[go: up one dir, main page]

WO2009019535A1 - Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique - Google Patents

Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique Download PDF

Info

Publication number
WO2009019535A1
WO2009019535A1 PCT/IB2007/053065 IB2007053065W WO2009019535A1 WO 2009019535 A1 WO2009019535 A1 WO 2009019535A1 IB 2007053065 W IB2007053065 W IB 2007053065W WO 2009019535 A1 WO2009019535 A1 WO 2009019535A1
Authority
WO
WIPO (PCT)
Prior art keywords
parameters
interest
volumes
initial
volume
Prior art date
Application number
PCT/IB2007/053065
Other languages
English (en)
Inventor
Timo Paulus
Alexander Fischer
Lothar Spies
Original Assignee
Koninklijke Philips Electronics N.V.
Philips Intellectual Property & Standards Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips Electronics N.V., Philips Intellectual Property & Standards Gmbh filed Critical Koninklijke Philips Electronics N.V.
Priority to CN200780100144A priority Critical patent/CN101772783A/zh
Priority to PCT/IB2007/053065 priority patent/WO2009019535A1/fr
Priority to EP07805303A priority patent/EP2174292A1/fr
Priority to JP2010518759A priority patent/JP2010536017A/ja
Publication of WO2009019535A1 publication Critical patent/WO2009019535A1/fr

Links

Classifications

    • 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
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/207—Analysis of motion for motion estimation over a hierarchy of resolutions
    • 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
    • G06T2207/10076—4D tomography; Time-sequential 3D tomography
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • 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

Definitions

  • This invention pertains in general to the field of molecular imaging. More particularly the invention relates to data-driven adaptation of initial parameters for pharmacokinetic modeling.
  • Molecular imaging is extensively used in medicine as a technique to image various targets or pathways, particularly in vivo. Tracers functioning as probes facilitate the imaging and chemically interact with their surroundings and in turn alter the image according to the molecular changes occurring within the area of interest. Molecular imaging is applied to many different areas of interest, such as determination of a pre-disease state or molecular states that occur prior to the occurrence or detection of typical symptoms of a disease. Other applications comprise the imaging of gene expression in vivo and the development of novel tracers or bio markers. In order to implement molecular imaging, there are currently several different molecular imaging systems and devices available, such as SPECT (Single Photon Emission Computed Tomography) systems and PET (Positron Emission Tomography) systems.
  • SPECT Single Photon Emission Computed Tomography
  • PET Pierositron Emission Tomography
  • Pharmacokinetic modeling is a technique to estimate functional or biological parameters from time series of medical imaging data.
  • PET is a common imaging modality to acquire this type of data.
  • a radioactively labeled imaging agent (tracer) is administered and the time course of its distribution is measured.
  • a compartment model describes the biological/chemical/physical behavior of the tracer in the tissue of interest.
  • the parameters of this model are to be estimated and have a direct functional interpretation, such as hypoxia for the PET tracer FMISO that can be of diagnostic value.
  • An integral part of pharmacokinetic modeling is the optimization or reconstruction process, where the model parameters are fitted in such a way to give the best description of the observed data.
  • initial parameters have a direct impact on the speed of the reconstruction process, as less iteration steps are necessary if "good” initial parameters have been selected which are relatively “close” to the final “true” parameters. Additionally, if the models used are more complex, the cost-function of the reconstruction may exhibit several local minima in the parameter space. In that case, again it is important that the initial parameters are close to the true parameters to avoid that the parameter reconstruction algorithm runs into a local minimum which may even correspond to physiologically or biologically insensible parameters.
  • the computation speed is of particular interest.
  • the set of initial parameters is the same for each voxel, meaning that the "distance" between initial and true parameters, and as a result both computational speed and robustness of the fits, will vary over the voxels of the analyzed dataset.
  • the present invention preferably seeks to mitigate, alleviate or eliminate one or more of the above-identified deficiencies in the art and disadvantages singly or in any combination and solves at least the above mentioned problems by providing a method, apparatus, computer readable medium, and use according to the appended patent claims.
  • a method is provided for pharmacokinetic modeling of an image dataset comprising a set of volumes of interest having a plurality of volumes, each volume comprising at least one voxel having at least one related data value.
  • the method comprises assigning an initial set of parameter values to initial parameters describing a first type of voxel within said image dataset, calculating a mean voxel data value for each volume in an initial set of volumes of interest comprised in said set of volumes of interest, reconstructing parameters for each volume in said initial set of volumes of interest, based on said mean voxel data value and said initial set of parameter values, resulting in a subsequent set of parameters, and assigning said subsequent set of parameters as initial parameters to a subsequent set of volumes of interest comprised in said image dataset.
  • an apparatus for pharmacokinetic modeling of an image dataset comprising a set of volumes of interest having a plurality of volumes, each volume comprising at least one voxel having at least one related data value.
  • the apparatus comprises a first assigning unit for assigning an initial set of parameter values to initial parameters describing a first type of voxel within said image dataset, a calculation unit for calculating a mean voxel data value for each volume in an initial set of volumes of interest comprised in said set of volumes of interest, a reconstruction unit for reconstructing parameters for each volume in said initial set of volumes of interest, based on said mean voxel data value and said initial set of parameter values, resulting in a subsequent set of parameters, and a second assigning unit for assigning said subsequent set of parameters as initial parameters to a subsequent set of volumes of interest comprised in said image dataset.
  • a computer-readable medium having embodied thereon a computer program for processing by a computer, for pharmacokinetic modeling of an image dataset comprising a set of volumes of interest having a plurality of volumes, each volume comprising at least one voxel having at least one related data value.
  • the computer program comprises a first assigning code segment for assigning an initial set of parameter values to initial parameters describing a first type of voxel within said image dataset, a calculation code segment for calculating a mean voxel data value for each volume in an initial set of volumes of interest comprised in said set of volumes of interest, a reconstruction code segment for reconstructing parameters for each volume in said initial set of volumes of interest, based on said mean voxel data value and said initial set of parameter values, resulting in a subsequent set of parameters, and a second assigning code segment for assigning said subsequent set of parameters as initial parameters to a subsequent set of volumes of interest comprised in said image dataset.
  • a use of the method, apparatus or computer-readable medium according to any of the appended claims 1-18 for diagnosing a disorder or disease in a human is provided.
  • the present invention is applicable to all cases where data, which represents regional information, is subjected to a reconstruction procedure to extract parametric maps.
  • Another feature of the present invention is the data-driven adaptation of the initial values of successive reconstruction steps.
  • FIG. 1 is an illustration showing a one-, two- and three-compartment model
  • Fig. 2 is an illustration showing a volume of interest comprising 8 voxels a-h;
  • Fig. 3 is an illustration showing a method for initial parameter adaptation for reconstruction according to an embodiment
  • Fig. 4 is an illustration showing a volume of interest reduction scheme according to an embodiment
  • Fig. 5 is an illustration showing a volume of interest reduction scheme according to an embodiment
  • Fig. 6 is an illustration showing an apparatus according to an embodiment.
  • Fig. 7 is an illustration showing a volume of interest reduction scheme according to an embodiment.
  • Fig. 1 is showing different examples of known pharmacokinetic compartment models.
  • Fig. Ia illustrates a one-compartment model
  • Fig. Ib illustrates a two-compartment model
  • Fig. Ic illustrates a three-compartment model.
  • the one-compartment model comprises one compartment, in which the tracer concentration is denoted C p .
  • k defines the outflow of the tracer from the compartment.
  • Cp denotes the tracer concentration in a blood compartment
  • C T specifies the concentration in a tissue compartment.
  • the activity function A(x, t) hence describes the bio distribution of the tracer.
  • parameter ⁇ (x) denotes the partition of blood and tissue compartments at a position x in the region of interest.
  • the region of interest may for instance be a human organ, such as the heart.
  • the concentration in the tissue compartment, C ⁇ is composed of an inflow from a reservoir with corresponding tracer concentration C p (specified by the input function) by a rate k and the outflow of tracer by the same rate k.
  • C p tracer concentration
  • Fig. 2 illustrates an image dataset comprising a volume of interest (hereinafter referred to as VOI, and volumes of interest referred to as VOIs) comprising 8 voxels 21a- 2 Ih.
  • VOI volume of interest
  • the set of initial parameters is the same for each voxel a-h independently of the data in the voxels. If the actual true parameters are far from the initial parameters estimate in a voxel the resolution of said voxel in the resulting visualized image will most likely have a large confidence interval meaning that the resolution of the visualized image may be poor or be very noisy/speckled.
  • the following description focuses on embodiments of the present invention applicable to molecular imaging and in particular to the adaptation of initial parameters for kinetic modeling.
  • the invention is not limited to these specific applications or implementations, but may be applied to many other applications within the field of molecular imaging.
  • Use of the present invention reduces the computation time needed for the reconstruction process.
  • reconstruction in this context means subsequent assessment of the parameters, i.e. reconstruction of the kinetic parameters.
  • the present invention increases the robustness of the parameter estimates, as local minima in the parameter space of the cost-function, which do not represent the true solution, may be avoided.
  • the present invention allows the user to interactively control the tradeoff between computation time and spatial resolution, if the method based on the iterative reduction of VOIs is applied.
  • the present invention provides an advantageous way of improving the estimation of relevant initial parameters of reconstruction processes in molecular imaging, which results in shorter process times and more robust results with small confidence intervals.
  • the present invention provides a convenient way of establishing quality assurance procedures, which take into account the individual investigated volume of interest (VOI) of the image dataset leading to variability of initial parameters between investigated VOIs of the image dataset.
  • VOI volume of interest
  • the present invention provides an improved approach of interactively controlling the spatial resolution of the parametric maps created by the reconstruction process before reaching the maximum resolution.
  • the present invention introduces data-driven adaptation of initial parameters for kinetic modeling, which aims at achieving a distribution of initial parameters adapted to the analyzed dataset by iterative pre-processing steps.
  • ⁇ v ⁇ ° contains the entire image dataset.
  • the method comprises assigning
  • the first type of voxel may be chosen to represent healthy tissue or may by some measure be a mean value of the typical parameter range for the image dataset.
  • a parameter in a VOI V 1 0 may take on values between 0 and 1, 0.5 could be used as the initial parameter P 1 0 in the first step.
  • Different parameters for the different volumes of interests within the initial sets of parameters may be used.
  • the method comprises calculating 32 a mean voxel data value for each VOI in the initial set of VOIs. Furthermore the method comprises reconstructing within 33 parameters for each volume in the initial set of VOIs, based on the mean voxel data value for each VOI and the first set of initial parameters resulting in a subsequent set of parameters.
  • the reconstructing in this embodiment may e.g. be a nonlinear regression using the Levenberg-Marquardt-algorithm. A simplex method or any other reconstruction procedure could also be used.
  • reconstruction is used as a synonym for parameter estimation.
  • the advantage of this embodiment is that the initial parameters for each new set of VOIs are not the same for all parts of the image dataset but are adapted based on pre- processed sets of VOIs. This means that the initial parameters used to reconstruct the parameters for the entire image dataset are adapted to the content of the image dataset. This drastically reduces the calculation time and flexibility of the reconstruction process.
  • the method further comprises iteratively repeating within 35 the calculating of 32, reconstructing of 33, and assigning of 34 using the subsequent set of initial parameters (calculated in the j :th iteration) as a new initial set of initial parameters
  • the method further comprises creating, a parametric map including the reconstructed parameters.
  • the parametric map is continuously updated with the latest reconstructed parameter estimates as the method is iteratively repeated.
  • the investigated sets of VOIs may be chosen differently in the method depending on the kind of reconstruction process.
  • a Volume Of Interest (VOI) reduction scheme is provided.
  • the VOI reduction scheme illustrates how successive sets of VOIs are chosen for each successive iteration step of the method.
  • Fig. 4a is shown using 2D VOIs, however 3D VOIs, as in Fig. 4b, are naturally equally possible within the scope of the present invention.
  • the first set of VOIs ⁇ v ⁇ ° 41 contains the entire dataset to be analyzed (i.e. not on the per-voxel basis).
  • the resulting initial parameter estimates from the reconstruction are assigned to VOIs, which are indicated by the different gray shadings, that originate from the same super ordinate VOI. This means that the first set of initial parameters
  • ⁇ p ⁇ ° will be used as initial parameters for the first set of VOIs ⁇ v ⁇ ° 41.
  • the second set of initial parameters ⁇ P ⁇ 1 will be used as initial parameters for the second set of VOIs ⁇ v ⁇ 1 42a- i
  • ⁇ p ⁇ 2 will be used as initial parameters for the third set of VOIs ⁇ v ⁇ 2 43 etc.
  • the overall number of reconstruction steps, summed over all iterations, will be reduced for the majority of the individual voxels in the full VOI, since for each iteration, the initial parameters have been adapted to the data in the prior reconstruction step.
  • the investigated set of VOIs, in the last iteration step will consist of the individual voxels within the image dataset, which will result in the maximum resolution of the resulting parametric map and hence after rendering maximum resolution of the visualized image.
  • the method is repeated until the user decides that the spatial resolution of the parametric map is high enough for his or her purposes (i.e. the sub- volumes are small enough) or until the VOIs in JF 7 ⁇ cannot be divided further (i.e. they already represent single voxels).
  • the final parameter estimates are then given by P/ +l .
  • the predetermined number of sub- VOIs are 3 throughout the entire reconstruction process. In the 3D case, one set of VOIs thus results in 9 (3x3x3) smaller VOIs.
  • the partitioning scheme is defined throughout the reconstruction process, e.g. depending on the difference or ratio between two subsequent sets of initial parameters.
  • the difference or ratio between two consecutive sets of initial parameters is large (e.g. predetermined threshold) the next element in the N-vector may be chosen to be smaller than if the difference or ratio between two consecutive sets of initial parameters is small. In this way the reconstruction process gives feedback to itself. Accordingly if two sets of initial parameters are too diverse, the proceeding reconstruction process compensates by changing the partitioning scheme.
  • This embodiment is advantageous, e.g. when the difference or ratio between two consecutive initial parameter estimates ⁇ P ⁇ 1 , ⁇ pf is larger than a predetermined threshold.
  • the resulting initial parameters ⁇ pf from the last iteration may then be used as initial parameters to the same VOI, i.e. ⁇ v ⁇ 1 , that was used resulting in ⁇ p ⁇ 2 .
  • the set of VOIs may be a set of voxels.
  • the VOIs will arrive on a voxel basis and hence cannot be subdivided further.
  • the greatest resolution of the parametric maps is achieved, provided that good initial parameter estimates are present.
  • the iterative reconstruction model complexity is increased in successive iteration steps.
  • the complexity of this model is stepwise increased so as to in the last iteration the original model is obtained again.
  • the original model consists of three compartments.
  • a simplified version of that model is used, that has been reduced to just one compartment.
  • an intermediate two-compartment model is used, and in the third step one arrives at the original three-compartment model.
  • the method further utilizes a combination of successively smaller VOIs, according to a partitioning scheme, and more complex reconstruction methods. In each iteration step, both the VOIs and the reconstruction models are successively refined.
  • N denotes the total number of voxels in the image dataset.
  • the average number of reconstruction steps for some other methods reconstruction models and the method according to an embodiment is defined as / and / , respectively.
  • the average should be appreciated as the average over all iterations and all VOIs for each iteration.
  • ⁇ is given by the predetermined number of sub- volumes into which a larger VOI is divided ( ⁇ > 1) .
  • the finite may be replaced by the (larger) infinite sum (i.e. k — > ⁇ ) and thus m > ⁇
  • m is larger than this value, i.e. if the average number of iterations is reduced by at least a factor of m, the proposed method is faster.
  • N voxels and / reconstruction steps in the "some other methods" method.
  • Both iterations together consist of N + 1 voxels with an average number / of reconstruction steps. Regarding computation efficiency this means (N + 1) / iterations will be performed, and hence, in accordance with eqn 1 ,
  • the iterative method will be more efficient for a sub-percent reduction in the average number of reconstruction steps.
  • the average over the 100 voxels is first calculated and then reconstructed to get the "improved" starting values and these are used for the second iteration.
  • m is larger than (100+l)/100, i.e. if the average number of iterations is reduced by at least a factor of m, the method according to this embodiment is faster than some other methods, i.e. it needs a smaller total number of reconstruction steps. This means for the given example above that 7 ⁇ 100 * 10/101 ⁇ 9,9 to be more efficient than the "some other methods" method.
  • the method is implemented into an analysis workstation that comes with the medical imaging device, e.g. the PET scanner, as a piece of additional software.
  • the method may run on the same processor and use the same memory as the reconstruction software for example.
  • the image dataset is a 2D, 3D, 4D or higher-dimensional medical image dataset, e.g. created using Computed Tomography, Magnetic Resonance Imaging or Ultrasound Imaging.
  • an apparatus 60 for pharmacokinetic modeling of an image dataset comprising a set of VOIs having a plurality of volumes, each volume comprising at least one voxel having at least one related data value.
  • the apparatus comprises at least one unit for performing the method according to embodiments.
  • the apparatus 60 further comprises a repeating unit 65 for iteratively repeating the calculating, the reconstructing, and the assigning, using each subsequent set of parameters as the initial parameters and each subsequent VOI as the initial set of VOI until a parametric map having a predetermined resolution is achieved.
  • the repeating unit 65 may be used by user interaction to interactively control the spatial resolution of the resulting parametric maps.
  • the user may stop the iterative reconstruction process prior to reaching the maximum resolution (i.e. reconstructing single voxels), if the resolution so far reached is considered adequate. This means that the user may stop the iterative reconstruction process independently of the predetermined spatial resolution.
  • the unit(s) comprised in the apparatus may be any unit(s) normally used for performing the involved tasks, e.g. a hardware, such as a processor with a memory.
  • the processor may be any of variety of processors, such as Intel or AMD processors, CPUs, microprocessors, Programmable Intelligent Computer (PIC) microcontrollers, Digital Signal Processors (DSP), etc.
  • PIC Programmable Intelligent Computer
  • DSP Digital Signal Processors
  • the memory may be any memory capable of storing information, such as Random Access Memories (RAM) such as, Double Density RAM (DDR, DDR2), Single Density RAM (SDRAM), Static RAM (SRAM), Dynamic RAM (DRAM), Video RAM (VRAM), etc.
  • RAM Random Access Memories
  • DDR Double Density RAM
  • SDRAM Single Density RAM
  • SRAM Static RAM
  • DRAM Dynamic RAM
  • VRAM Video RAM
  • the memory may also be a FLASH memory such as a USB, Compact Flash, SmartMedia, MMC memory, MemoryStick, SD Card, MiniSD, MicroSD, xD Card, TransFlash, and MicroDrive memory etc.
  • FLASH memory such as a USB, Compact Flash, SmartMedia, MMC memory, MemoryStick, SD Card, MiniSD, MicroSD, xD Card, TransFlash, and MicroDrive memory etc.
  • the scope of the invention is not limited to these specific memories.
  • the apparatus is comprised in a medical workstation or medical system, such as a PET Scan System, Computed Tomography (CT) system, Magnetic Resonance Imaging (MRI) System or Ultrasound Imaging (US) system.
  • a medical workstation or medical system such as a PET Scan System, Computed Tomography (CT) system, Magnetic Resonance Imaging (MRI) System or Ultrasound Imaging (US) system.
  • CT Computed Tomography
  • MRI Magnetic Resonance Imaging
  • US Ultrasound Imaging
  • the method may be integrated into a stand-alone device, which essentially has processing and memory capability, such as a normal computer that runs the software.
  • processing and memory capability such as a normal computer that runs the software.
  • the apparatus 60 further comprises a render unit 66 for rendering a 2D or 3D visualization of the image dataset based on the resulting parametric map.
  • the apparatus 60 further comprises a display unit 67 for displaying the rendered 2D or 3D visualization to a user.
  • the method according to some embodiments may be used as follows: 1) The user selects the initial set of VOIs that he wants to analyze; 2) The user then sets all options and starts the reconstruction process. 3) On the screen, he would then see in real time the results in each iteration step. That means, that in the first iteration the results would be presented in rather larger VOIs, in the next steps, the VOIs would be more refined (depending on the partitioning scheme), and so on. 4) Once the user decides that the (spatial) resolution is sufficient for the special case he is investigating, he can press a STOP button and the reconstruction is stopped. 5) The results computed so far are then treated as the final ones.
  • a computer-readable medium 70 having embodied thereon a computer program for processing by a computer for pharmacokinetic modeling of an image dataset comprising a set of VOIs having a plurality of volumes, each volume comprising at least one voxel having at least one related data value.
  • the computer program comprises a first assigning code segment 71 for assigning an initial set of parameter values to initial parameters describing a first type of voxel within the image dataset, a calculation code segment 72 for calculating a mean voxel data value for each volume in an initial set of VOIs comprised in the set of VOIs, a reconstruction code segment 73 for reconstructing parameters for each volume in the initial set of VOIs, based on the mean voxel data value and the initial set of parameter values, resulting in a subsequent set of parameters, and a second assigning code segment 74 for assigning the subsequent set of parameters as initial parameters to a subsequent set of VOIs comprised in the image dataset.
  • a first assigning code segment 71 for assigning an initial set of parameter values to initial parameters describing a first type of voxel within the image dataset
  • a calculation code segment 72 for calculating a mean voxel data value for each volume in an initial set of VOIs comprised in the set of
  • the computer-readable medium 70 further comprises a repeating code segment 75 for iteratively repeating the calculating, the reconstructing, and the assigning, using each subsequent set of parameters as the initial parameters and each subsequent VOI as the initial set of VOI, until a parametric map having a predetermined resolution is achieved.
  • the computer-readable medium 70 further comprises a render code segment 76 for rendering a 2D or 3D visualization of the image dataset based on the resulting parametric map.
  • the computer-readable medium 70 further comprises a display code segment 77 for displaying the rendered 2D or 3D visualization to a user.
  • the computer-readable medium comprising code segments arranged, when run by an apparatus having computer-processing properties, for performing all of the method steps defined in some embodiments.
  • the method, apparatus, or computer-readable medium is used for diagnosing a disorder or disease, such as cancer, in a human.
  • a computer readable medium for performing the method according some embodiments of the invention.
  • Applications and use of the above-described method, apparatus, computer readable medium, and use according to embodiments of the invention are various and is applicable to all cases where data, which represents regional information, is subjected to a reconstruction procedure to extract parametric maps.
  • the invention may be implemented in any suitable form including hardware, software, firmware or any combination of these. However, preferably, the invention is implemented as computer software running on one or more data processors and/or digital signal processors.
  • the elements and components of an embodiment of the invention may be physically, functionally and logically implemented in any suitable way. Indeed, the functionality may be implemented in a single unit, in a plurality of units or as part of other functional units. As such, the invention may be implemented in a single unit, or may be physically and functionally distributed between different units and processors.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Image Generation (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

L'invention concerne un procédé, un appareil, une utilisation et un support lisible par ordinateur avantageux permettant d'améliorer l'estimation de paramètres initiaux pertinents pour les processus de reconstruction de paramètres en imagerie moléculaire, ce qui conduit à des durées de processus plus courtes et à des résultats plus solides avec de petits intervalles de confiance.
PCT/IB2007/053065 2007-08-03 2007-08-03 Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique WO2009019535A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
CN200780100144A CN101772783A (zh) 2007-08-03 2007-08-03 药物动力学建模的方法、装置、计算机可读介质及用途
PCT/IB2007/053065 WO2009019535A1 (fr) 2007-08-03 2007-08-03 Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique
EP07805303A EP2174292A1 (fr) 2007-08-03 2007-08-03 Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique
JP2010518759A JP2010536017A (ja) 2007-08-03 2007-08-03 薬物動態学的モデリングのための方法、装置、コンピュータ可読媒体及びそれらの使用

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/IB2007/053065 WO2009019535A1 (fr) 2007-08-03 2007-08-03 Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique

Publications (1)

Publication Number Publication Date
WO2009019535A1 true WO2009019535A1 (fr) 2009-02-12

Family

ID=39232824

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2007/053065 WO2009019535A1 (fr) 2007-08-03 2007-08-03 Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique

Country Status (4)

Country Link
EP (1) EP2174292A1 (fr)
JP (1) JP2010536017A (fr)
CN (1) CN101772783A (fr)
WO (1) WO2009019535A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2365455A1 (fr) * 2010-03-10 2011-09-14 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Procédé d'extraction simultanée de la fonction d'entrée et des paramètres pharmacocinétiques d'un principe actif
JP2013524893A (ja) * 2010-04-16 2013-06-20 バクスター・インターナショナル・インコーポレイテッド 腎不全血液治療、特に、家庭血液透析のための治療予測および最適化
EP2649547A4 (fr) * 2010-12-08 2017-08-02 Invicro, LLC Estimation de paramètres pharmacocinétiques en imagerie
US11813100B2 (en) * 2012-01-04 2023-11-14 The Trustees Of Dartmouth College Methods for quantitative and enhanced-contrast molecular medical imaging using cross-modality correction for differing tracer kinetics
US12285277B2 (en) 2013-02-13 2025-04-29 The Trustees Of Dartmouth College Method and apparatus for medical imaging using differencing of multiple fluorophores

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005109343A2 (fr) 2004-05-10 2005-11-17 Philips Intellectual Property & Standards Gmbh Systeme de traitement de donnees pour analyse compartimentee

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005109343A2 (fr) 2004-05-10 2005-11-17 Philips Intellectual Property & Standards Gmbh Systeme de traitement de donnees pour analyse compartimentee

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
BOUMAN C A ET AL: "Direct Reconstruction of Kinetic Parameter Images From Dynamic PET Data", IEEE TRANSACTIONS ON MEDICAL IMAGING, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 24, no. 5, May 2005 (2005-05-01), pages 636 - 650, XP011131264, ISSN: 0278-0062 *
F.E. TURKHEIMER, R. HINZ, V.J. CUNNINGHAM: "On the Undecidability Among Kinetic Models: From Model Selection to Model Averaging", JOURNAL OF CELEBRAL BLOOD FLOW & METABOLISM, vol. 23, 2003, pages 490 - 498, XP002475171 *
F.E.TURKHEIMER; R. HINZ; V.J. CUNNINGHAM: "On the undecidability Among Kinetic Models: From Model Selection to Model Averaging", JOURNAL OF CELEBRAL BLOOD FLOW & METABOLISH, vol. 23, 2003, pages 490 - 498
J.B. BASSINGTHWAIGHTE, H.J.CHIZECK, L.E ATLAS: "Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems", PROCEEDINGS OF THE IEEE, vol. 94, no. 4, 2006, pages 819 - 831, XP002475186 *
JONATHAN S MALTZ: "Optimal Time-Activity Basis Selection for Exponential Spectral Analysis: Application to the Solution of Large Dynamic Emission Tomographic Reconstruction Problems", IEEE TRANSACTIONS ON NUCLEAR SCIENCE, IEEE SERVICE CENTER, NEW YORK, NY, US, vol. 49, no. 4, August 2001 (2001-08-01), pages 1452 - 1464, XP011042113, ISSN: 0018-9499 *
M.E. KAMASAK, C.A. BOUMAN, E.D. MORRIS, K.D. SAUER: "Parametric reconstruction of kinetic PET data with plasma function estimation", PROCEEDINGS OF SPIE - COMPUTATIONAL IMAGING III, vol. 5674, 2005, pages 293 - 304, XP002475170 *
M.E. KAMASAK; C.A. BOUMAN; E.D. MORRIS; K.D. SAUER: "Parametrc reconstruction of kinetic PET data with plasma function estimation", PROCEEDINGS OF SPIE - COMPUTATIONAL IMAGING III, vol. 5674, 2005, pages 293 - 304
TURKHEIMER ET AL: "Multi-resolution Bayesian regression in PET dynamic studies using wavelets", NEUROIMAGE, ACADEMIC PRESS, ORLANDO, FL, US, vol. 32, no. 1, 20 July 2006 (2006-07-20), pages 111 - 121, XP005768331, ISSN: 1053-8119 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2365455A1 (fr) * 2010-03-10 2011-09-14 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Procédé d'extraction simultanée de la fonction d'entrée et des paramètres pharmacocinétiques d'un principe actif
FR2957441A1 (fr) * 2010-03-10 2011-09-16 Commissariat Energie Atomique Procede d'extraction simultanee de la fonction d'entree et des parametres pharmacocinetiques d'un principe actif.
US8571283B2 (en) 2010-03-10 2013-10-29 Commissariat A L'energie Atomique Et Aux Energies Alternatives Method for simultaneously extracting the input function and pharmacokinetic parameters of an active ingredient
JP2013524893A (ja) * 2010-04-16 2013-06-20 バクスター・インターナショナル・インコーポレイテッド 腎不全血液治療、特に、家庭血液透析のための治療予測および最適化
EP2649547A4 (fr) * 2010-12-08 2017-08-02 Invicro, LLC Estimation de paramètres pharmacocinétiques en imagerie
US11813100B2 (en) * 2012-01-04 2023-11-14 The Trustees Of Dartmouth College Methods for quantitative and enhanced-contrast molecular medical imaging using cross-modality correction for differing tracer kinetics
US12144667B2 (en) 2012-01-04 2024-11-19 The Trustees Of Dartmouth College Methods for quantitative and enhanced-contrast molecular medical imaging using cross-modality correction for differing tracer kinetics
US12285277B2 (en) 2013-02-13 2025-04-29 The Trustees Of Dartmouth College Method and apparatus for medical imaging using differencing of multiple fluorophores

Also Published As

Publication number Publication date
EP2174292A1 (fr) 2010-04-14
JP2010536017A (ja) 2010-11-25
CN101772783A (zh) 2010-07-07

Similar Documents

Publication Publication Date Title
US12223649B2 (en) Systems and methods for medical acquisition processing and machine learning for anatomical assessment
CN112885453B (zh) 用于标识后续医学图像中的病理变化的方法和系统
Tyrrell et al. Robust 3-D modeling of vasculature imagery using superellipsoids
US9754390B2 (en) Reconstruction of time-varying data
CN101115989A (zh) 用于确定血管几何形状和流特征的系统
EP3788633A1 (fr) Procédé indépendant de la modalité pour une représentation d'image médicale
CN103889328B (zh) 灌注成像
CN112969412A (zh) 深谱团注剂跟踪
CN116630463B (zh) 一种基于多任务学习的增强ct图像生成方法和系统
CN113989231A (zh) 动力学参数的确定方法、装置、计算机设备和存储介质
WO2010086810A1 (fr) Analyse d'image par gradient de perfusion transmurale
CN116437853A (zh) 从核磁共振成像数据的生物物理参数精确定量映射的系统和方法
Govyadinov et al. Robust tracing and visualization of heterogeneous microvascular networks
EP2174292A1 (fr) Procédé, appareil, support lisible par ordinateur et utilisation pour une modélisation pharmacocinétique
CN116977466B (zh) 一种增强ct图像生成模型的训练方法和存储介质
CN116703842B (zh) 一种磁共振定量生理参数图生成方法和装置
JP6692001B2 (ja) 表面空間における器官の動脈／組織／静脈の動態系の生理的信号を再構築するためのシステム及び方法
CN113143305B (zh) 提供血管畸形的血流参数组
CN112541882B (zh) 医学体积渲染中的隐式表面着色
Marin et al. Numerical observer for cardiac motion assessment using machine learning
WO2025137799A1 (fr) Simulation d'images avec meilleure amélioration de contraste dans des applications médicales sur la base d'un problème inverse
Stefaniga et al. An approach of segmentation method using deep learning for CT medical images
CN112365593B (zh) 一种pet图像重建的方法和系统
US20250111500A1 (en) Blood vessels and lesion segmentations by deep neural networks trained with synthetic data
Scalzo et al. Computational hemodynamics in intracranial vessels reconstructed from biplane angiograms

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 200780100144.2

Country of ref document: CN

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 07805303

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2007805303

Country of ref document: EP

WWE Wipo information: entry into national phase

Ref document number: 2010518759

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 1064/CHENP/2010

Country of ref document: IN