpose: The sensitivity of the magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) for early detection of brain metastases was investigated in mice and humans. Methods: Mice underwent MRI twice weekly for up to 31 days following intra-cardiac injection of the brain-homing breast cancer cell line MDA-MB231-BR. Patients with small cell lung cancer underwent quarterly MRI for a year. MTR and ADC were measured in regions of metastasis and matched contralateral tissue at the final time-point and in registered regions at earlier time-points. Texture analysis and linear discriminant analysis were performed to detect metastasis-containing slices. Results: Compared with contralateral tissue, mouse metastases had significantly lower MTR and higher ADC at the final time-point. Some lesions were visible at earlier time-points on the MTR and ADC maps: 24% of these were not visible on corresponding T2-weighted images. Texture analysis using the MTR maps showed 100% specificity and 98% sensitivity for metastasis at the final time-point, with 77% sensitivity 2-4 days earlier and 46% 5-8 days earlier. Only 2/16 patients developed metastases, and their penultimate scans were normal. Conclusion: Some brain metastases may be detected earlier on MTR than conventional T2; however, the small gain is unlikely to justify ‘predictive’ MRI.The authors gratefully acknowledge the Cambridge Institute Biological Resources Unit for expert animal care and technical assistance, the Histopathology Core Facility, Drs Joe Frank and Diane Palmieri for providing the cell line, the advice of Dr. Dan Tozer, and the support of Cancer Research UK [grant number C14303/A17197], the Brian Cross Memorial Trust, the Addenbrooke’s Charitable Trust, the University of Cambridge, Hutchison Whampoa Ltd, the Cambridge Experimental Cancer Medicine Centre, and the NIHR Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1002/mrm.2625

Quantitative and textural analysis of magnetization transfer and diffusion images in the early detection of brain metastases.

PURPOSE: The sensitivity of the magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) for early detection of brain metastases was investigated in mice and humans. METHODS: Mice underwent MRI twice weekly for up to 31 d following intracardiac injection of the brain-homing breast cancer cell line MDA-MB231-BR. Patients with small cell lung cancer underwent quarterly MRI for 1 year. MTR and ADC were measured in regions of metastasis and matched contralateral tissue at the final time point and in registered regions at earlier time points. Texture analysis and linear discriminant analysis were performed to detect metastasis-containing slices. RESULTS: Compared with contralateral tissue, mouse metastases had significantly lower MTR and higher ADC at the final time point. Some lesions were visible at earlier time points on the MTR and ADC maps: 24% of these were not visible on corresponding T2 -weighted images. Texture analysis using the MTR maps showed 100% specificity and 98% sensitivity for metastasis at the final time point, with 77% sensitivity 2-4 d earlier and 46% 5-8 d earlier. Only 2 of 16 patients developed metastases, and their penultimate scans were normal. CONCLUSIONS: Some brain metastases may be detected earlier on MTR than conventional T2 ; however, the small gain is unlikely to justify "predictive" MRI. Magn Reson Med 77:1987-1995, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.The authors gratefully acknowledge the Cambridge Institute Biological Resources Unit for expert animal care and technical assistance, the Histopathology Core Facility, Drs Joe Frank and Diane Palmieri for providing the cell line, the advice of Dr. Dan Tozer, and the support of Cancer Research UK [grant number C14303/A17197], the Brian Cross Memorial Trust, the Addenbrooke’s Charitable Trust, the University of Cambridge, Hutchison Whampoa Ltd, the Cambridge Experimental Cancer Medicine Centre, and the NIHR Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1002/mrm.2625