WO2010085856A1 - Procédé et appareil d'évaluation de la progression d'une maladie cérébrale - Google Patents

Procédé et appareil d'évaluation de la progression d'une maladie cérébrale Download PDF

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Publication number
WO2010085856A1
WO2010085856A1 PCT/AU2010/000095 AU2010000095W WO2010085856A1 WO 2010085856 A1 WO2010085856 A1 WO 2010085856A1 AU 2010000095 W AU2010000095 W AU 2010000095W WO 2010085856 A1 WO2010085856 A1 WO 2010085856A1
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WIPO (PCT)
Prior art keywords
brain
data
candidate subject
disease
state
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PCT/AU2010/000095
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English (en)
Inventor
Jurgen Fripp
Olivier Salvado
Pierrick Bourgeat
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Commonwealth Scientific And Industrial Research Organisation
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Priority claimed from AU2009900356A external-priority patent/AU2009900356A0/en
Application filed by Commonwealth Scientific And Industrial Research Organisation filed Critical Commonwealth Scientific And Industrial Research Organisation
Publication of WO2010085856A1 publication Critical patent/WO2010085856A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30016—Brain

Definitions

  • the present invention relates to the field of Biomedical Image Processing, and, in particular, discloses a method and apparatus for scoring the progression of a brain disease such as Alzheimer's disease.
  • a brain disease such as Alzheimer's disease is characterised by the accumulation in the brain of Amyloid plaques, leading to neuronal death, and cognitive decline.
  • the affects of Alzheimer's disease normally result in a slow progression over many years.
  • Clinicians usually categorized a subject as Healthy control (HC), Mild cognitive impaired (MCI), and Alzheimer's disease (AD), based on cognitive tests. Patients clinically diagnosed with MCI have been found to convert to AD at a rate of up to 30% over three years.
  • HC Healthy control
  • MCI Mild cognitive impaired
  • AD Alzheimer's disease
  • Several imaging modalities are known to show different aspects of the disease: For example:
  • PET-PiB shows accumulation of plaques (new marker);
  • PET-FDG shows loss of neuronal activity
  • PET-PiB plaques accumulate and spread in the brain with a characteristic pattern. It has also been shown that the brain's neurons die with a different specific pattern. Other modalities reveal other patterns of evolution of this disease.
  • Most clinical use of PET-PiB involves the simple calculation of signal average in some part of the brain (e.g. neocortex) after normalization with some other parts (e.g. cerebellum). This is usually converted into a binary diagnosis of PiB+ or PiB- by thresholding at some specific value (i.e. PiB+ if the average signal of the neocortex is greater than 1.5 times the average signal of the cerebellum).
  • a method of characterizing the state of brain disease within a candidate subject comprising the steps of: (a) acquiring brain data of the brain of a number of subjects, each of the subjects having a known brain state or condition relative to the brain disease; (b) utilizing a statistical method to map the brain data to a reduced dimensional space; (c) collecting like brain data states to determine a healthy brain cluster position in the reduced dimensional space and a brain disease condition cluster position in the reduced dimensional space; (d) for the candidate subject, acquiring the brain data for the candidate subject, and, mapping the candidate subject's brain data to the reduced dimensional space; and (e) determining a relative position of the mapping of the candidate subject's brain data relative to the healthy cluster position and the brain condition cluster position; and utilizing the relative position as a measure of characterization of the state of brain disease within the candidate subject.
  • a method of characterising the state of brain disease within a candidate subject comprising the steps of: (a) determining physiological brain data of the brain of a number of subjects, each of the subjects having a known brain state or condition relative to the brain disease; (b) utilising a statistical analysis (e.g.
  • the principal component analysis process on the brain data to reduce the large number of pixels to form a series of component vectors, (c) collecting like brain data states to determine a healthy brain cluster position in the component space and a brain disease condition cluster position in the component space; (d) for the candidate subject, acquiring the brain data for the candidate subject, and, mapping the candidate subject's brain data to the components vectors; (e) determining a relative position of the mapping of the candidate subject's brain data relative to the healthy cluster position and the brain condition cluster position; and utilising the relative position as a measure of characterisation of the state of brain disease within the candidate subject.
  • a system for characterizing the state of a brain disease within a candidate subject including: a first acquisition unit for acquiring first brain data from a first group of subjects having a known brain state or condition relative to the brain disease; a data dimensionality reduction unit for statistically reducing the first brain data to a corresponding reduced dimensional space; a first processing unit for processing the reduced first brain data to determine a healthy brain cluster position in the reduced dimensional space and a brain disease condition cluster position in the reduced dimensional space; a further acquisition unit for acquiring corresponding brain data for said candidate subject; a further data dimensionality reduction unit for dimensionally reducing the candidate subject brain data to corresponding candidate reduced brain data; and a further processing unit for determining a relative position of the candidate reduced brain data to the healthy brain cluster position and the brain disease condition cluster position, and outputting the relative position information as a state of a brain disease within a candidate subject.
  • the physiological brain data can include at least one of a PET-PiB scan of the brain or a MRI scan of the brain.
  • the brain disease can be Alzheimer's disease.
  • the dimensionality reduction method can be principal component analysis, the number of principal components utilised can be two.
  • the brain data can comprise at least two separate scans of a subject's brain using different scanning modalities.
  • the relative position of the mapping of the candidate subject's brain data can be determined in a non-linear manner.
  • Fig. 1 is a flow chart of the steps of patient processing in the preferred embodiment
  • Fig. 2 is a flow chart of the steps involved in background preparation of data
  • Fig. 3 is a flow chart of the steps involved in calculating of HC and AD clusters
  • Fig. 4 is a flow chart of calculating a patient's cluster position
  • Fig. 5 illustrates example plots of data points on two principal components
  • Fig. 6 illustrates the process of determination of clusters and an associated composite score.
  • the preferred embodiment involves a software system that allows the scoring of the degree of Alzheimer's disease in a patient from their medical images (PET and/or MR). This is achieved by characterizing the amyloid load and neuronal loss suffered by the patient, which is compared to statistical analysis performed on a large population and (if available) previous scans.
  • PET and/or MR medical images
  • Using a model that characterizes the pattern of evolution rather than a simple signal average, allows the better characterization of the likely progression of HC, MCI and AD subjects in a fully automatic way.
  • This preferred embodiment provides a method used to characterize and score the medical images, and its use allows evaluating the presence or risk of developing Alzheimer's disease (AD) or related conditions.
  • the preferred embodiments have practical use in the early diagnosis of disease, in monitoring humans at risk of developing AD, and in enabling better treatment and management decisions to be made in clinically and sub-clinically affected humans.
  • the preferred embodiment provides a continuous value to characterize patients' progression. Previous approaches have had limited sensitivity and specificity in diagnosing AD. The present approach has been found to improve this significantly by increasing the differentiation between AD and PiB+ NCs. It is also more sensitive to longitudinal changes.
  • Fig. 1 illustrates the steps 10 of the preferred embodiment.
  • the user loads 11 the scans from PET-PiB, MRI-TlW or any other available scan and provide information about the subject (e.g. age, gender).
  • the scans are then utilised to produce a corresponding 3-D volume of the brain containing a series of voxels.
  • the software then computes a score between 0.0 and 1.0.
  • a 0.0 corresponds to a healthy person and 1.0 to advanced Alzheimer's disease, taking into account the demographics of the subject such as age, gender, etc
  • the scoring can then be used either to help in diagnosing the subject, to assess the efficacy of a treatment (the score should go down if the treatment is effective), or to compute the average score of a group of individuals in order to study a new therapy or a specific characteristic of the group (e.g. genetic mutation).
  • the scans from a large number of predefined subjects are loaded into the system.
  • the large population of subjects has been previously clinically diagnosed as HC, MCI and AD (typically the AIBL database is utilised).
  • a number of steps are taken as indicated 20 in Fig. 2.
  • Each subject's scans are initially acquired 21 and entered into a template containing the patients demographics.
  • the images are corrected for image artefacts and degradations 24, 25.
  • a principle component analysis (PCA) is performed 26 on the voxel intensity values to allow a statistical analysis to extract the pattern of evolution of the disease for each imaging modality.
  • PCA principle component analysis
  • the PCA allows for the computation of an axis that shows the pattern of evolution in the whole population from HC to AD.
  • the axis of evolution can be determined via the steps illustrated in Fig. 3. All known subjects are initially mapped to the first few principal components 31. Next, those in a healthy group (HC), having no symptoms or image features (e.g. no plaque detected by PiB) are collated and a "center location" determined. A second group of patients having an advanced stage of AD and with the typical image features (e.g. pronounced pattern of plaque deposition detected by PiB) is determined 32. A center location is also determined for this group. The average center location of each group defines two prototypes that we use to normalize the axis between 0 (HC) and 1 (AD) 33.
  • Fig. 4 For a new individual, the steps of Fig. 4 are carried out. Initially, the scans are acquired and processed 42 in the usual way by alignment to a template and image corrections etc. Next, a corresponding position along the axis between HC and AD is computed giving a score between 0 and 1. This "AD score" reflect the progression of the subject towards AD. It provides a quantitative assessment of a new subject at a single time point, and allows monitoring the disease progression on a given subject, or a population.
  • composite scores can be computed using patterns from different sources. Either using two different scores (e.g. plaque deposition and tissue loss), or using a single axis in a multi-dimensional scoring system. Further, the axis can be linear or non linear (e.g. a smooth manifold).
  • the preferred embodiment allows for a more accurately characterize of features of PiB data and relates them to clinical outcomes using a simple relationship to a trained model. As will be evident, the preferred embodiment utilizes training to estimate weighted images to characterize
  • Fig. 5 shows an initial set of example results.
  • the plot point shape codes the clinical assessment. A group of healthy examples is seen on the right, whereas the group of AD clusters on the left.
  • a new axis defining a score is computed between HC (0.0) and AD (1.0).
  • two dimensions are used corresponding to the two first principal components of a statistical analysis of PET-PiB images.

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  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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Abstract

L'invention porte sur un procédé de caractérisation de l'état d'une maladie cérébrale chez un sujet candidat, le procédé comprenant les étapes consistant : (a) à acquérir des données concernant le cerveau d'un certain nombre de sujets, chacun des sujets ayant un état ou une condition cérébrale connue se rapportant à la maladie cérébrale; (b) à utiliser un procédé statistique de réduction de dimensionnalité sur les données concernant le cerveau afin de former une série de vecteurs composants; (c) à recueillir les états de données concernant le cerveau similaires afin de déterminer une position de groupe de cerveaux sains dans l'espace de composant principal et une position de groupe de condition de maladie cérébrale dans l'espace de composant principal; (d) pour le sujet candidat, à acquérir les données concernant le cerveau physiologiques pour le sujet candidat et à mettre en correspondance ces données concernant le cerveau sur les vecteurs composants; (e) à déterminer une position relative du mappage des données concernant le cerveau du sujet candidat par rapport à la position du groupe sain et à la position de groupe de condition cérébrale, et à utiliser la position relative sous la forme d'une mesure de caractérisation de l'état de maladie cérébrale chez le sujet candidat.
PCT/AU2010/000095 2009-02-02 2010-02-02 Procédé et appareil d'évaluation de la progression d'une maladie cérébrale WO2010085856A1 (fr)

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AU2009900356A AU2009900356A0 (en) 2009-02-02 Brain disease progression scoring method and apparatus
AU2009900356 2009-02-02

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012147016A1 (fr) * 2011-04-26 2012-11-01 Koninklijke Philips Electronics N.V. Imagerie de diagnostic du cerveau

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WO2007023522A1 (fr) * 2005-08-22 2007-03-01 National Center Of Neurology And Psychiatry Dispositif et procédé de diagnostic d’une maladie cérébrale
WO2008093057A1 (fr) * 2007-01-30 2008-08-07 Ge Healthcare Limited Instruments d'aide au diagnostic de maladies neurodégénératives
WO2008155682A1 (fr) * 2007-06-21 2008-12-24 Koninklijke Philips Electronics N.V., Diagnostic différentiel de la démence, basé sur un modèle, et réglage interactif du taux de signification

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Publication number Priority date Publication date Assignee Title
US6490472B1 (en) * 1999-09-03 2002-12-03 The Mcw Research Foundation, Inc. MRI system and method for producing an index indicative of alzheimer's disease
WO2007023522A1 (fr) * 2005-08-22 2007-03-01 National Center Of Neurology And Psychiatry Dispositif et procédé de diagnostic d’une maladie cérébrale
WO2008093057A1 (fr) * 2007-01-30 2008-08-07 Ge Healthcare Limited Instruments d'aide au diagnostic de maladies neurodégénératives
WO2008155682A1 (fr) * 2007-06-21 2008-12-24 Koninklijke Philips Electronics N.V., Diagnostic différentiel de la démence, basé sur un modèle, et réglage interactif du taux de signification

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KLOPPEL S ET AL.: "Automatic classification of MR scans in Alzheimer's disease", BRAIN., vol. 131, 2008, pages 681 - 689 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012147016A1 (fr) * 2011-04-26 2012-11-01 Koninklijke Philips Electronics N.V. Imagerie de diagnostic du cerveau
CN103501701A (zh) * 2011-04-26 2014-01-08 皇家飞利浦有限公司 诊断性脑成像
JP2014516414A (ja) * 2011-04-26 2014-07-10 コーニンクレッカ フィリップス エヌ ヴェ 脳の画像診断

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