Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
  • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
  • A61B6/03—Computed tomography [CT]
  • A61B6/037—Emission tomography
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T11/00—2D [Two Dimensional] image generation
  • G06T11/003—Reconstruction from projections, e.g. tomography
  • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/10—Segmentation; Edge detection
  • G06T7/11—Region-based segmentation
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/4808—Multimodal MR, e.g. MR combined with positron emission tomography [PET], MR combined with ultrasound or MR combined with computed tomography [CT]
  • G01R33/481—MR combined with positron emission tomography [PET] or single photon emission computed tomography [SPECT]
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R33/00—Arrangements or instruments for measuring magnetic variables
  • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
  • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
  • G01R33/48—NMR imaging systems
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; 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 a method and an apparatus for determining areas of weakness in a body.
• the invention particularly relates to a method and device for carrying out
• Positron emission tomography is a nuclear medicine imaging technique that produces sectional images of living organisms by dividing a weakly radioactively labeled one
• a radiopharmaceutical is administered to the living being to be examined, namely radionuclides which emit positrons (beta radiation).
• positrons beta radiation
• two high-energy photons are emitted in exactly opposite directions, ie at an angle of 1 80 degrees to each other (annihilation radiation).
• the PET device contains detectors for the photons annularly arranged around the patient. If two exactly opposite detectors register a photon at the same time, this event is assigned to a decay which has taken place on a straight line between the two detectors. Out of that way
• Decay events are based on the spatial distribution of the radiopharmaceutical inside the body and a series of sectional images is calculated. Frequent application finds the PET in metabolic
• MRT M ⁇ gnetreson ⁇ nztomogr ⁇ phie
• Radio frequency range with which certain atomic nuclei (mostly the
• Hydrogen nuclei / protons in the body. Receive extremely weak electromagnetic fields that are emitted by the excited atomic nuclei. An essential basis for the image contrast are different relaxation times of different
• Classify tissue classes such as gray matter, white matter, cerebrospinal fluid (CSF), adipose tissue, and background to obtain an anatomical picture of the organism being studied.
• CSF cerebrospinal fluid
• a positron emission tomography only allows a measurement of functions, for example with respect to blood circulation or metabolism. Structural information can not be obtained using positron emission tomography.
• Structural information can not be obtained using positron emission tomography.
• an MR measurement and thus a morphological measurement performed. It is so u. a. determines where tissue areas are found, for example, in the case of a brain, so that an association between function and anatomy is possible.
• the morphology of a knee joint or of organs can be visualized with the help of an MR measurement, which is not possible with positron emission tomography.
• MR / PET devices - for example, known from WO 2008/006451 A
• MR / PET devices - which comprise both a magnetic resonance tomograph and a positron emission tomograph so as to be able to carry out both measurements mentioned above, without having to move the organism to be examined.
• Radiation occurs inside an organism and is detected by detectors located outside the organism. Since the radiation on the way from the place of origin to the detector areas of the organism like
• weakening regions Soft tissues, bones and air-filled cavities must pass, This is weakened by these areas (hereafter referred to as weakening regions) and thus incompletely detected by the detectors.
• the different areas of weakening weaken radiation differently.
• Intensity values can be distinguished. Thus, it is not possible to differentiate bone areas and air-filled cavities, as they are displayed in the same gray scale range. However, this would be necessary to properly determine whether radiation has been weakened by a bone or has passed through an air-filled cavity.
• an animal or human body is completely or partially detected by means of magnetic resonance tomography, so that an MRI image data record is obtained.
• the gray levels of the MRI image data set are classified such that each gray level is assigned to a tissue class. For the reasons mentioned above, such a first assignment succeeds only approximately, since not all areas of weakness due to the
• Image data set compared with the anatomy of the examined body or body part examined to check whether the result of the classification is plausible. If the comparison shows that the result of the gray value-based classification of a voxel can not agree with the anatomy of the examined body or the body part examined, the voxel is reclassified so that there is no longer any contradiction between the classification in a tissue class and the known anatomy. So it finds a reclassification depending on
• the body part makes it possible to better detect and correct the attenuation of a radiation.
• a body of a living being is wholly or partially examined by means of positron emission tomography, thus obtaining a PET image data record.
• the living organism is wholly or partially examined by means of magnetic resonance tomography and obtained an MRI image data record. If a living being is only partially examined, the PET and MRI examinations refer to the same
• both measurements take place in an MR / PET device. Otherwise the two image datasets must be matched (registered) so that the two image data sets refer to the same spatial orientation.
• the PET image data serve to represent the function in an organ (as mentioned above) and this PET image data is attenuation corrected.
• the measured function (e.g.
• An MRT image data set obtained in this way comprises a multiplicity of voxels, to each of which a gray value is assigned.
• the gray values are now classified as mentioned, that is, each considered gray value is considered one
• the gray levels of an MRI image data set become gray matter, white matter, cerebrospinal fluid, adipose tissue, and
• Invention first an area or a section of the examined
• tissue classes of gray matter white matter, cerebrospinal fluid, adipose tissue, background, as is known.
• tissue classes of gray matter white matter, cerebrospinal fluid, adipose tissue, background
• a middle transaxial section through a human brain is suitable for this purpose.
• the five mentioned tissue classes whose anatomy, and thus their properties, such as homogeneity, size and spatial position in relation to each other are known. Initially limited to this range, the gray values of the voxels belonging to the slice are approximated. It is thus determined based on the known anatomy, which gray value which
• Tissue class is assigned. If the classification method has been sufficiently trained in this way, then based on this Assignment of the remaining voxels of the MRI image ⁇ based on their
• a data set 1 of a class image is obtained in which each voxel of a tissue class, that is in the case of a head of one of the classes gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), adipose tissue (AT) and background (BG) is assigned.
• GM gray matter
• WM white matter
• CSF cerebrospinal fluid
• AT adipose tissue
• BG background
• BG background
• a voxel classified as background is actually outside the body or body region being examined. If this examination reveals that a voxel lies within the examined body, and yet was classified as a background due to its gray value, it is suitably reclassified depending on the anatomy of the examined body or body part. The reclassification takes place in such a way that the classification into another tissue class does not contradict the known anatomy of the examined body or of the examined body region.
• a voxel has been classified as background (BG), then it should be an area outside the body.
• all voxels are determined which lie outside the considered, known body or outside the considered, known body region. If there are voxels underneath that have not been classified as backgrounds due to their gray value, they are reclassified or renamed and assigned to class BG.
• Step 1 in the case of the examination of a head in each slice of the data set, the out-of-the-head area of the tissue class BG is first detected, for example, by 2D region growing.
• the remaining BG voxels not covered, for example, in the region -Growing are thus within the head and are re-labeled to CSF, since they can not be background.
• the area of the head is detected, for example, with a 3D region growing. All non-head voxels are then assigned to tissue class BG.
• misclassifications in the background, z. B. due to noise or movement artifacts in the patient measurement can be eliminated.
• Region Growing is a picture segmentation method. In this process, homogeneous image elements are merged into regions. First, that will be
• Step 2
• the two eye regions of the head are first detected and segmented, since there is a pronounced connection due to the optic nerve between the two regions. These regions are lateral / anterior in the middle layer area of the head and are surrounded by fatty tissue due to the corpus adiposum orbitae. Therefore, the detection is done by means of a reasonably sized and shaped, preferably square transaxial template of tissue class AT.
• a template is a reference object (here, a particular shape and class property) that moves across a region of the image.
• the image data is in a three-dimensional matrix in which each voxel of the matrix has a class label.
• the template is moved in the middle / front area of the three-dimensional image matrix starting from the front on the left and right side (anatomically: lateral) as long as on each side independently of each other the region of class AT is found, where the largest number the voxel of the AT template is superimposed with voxels of the AT region.
• All vertexes of the AT class superimposed on the template on the right and left side are labeled with the "AUGE" class label and the area of the adipose tissue is then completely captured by 3D Region Growing and labeled as EYE then dilated into the area of GM - dilated to capture the eye area including optic nerves.
• Dilatation means that the area at the edges is widened (see also http://de.wikipedia.org/wiki/Dilatation_ (Image Processing), Stand 1 June 8, 2009). For this purpose a structural element is selected and compared with it (eg a 3x3x3 large element). This process of dilatation can be repeated iteratively. Step 3 :
• the brain region is separated from the extracerebral area by erosion.
• Erosion just like dilatation, is a basic morphological operation
• Voxel position allowed The actual operation consists of the voxelwise displacement of the structure element over the entire image. It will
• the voxel of the picture, where the reference point of the structural element is located belongs to the eroded set (cf.
• CSF Since the brain is surrounded by cerebrospinal fluid, the area CSF is now dilated by erosion. For this, the voxels of the classes GM, WM and AT in the vicinity of CSF are eroded and assigned to CSF. CSF is thereby indirectly dilated.
• this area is detected and all other (in the extracerebral area) voxels of the classes GM, WM and AT are re-routed to CSF.
• the extracerebral area is now completely assigned to CSF.
• all voxels of class AT are assigned to the class WM because they are misclassified adipose tissue voxels that can not occur within the brain. Since in the original MRI image data set the gray values of the fatty tissue are similar to those of the white matter, AT voxels are switched to WM.
• Step 4 Since the region of the brain for the separation of the extracerebral area has been reduced by the erosion, in one embodiment of the invention this is now reversed by dilatation operations. Since the white matter of the brain (WM) is surrounded by a thin cortex of gray matter (GM), but erosion caused this GM area to be completely eroded in some places, the area of the WM class first enters the CSF area dilated. In addition, voxels that belonged to class AT erosion are assigned to class WM during this dilatation. Subsequently, the cortex is reconstructed by dilating the region of the class GM into the region CSF. Finally, all voxels of the region AUGE are also assigned to CSF. The brain is now reconstructed and the extracerebral area completely assigned to the class CSF. Step 5:
• the area CSF in the extracerebral region is reduced.
• the class SB is dilated from the edge region of the head toward the brain.
• CSF voxels which are surrounded in the same layer exclusively by neighboring voxels of the classes BG, CSF or SB are converted into the class SB.
• SB voxels in the vicinity of GM or WM are converted to CSF voxels because there must be cerebrospinal fluid around the brain.
• the extracerebral area is now assigned to SB and the cerebral area to the classes GM, WM and CSF.
• Subsequent 3D Region Growing in the out-of-the-head area of the BG class can capture the background.
• the region of BG voxels and dilated SB voxels, which are not covered by the region-growing, will now be switched to TEMP.
• the enlargement of the head area is reversed by dilating the area BG into the area TEMP.
• the remaining TEMP voxels are then redirected to SB and the extracerebral area is fully segmented. It has been found that particularly good results are achieved if the said sequence of steps is observed. If the said order is only partially complied with, then no optimal but nevertheless improved result compared to the prior art will be obtained.
• a data set is obtained in which the cerebral region is subdivided into the various brain tissue classes (class image in which each cerebral voxel is assigned to one of gray matter (GM), white matter (WM), and CSF (CSF) and in which each voxel of the extracerebral head region is assigned to the class Scalp / Bone (SB) and each non-head voxel is assigned to the Background (BG) class
• class image in which each cerebral voxel is assigned to one of gray matter (GM), white matter (WM), and CSF (CSF) and in which each voxel of the extracerebral head region is assigned to the class Scalp / Bone (SB) and each non-head voxel is assigned to the Background (BG) class
• SB Scalp / Bone
• BG Background
• the following is an assumed first data record 1 with reference to a human head, which resulted from a tissue classification.
• the data set 1 is a tissue class image in which each voxel one of the tissues is associated with gray matter (GM), white matter (WM), CSF, adipose tissue (AT) and background (BG).
• GM gray matter
• WM white matter
• CSF adipose tissue
• BG background
• a second data set 2 is assumed which resulted from a separation of the cerebral and extracerebral regions.
• the data set 2 is a class image in which each voxel of the brain is assigned to one of the classes gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), each voxel of the extracerebral head region of the class (SB) and each not associated with the head voxel of the class background.
• the extracerebral area of the head is then segmented so that each voxel of this area is assigned to one of the cave areas (classes MASTOID, SINUSI-SUSUS4), bone (class BONE) or extracerebral soft tissue (CLASS SOFT): Region in temporal bone [lat , Os temporale]:
• Mastoid cell region [lat. Mastoid process] (class: MASTOID);
• Frontal sinus [lat. Sinus frontalis] (class: SINUSl), the region nasal cavity [lat. Cavum nasi], ethmoidal cells [lat. Cellulae ethmoidales] and sphenoid sinus [lat. Sinus sphenoidalis] (class: SINUS2),
• Soft tissue of the extracerebral head area ie z. Muscle, adipose tissue, skin etc. (class: SOFT)
• the procedure is as follows.
• the brain region (cerebrum and cerebellum, brainstem) is processed as follows: Based on dataset 2, all voxels of classes WM and GM are assigned to class WMGM since the attenuation properties of these regions are considered equal. All voxels of class CSF are initially retained, as these are important for the reconstruction of the cranial bone areas. Finally, the voxels of the classes WMGM and CSF can still be assigned to a uniform class BRAIN, which then receives an attenuation value.
• the extracerebral region of the head is given by all voxels of class SB in dataset 2.
• class affiliation of all voxels in data set 1 is considered.
• Cavity regions and bone regions essentially have the class membership BG or CSF in the data record 1;
• the extracerebral soft tissue regions essentially have the class membership GM, WM or AT, which are now combined to form a class SOFT.
• segmentation of the extracerebral region into debilitating regions is as follows:
• the two mastoid cell regions are detected and segmented.
• the frontal sinus SINUS l
• nasal cavity ethmoidal and sphenoidal sinus
• SINUS3 jaw cavities
• pharynx SINUS4
• the area of the bones (BONE) is reconstructed.
• the anatomical knowledge of the bearing relations of the bone regions to the brain region flows (so CSF surrounds the brain (cerebrum and cerebellum, brainstem) in record 2) and to those already detected Cavities (mastoid cell region, cavities in the facial skull) for detection with a.
• Segmentation is the assignment of each voxel of that range to the corresponding class.
• the class property of the voxels in record 1 flows into either the CSF or BG class and the connectedness of all the cranial and facial skull bones.
• any remaining voxels that are not associated with any of the MASTOID, SINUSI -4, BONE, or SOFT classes and that belong to the extracerebral area in record 2 are assigned to the SOFT class.
• the classes SINUSI -4 can be combined into a class SINUS as an alternative.
• class CSF can be merged with GMWM to form class BRAIN.
• Step 1
• the areas of the class BG (data set 1) located in the extracerebral area (data set 2) are dilated into the area of the class CSF (data set 1) insofar as the CSF voxels lie in the extracerebral area (data set 2), since the BG regions definitely belong to cavities or bones and the CSF areas around the BG areas also belong to cavities or bones due to partial volume effects. CSF voxels that are not in the vicinity of BG areas, on the other hand, tend not to belong to cavities or bones and are therefore (initially) assigned to class SOFT. Subsequently, the area BG is eroded again to separate the interconnected regions. The edge area resulting from the erosion is first assigned to a temporary class TEMP.
• the first regions are the two mastoid cell regions detected and segmented. These regions are located in the head latera l / posterior to the ear canal. Therefore, the detection is done by means of one of the anatomical region of appropriately sized and shaped (here square) transaxial template class BG.
• This template is moved in the lower / posterior region of the matrix (anatomically: caudal / posterior) from the rear on the left and right side (anatomical: lateral) as long as the region of the class BG is independently found on both sides the largest number of voxels of the BG template is superimposed with voxels of the BG region.
• All voxels of class BG (data record 1) on the right and left side superimposed by the template are given the class label MASTOID.
• the area MASTOID is then dilated by dilatation into area BG to capture the entire mastoid cell area.
• Step 3
• the sinus region is detected and segmented.
• This region is in the head median / cranial / anterior. Therefore, the detection is done by means of a class BG template that is reasonably sized and shaped (here rectangular, narrow and long in the lateral direction) of the anatomical region.
• This template is moved in the upper / anterior area of the matrix (anatomically: cranial / anterior) from the beginning between the template positions of the MASTOID templates (ie in the median area) until the region of the class BG is found, where the largest number of voxels of the BG template is superimposed with voxels of the BG region. All voxels of class BG (data record 1) superimposed by the template are provided with the class label SINUSl. The area SINUS1 is then dilated by dilatation into the area BG in order to capture the entire sinus frontalis region.
• the area of the nasal cavity, ethmoidal cells and sphenoid sinus is detected and segmented.
• This region is provided with a single class label SINUS2 because the transitions are so large that a separation is hardly possible.
• This region is median / anterior and caudal (below) to the sinus frontalis region. Therefore, the detection is done by means of a class BG template which is appropriately sized and shaped (here rectangular, narrow and long in the anterior-posterior direction) by means of an anatomical region.
• this template is moved from the front / top (anterior / cranial) in the middle / front area of the matrix until the region of the class BG is found, where the largest number of voxels of the BG template is superimposed with voxels of the BG region. All voxels of the class BG (data record 1) and SINUS I superimposed by the template are provided with the class label SINUS2.
• the area SINUS2 is then dilated by dilatation (first transaxially 2D, then 3D) into the area BG and SINUS1 to cover the entire area of the nasal cavity, ethmoidal cells and sphenoid sinus.
• next two caves are now detected and segmented as the next regions. These regions are in the head median / anterior and caudal (below) to the nasal cavity, ethmoidal cells and sphenoid sinus region. Therefore, the detection is done by means of one of the anatomical region of appropriately large and shaped (here almost square, in the anterior-posterior direction slightly longer) transaxial template class BG.
• this template is moved from the front / top (anterior / cranial) on the left and right sides until the region of the class is independent of each other on both sides BG is found, in which the largest number of voxels of the BG template is superimposed with voxels of the BG region. All voxels of the class BG (data record 1) and SINUS2 on the right and left side superimposed by the template are provided with the class label SINUS3.
• the area SINUS3 is then dilated by dilatation (first transaxially 2D, then 3D) into the area BG and SINUS2 in order to detect the entire area of the pine cavities.
• the area of the pharynx is detected and segmented. This region is located in the head median / anterior and caudal (below) to the nasal cavity, ethmoid cells and sphenoid sinus region as well as behind the area of the pine cavities. Therefore, the detection is done by means of one of the anatomical region of appropriately sized and shaped (here square) transaxial template class BG.
• this template is moved past (posterior) the middle position of the SINUS3 templates in the lower / front area of the matrix from the front / top (anterior / cranial) until the region of the class BG is found, in which the largest number of voxels of the BG template is superimposed with voxels of the BG region. All voxels of class BG (data record 1) superimposed by the template are provided with the class label SINUS4.
• the area SINUS4 is then dilated by dilation into the area BG and SINUS3 in order to detect the entire area of the pharynx.
• the bone reconstruction takes place starting from the mastoid cell region and the frontal sinus region.
• the TEMP voxels adjacent to MASTOID and SINUS I voxels are being redelegged into BONE. These serve as seed voxels for subsequent region growing in the TEMP area.
• All TEMP voxels not covered by the Region Growing will now be redirected to SOFT as they obviously do not belong to the bone area.
• the areas of the class BONE lying around the regions SINUS2-SINUS4 should now be excluded from the further bone reconstruction, since the bone is not very thick here and therefore should not be expanded.
• the regions SINUS2 after caudal and SINUS3-SINUS4 are dilated into the area BONE and the dilated area is then renamed to TEMP.
• Step 1 0
• the reconstruction of the bone is carried out starting from the CSF area around the brain tissue (cerebrum, cerebellum, brainstem), since the brain "floats" in the cerebrospinal fluid and is surrounded by bone (Dataset 2) into the SOFT area when the extracerebral voxel in dataset 1 belongs to class BG or CSF
• This extracerebral CSF area is switched to TEMP I and serves as a starting point for the expansion of the cranial bone area TEMP I dilated into the BONE area.
• WM cerebrospinal fluid
• GM cerebrospinal fluid
• AT Repcord 1
• the CSF region of the brain data set 2 is first dilated in such areas into the SOFT area and the dilated voxels are transposed to TEMP I.
• the TEMP I region is then further dilated into the SOFT region if the considered extracerebral voxel in data set 1 belongs to the BG or CSF class.
• the data set is a class image or tissue class image in which each voxel of the cerebral region is assigned to one of the classes brain tissue (GMWM) and CSF and in which each voxel of the extracerebral region of the head belongs to one of the classes mastoid cell region (MASTOID), cavities in the facial skull (SINUS1-SUSUS4), bone (BONE), and soft tissue (SOFT).
• GMWM brain tissue
• MASTOID classes mastoid cell region
• SINUS1-SUSUS4 cavities in the facial skull
• BONE bone
• SOFT soft tissue
• the three figures a show examples of cuts through class images.
• all cerebral voxels of the class GMWM are shown in green, the voxels of the class CSF dark green.
• all voxels of the class SOFT are white, the voxels of the class BONE are flesh-colored.
• the voxels of the class MASTOID are purple, the sinus voxels (CLASS SINUSl) pink, the voxels of the class SINUS2 red, the voxels of the pine cavity (class SINUS3) orange and the voxels of the class SINUS4 are yellow.
• the two illustrations b show, by way of example, sections through class images superimposed in color on the gray-scale original sections of the underlying T1-weighted input image data in order to illustrate the quality of the segmentation results.
• the background (class BG) is shown in black in Figures a and b.
• the six figures c show 3D representations of three segmented heads. There are two pictures for each head. The left picture shows the surface all segmented caves in blue. The remaining areas are shown in the spatial allocation of the cave regions in the head in the colors red to yellow and also transparent. The illustration on the right shows the surface of the segmented skull bones in blue. Both the 2D and 3D images exemplify the very good quality of the results for different input image data.
• the range CSF and GMWM can still be combined into one class (BRAIN), since the attenuation properties hardly differ.
• the MR / PET preferably used according to the invention is a device for carrying out diagnostic examinations.
• Realization of an MR / PET tomography system consists, for example, of a 3 Tesla MRI apparatus in which a so-called PET insert is inserted (for example the commercially available Siemens 3T MR / PET TimTrio system).
• a so-called PET insert for example the commercially available Siemens 3T MR / PET TimTrio system.
• computer systems are used for controlling the device.
• Classification procedure 1 2.6 sec for the classification of a 256 3D matrix, 2 min 47.5 sec for the separation of cerebral and extracerebral regions, 36.1 sec for the segmentation of the extracerebral region into cavities, bones and soft tissue.
• Morphology imaging is used for the PET attenuation correction by determining the different attenuating areas from the morphological image data. By assigning appropriate
• Attenuation coefficients for 51 1 keV radiation to each voxel of the differently attenuating areas (Alternative Methods for
• Attenuation correction factors (ACF), which are used in reconstructing the PET emission image data for attenuation correction.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Theoretical Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Medical Informatics (AREA)
• Heart & Thoracic Surgery (AREA)
• General Health & Medical Sciences (AREA)
• Pathology (AREA)
• Radiology & Medical Imaging (AREA)
• Biomedical Technology (AREA)
• Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
• Molecular Biology (AREA)
• Surgery (AREA)
• Animal Behavior & Ethology (AREA)
• Optics & Photonics (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• High Energy & Nuclear Physics (AREA)
• Biophysics (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Magnetic Resonance Imaging Apparatus (AREA)
• Image Processing (AREA)
• Image Analysis (AREA)
• Nuclear Medicine (AREA)

Priority Applications (3)

Applications Claiming Priority (2)

Publications (2)

Family

ID=42313930

Family Applications (1)

Country Status (6)

Cited By (1)

Families Citing this family (15)

Citations (1)

Family Cites Families (7)

• 2009
  • 2009-07-03 DE DE102009027448A patent/DE102009027448A1/de not_active Withdrawn
• 2010
  • 2010-06-22 EP EP10724535.9A patent/EP2449528B1/de not_active Not-in-force
  • 2010-06-22 US US13/380,291 patent/US8761482B2/en not_active Expired - Fee Related
  • 2010-06-22 HU HUE10724535A patent/HUE035842T2/en unknown
  • 2010-06-22 WO PCT/EP2010/058811 patent/WO2011000739A1/de not_active Ceased
  • 2010-06-22 JP JP2012516698A patent/JP5840125B2/ja not_active Expired - Fee Related

Patent Citations (1)

Non-Patent Citations (15)

Also Published As

Similar Documents

Legal Events