WO2007137179A2 - systÈme amÉliorÉ et procÉdÉ pour biopsie 3D - Google Patents

systÈme amÉliorÉ et procÉdÉ pour biopsie 3D Download PDF

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Publication number
WO2007137179A2
WO2007137179A2 PCT/US2007/069275 US2007069275W WO2007137179A2 WO 2007137179 A2 WO2007137179 A2 WO 2007137179A2 US 2007069275 W US2007069275 W US 2007069275W WO 2007137179 A2 WO2007137179 A2 WO 2007137179A2
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prostate
image
zone
biopsy
shape model
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PCT/US2007/069275
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English (en)
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WO2007137179A3 (fr
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Jasjit S. Suri
Ramkrishnan Narayanan
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Eigen
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0833—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0833—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures
    • A61B8/0841—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures for locating instruments
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/12—Diagnosis using ultrasonic, sonic or infrasonic waves in body cavities or body tracts, e.g. by using catheters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/483—Diagnostic techniques involving the acquisition of a 3D volume of data

Definitions

  • the present invention relates to medical imaging.
  • One aspect is directed to image guided surgery using 3D patient related statistics.
  • One application is in aiding a urologist during prostate biopsy to find potential cancerous tissue sites for extraction often in the absence of any significant features or distinguishing characteristics of cancers in 3D ultrasound images.
  • Prostate cancer The Center for Prostate Disease Research (CPDR) has projected that there will be over 200,000 new cancer cases and 27,000 deaths from prostate cancer in the year 2007. Prostate cancer alone accounts for roughly 29% of cancer incidences in men. According to the National Cancer Institute (NCI), a man's chance of developing prostate cancer increases drastically from 1 in 10,000 before age 39 to 1 in 45 between 40-59 and 1 in 7 after age 60. The overall probability of developing prostate cancer from birth to death being close to 1 in 6. Traditionally either elevated Prostate Specific Antigen (PSA) level or Digital
  • Rectal Examination has been widely used as a standard for prostate cancer detection.
  • a biopsy of the prostate must be performed. This is done on patients that have either abnormal PSA levels or an irregular digital rectal exam (DRE), or on patients that have had previous negative biopsies but continue to have elevated PSA.
  • Biopsy of the prostate requires that a number of tissue samples (i.e, cores) be obtained from various regions of the prostate. For instance, the prostate may be divided into six regions (i.e., sextant biopsy), apex, mid and base bilaterally, and one representative sample is randomly obtained from each sextant.
  • an image e.g., 3-D ultrasound image
  • an image may be acquired and utilized to guide a biopsy needle to locations on or within the prostate.
  • the present inventors have recognized that the ability to combine statistical data (e.g., cancer data by prostate region) with the image may allow medical personnel to obtain biopsy cores from (or perform procedures on) regions of the prostate having a greater probability of containing cancerous cells if caner is indeed present. More specifically, it has been determined that the occurrence and location of a number of prostate cancers may be based on one or more demographic characteristics (e.g., age, ethnicity, etc.) and that by utilizing such information, the effectiveness of a biopsy procedure may be improved.
  • demographic characteristics e.g., age, ethnicity, etc.
  • the systems and method i.e, utilities
  • a prostate is identified within an ultrasound volume.
  • the identified prostate is mapped, in real time, to a shape model whose contents include statistical information and/or zone related information that is previously determined and stored.
  • one utility involves the training of a prostate shape model and the corresponding association of statistical information with the shape model and another utility involves fitting the shape model to fit patient image/data and the transfer of statistical information from the shape model to the patient image.
  • Such a shape model may be a 3D model such that it can be fit to a 3D ultrasound image. Accordingly, such statistical data may be transferred to locations within the 3D ultrasound image as well as onto the surface of the image.
  • the statistical information transferred to the patient image/data may contain information regarding the various zones of the prostate and also cancer probability maps specific to patient related data (age, PSA level and ethnicity).
  • patient related data e.g., cancer probability maps
  • Such data may allow targeting or treating areas/regions to specific to each patient while still focusing on zones where cancers are most prevalent.
  • such statistical data may be overlaid onto the patient image to allow guiding a biopsy device to a region that is statistically at risk for cancer based on one or more patient specific parameters including, without limitation, demographic parameters (age, ethnicity, etc.), PSA levels etc.
  • overlaid includes the incorporation of statistical data onto and/or into a 3D patient image as well as onto 2D patient images.
  • Statistics are generated from a large database of ground truth images.
  • the procedure begins with the collection of data from histology specimens that are outlined and labeled. These labels correspond to whether cancer is present or not at a 3-D location. Several such samples are collected are used to compile statistics on the presence of cancer locations.
  • the database of such images whose cancer characteristics are known is referred to as ground truth data. These ground truth images are all fitted to a common anatomical frame that contains labels that mark landmark locations of the prostate, whether cancer is present or not.
  • Cancer probability maps are then computed from this data and a cancer probability map/atlas or more generally look-up-table (i.e., LUT) is created. This LUT can be used for biopsy guidance.
  • the acquired 3-D ultrasound image is fit to the LLTT (which could be an image in which the LUT resides) or vice versa.
  • the image including the LUT may be a shape model that is fit to an acquired ultrasound image.
  • 3-D statistical data associated with the LUT, including statistical locations of interest is available (e.g., displayed on and/or within) with the acquired 3-D ultrasound image and can be used to perform biopsy procedures.
  • a shape model may be generated from a database of ultrasound volumes. Such ultrasound volumes may be compiled and segmented either manually or using a segmentation program to obtain several prostate ultrasound surfaces. These surfaces can be used to train a shape model.
  • a shape model may include a mean shape and one or more vectors (e.g., Eigen vectors) that correspond to the principal modes of variation.
  • the projections on these vectors can then be used to describe any shape resembling the training data accurately.
  • the advantage of using shape models is that these projections may represent the direction of largest variance of the data. For instance, 10-15 such projections may adequately represent a large range of shapes accounting for more than 95% of the variance in the data.
  • the projections can be either directly optimized to maximize the similarity between the given shape and the model or the model can be allowed to warp freely and can then be constrained by the requirements of the model that prevent the model from fitting (e.g., warping) into shapes that do not resemble a prostate.
  • one aspect includes obtaining an ultrasound image of a prostate of a patient and fitting a predetermined prostate shape model to that image.
  • Statistical data is then transferred from the prostate shape model to the ultrasound image such that one or more procedures may be performed on the prostate based on the statistical data.
  • a procedure may include obtaining at least one biopsy sample from a location of interest within the prostate and/or placing objects within the prostate.
  • Transferring data may include any method of overlaying statistical data onto the ultrasound image of the prostate.
  • overlaying of statistical data may include orienting regions and/or markers associated with statistical data within the three-dimensional ultrasound image.
  • information may be overlaid onto the surface of the three-dimensional image. It will be further recognized that such three-dimensional images may be sliced to provide two-dimensional images on which statistical information is present.
  • performing the procedure includes selecting one or more potentially cancerous regions for biopsy and obtaining a biopsy sample from the selected regions.
  • the method may include establishing one or more biomarkers on the prostate.
  • biomarkers may represent biopsy locations statistically associated with cancer.
  • the statistical data may include one or more regions that are associated with cancer.
  • a centroid of such regions may be associated with an optimal target location (e.g., biomarker) for obtaining a biopsy sample
  • information from the ultrasound image e.g., biomarker
  • information from the ultrasound image may be provided to a guidance instrument for use in guiding a biopsy needle to a location on and/or within the prostate.
  • transferring statistical data includes transferring prostate zone information to the ultrasound image.
  • the prostate may include various different zones, and statistical size averages associated with such zones may be overlaid onto an ultrasound image. Further statistical histological data associated with each zone may be provided. Accordingly, procedures, such as biopsy, may be performed zone by zone, for example, sequentially.
  • statistical data may be associated with specific patient data.
  • statistical data based on one or more demographic factors and/or PSA levels may be utilized to select statistical data that is more relevant to a particular patient.
  • various forms of cancers originate in different locations based on ethnicity and/or other factors.
  • improved biopsy may be provided.
  • Fig. 1 illustrates processes for generating a shape model, incorporating statistical information into the shape model and applying the shape model to a prostate image.
  • Fig. 2 illustrates obtaining a prostate image.
  • Figs. 3A and 3B illustrate 2D and 3D prostate images.
  • Fig. 4 illustrates a process for labeling ultrasound volumes.
  • Fig. 5 illustrates a process for generating a shape model.
  • Figs. 6A-6E illustrate application of a shape model and statistical data to an ultrasound image.
  • Fig. 7 illustrates various zones of prostate.
  • Figs 8A-C illustrate application of zone information to a prostate image.
  • Fig. 9 illustrates a zonal biopsy procedure.
  • the utilities use statistical information regarding various zones within a prostate where the cancer resides and/or probability maps of cancer locations obtained from an expert (histologist) based ground truth selection.
  • the utilities begin with identifying the prostate first within an ultrasound volume.
  • the identified prostate image e.g., segmented prostate
  • the mapping/fitting of the prostate image to the model is achieved in real time and statistical information may be applied to the prostate image such that the statistical information may be utilized for performing one or more procedures (e.g., biopsy, brachytherapy, etc.).
  • 3-D ultrasound images of multiple prostates are acquired 102 using, for example a TransRectal UltraSound (TRUS) system.
  • TRUS TransRectal UltraSound
  • the acquired images may then be converted to 3-D orthogonal voxel data (e.g., ultrasound volumes) having equal resolution in all three dimensions.
  • the images may be acquired in an appropriate manner.
  • Fig. 2 illustrates a transrectal ultrasound probe being utilized to obtain a plurality of two- dimensional ultrasound images of a prostate 12.
  • the probe 10 may be operative to automatically scan an area of interest.
  • a user may rotate the acquisition end 14 of the ultrasound probe 10 over an area of interest.
  • the probe 10 may acquire plurality of individual images while being rotated over the area of interest. See Figs. 3A-B.
  • Each of these individual images may be represented as a two-dimensional image. See Fig. 3 A. Initially, such images may be in a polar coordinate system. In such an instance, it may be beneficial for processing to translate these images into a rectangular coordinate system. In any case, the two- dimensional images may be combined to generate a 3-D image. See Fig. 3B. As will be appreciated, such a procedure may be performed on a plurality of patients to obtain a database of ultrasound volumes, which may be utilized to generate a shape model.
  • the ultrasound volumes may be utilized to train a shape model.
  • the segmented surfaces must be labeled. That is, corresponding structures within each segmented surface must be labeled to a common reference frame. This is referred to as a registration process 104. See Fig. 1.
  • each of the ultrasound volumes are marked by an expert (e.g., histologist) in feature rich regions or regions that distinguish the shape of the surface. The marked points are called landmarks, and they are correspondingly marked in all of the images of the training data/ultrasound volume database.
  • Fig. 4 illustrates a process utilized in the current embodiment where a non-linear registration method is utilized to align all the images to a common reference.
  • the ultrasound volume database is provided 402.
  • a reference image is selected 404 from the ultrasound database 402. This image may be randomly picked from the ultrasound volume database 402.
  • the remaining volumes (n-1) are all aligned 408 with the reference volume 406 associated with the selected image.
  • This alignment is non- linear and may result in a point wise correspondence from one surface to the other, resulting in labeled data 410. That is, all of the common landmarks of the ultrasound volumes are aligned with a chosen reference surface from the database.
  • the resulting aligned images or 'labeled correspondences' 106 may then be utilized to train the shape model 108.
  • the training images i.e., labeled data 502 reflect a variety of different geometrical prostate shapes. These different shapes must be taken into account in training the system.
  • an average shape is created from the training images in the form of a mean shape vector.
  • creating the average prostate shape involves labeling a set of feature points corresponding to prostate features/landmarks depicted in each training image in the training set of ultrasound volumes. The locations of the labeled feature points from a training images are used to form vector shapes. The average of all the vectors is then computed to produce a mean vector shape that represents the average prostate shape.
  • the ultrasound volumes associated with the labeled data 502 are then Procrustes aligned so as to remove variations in translation, rotation and scaling across the dataset in order to move them into a common frame of reference. Such alignment 504 results in rigidly aligned training volumes 506.
  • a mean shape may be computed 507 to generate a mean shape 508.
  • a principle component analysis (PCA) is performed 510 to identify Eigen values and Eigen vectors 512 that account for the variance in the set of images.
  • a top percentage of the Eigen Vectors are selected 514 that account for more than 95% variance of the entire set of images. Accordingly, the projections on the selected Eigen Vectors 516 can then be utilized to align the shape model (i.e., mean shape) to any other shape.
  • a mean shape and its principal mode of variation are defined 110 (See Fig. 1). These modes of variation can be utilized to fit the mean prostate shape to a prostate image acquired from a patient. Registration of the model to any shape resembling the training shape now becomes a straightforward mathematical process.
  • the projection can be either directly optimized to maximize the similarity between the given shape and the model or the model can be allowed to "warp" freely and may be constrained by requirements of the model that would prevent the model from warping into shapes that do not resemble a prostate.
  • Statistical information collection entails the collection of histology data 120, which are outlined and labeled 122. See Fig. 1.
  • prostate cancer locations are identified and mapped for a large group of patient data. These samples are collected and used to compile statistics on the presence of cancer locations.
  • Reference to the database of images whose cancer characteristics are fully known is referred to as ground truth data.
  • This ground truth data may be utilized to generate a look-up-table or LUT/map/atlas that indicates the probability of various regions of developing cancer.
  • These ground truth images may be generated from histological data including histological slices from actual prostates and/or histological data identified from individual (e.g., 2-D) prostate images.
  • the ground truth images are all mapped to a common anatomical frame and contain labels that mark every location of the prostate, i.e. whether cancer is present or not. Such labels may be selected by a histologist 124. Cancer probability maps/atlases are then computed from this data. These maps/atlases can be used for biopsy guidance.
  • the specificity of the map/atlas may be further improved by normalizing subgroups of the data separately based on age, ethnicity, PSA levels and/or other demographic factors. In this regard, statistical information may be based on one or more demographic parameters.
  • cancer probability maps/atlases are computed from histological data which may include actual prostates that have been removed from cancer patients as well as from images of cancerous prostates (e.g., samples). The cancer in the samples may be mapped by a histologist who reviews the sample identifies the location of cancer therein.
  • a database may be generated from a plurality of such prostates to identify which regions of the prostates are likely to identify which regions of the prostates are likely to have cancer (e.g., based on one or more demographics), as well as to identify the exact location of such cancer.
  • Data from separate prostates is labeled to a common reference frame such that the data may be incorporated into a map/atlas that may be utilized to identify areas within a prostate for a given patient.
  • Such labeling may include selecting a volume as a common volume of reference for a set of image volumes. Each of the remaining volumes may be registered to the chosen common volume of reference so as to create an atlas.
  • prostate regions that contain cancer may be identified. For instance, if a plurality of the histological samples of different prostates include cancer in a common region, a centroid of that region may be identified.
  • the centroid may be a common point or biomarker of all the map/atlas coordinates and may represent an optimal target position for biopsy to identify cancer within that region of the prostate. That is, the centroid/biomarker may identify an optimal position for sampling for a patient having demographic information and/or PSA levels that match those of a given map/atlas.
  • a map/atlas may be aligned 128 with the mean shape of the shape model discussed above. That is, statistical information of the map/atlas (e.g., regions of increased probability of cancer) may be incorporated into the shape model.
  • This shape model and corresponding statistical information 130 may then be fit to an image of a prostate of a patient in an online procedure. Accordingly, statistical information associated with the regions having a high incidence of cancer may be overlaid onto the surface of the image of the prostate of the patient. Accordingly, these regions may be targeted for biopsy.
  • the online portion of the utility involves acquiring an image 140 for a new patient. This may be performed as illustrated in Fig. 2 where a side fire or end fire TRUS probe 10 is utilized to acquire a patient prostate image. However, it will be appreciated that other probes may be may be utilized as well.
  • the probe 10 may also includes a biopsy gun (not shown) that may be attached to the probe. Such a biopsy gun may include a spring driven needle that is operative to obtain a core from desired area within the prostate. In this regard, it may be desirable to generate an image of the prostate 12 while the probe 10 remains positioned relative to the prostate as well as identify target areas for sampling.
  • the ultrasound image may be segmented 142 to identify the surface of the 3-D volume/capsule 144 and/or the boundaries of individual 2- D images. Such segmentation may be performed in any known manner.
  • One such segmentation method is provided in co-pending U.S. Patent Application No. 11/615,596, entitled "Object Recognition System for Medical Imaging” filed on December 22, 2006, the contents of which are incorporated by reference herein.
  • the segmented image is then provided for combination with the shape model 146 in order to align the map/atlas information with the acquired image. Biopsy locations may then be identified 148.
  • Figures 6A-6E graphically illustrate the overall process. Though illustrated as 2D figures, it will be appreciated that the shape model, prostate image, statistical regions and biomarkers discussed herein may be three dimensional. Accordingly, the statistical information and biomarkers may be displayed on and/or within the prostate image.
  • the shape model 202 is provided 6A.
  • Statistical information 200 e.g., ground truth data
  • a current patient e.g., based on demographics, PSA etc
  • a completely defined geometrical deformation shape model 204 including statistical information is provided.
  • the deformation shape model 204 may be based on a set of Eigen vectors that allow the model 204 to only be fitted in ways allowed by predetermined limitations of the model.
  • the model may then be applied (e.g., fit) to an acquired ultrasound prostate image 206.
  • the result of this fitting procedure is also the transfer of statistical information to the prostate image 206 of the patient. That is, the statistical information may be applied to the prostate image 206 of the patient to provide a combined image with statistical data 208.
  • Fig. 6D A urologist may utilize the combined image 208 to identify regions on the prostate of the current patient that have, for example, higher likelihood of cancer. Accordingly, the urologist may target such regions for biopsy.
  • the identification of target locations may allow for use of a positioning system to obtain biopsies from those locations.
  • a urologist may use 3-D cancer distribution and/or biomarkers for needle positioning during biopsy. That is, the statistical information applied to the prostate may be reduced into a biomarker framework to generate the cancer biopsy spots as surrogate biomarkers for biopsy. See Fig. 6E.
  • the concept comes from knowledge of the centroid/central core of the cancer for a 3D cancer region. Since the 3D model will have different 3D regions, one can reduce the 3D regions into 3D centralized spots as biomarkers for biopsy. The reduction of the 3D cancer regions into biomarkers is illustrated in Figure 6E.
  • the biomarkers 210 are displayed on the on the combined image 208 that provide a target location for biopsy.
  • the location of these biomarkers as applied to the prostate image may be output from, for example the imaging device to a positioning device.
  • the positioning device may then guide a biopsy device (e.g., needle) to the biomarker.
  • a biopsy device e.g., needle
  • Such a biopsy device may be incorporated onto, for example, a TRUS ultrasound.
  • the prostate is formed of three zones including a peripheral zone, a central zone and a transition zone. See Fig. 7. Since cancer is most often found in the peripheral zone of the prostate, followed by transition and central zones, biopsy can be performed starting in order of the likelihood of cancer. That is, zonal targeting of biopsy sites can be used to improve the specificity of diagnosis and/or treatment.
  • the shape model discussed above may also include zonal information.
  • data associated with the transitional zone, central zone and/or peripheral zones of multiple prostates may be incorporated into the shape model such that such information may be applied to the prostate image.
  • the transition zone 302, central zone 304 and peripheral zone 306 may be incorporated into a shape model 300.
  • the shape model may also include statistical information as discussed above.
  • an ultrasound image of a prostate 206 of a patient may be acquired. See Fig. 8B.
  • the shape model 300, including the zone information may be overlaid onto the prostate 206.
  • the resulting three-dimensional image may be segmented in order to generate views in which the different zones within the prostate are visible. See Fig. 8C.
  • the combined view 308 may then be utilized to identify areas within specific zones for biopsy purposes.
  • the use of such zones may allow for sequential identification of target locations.
  • the zones may be identified sequentially within the patient's prostate. Further, these zones may be selected in the order of importance.
  • three-dimensional locations within a zone may be ascertained through use of an atlas/map containing statistical information regarding that zonal area. Accordingly, regions of interest within the zone and/or biomarkers may be generated for the zone and may identify one or more points of maximum likelihood for cancer based on the map/atlas. Accordingly, a biopsy of this location may be performed.
  • Fig. 9 illustrates an overall process for performing a zonal biopsy.
  • the method starts with the obtaining 600 an ultrasound image of a prostate of a patient.
  • the patient's information is entered (PSA, age, ethnicity and/or others) and an atlas or look-up table specific to these statistics is retrieved 602 from a database.
  • the patient's ultrasound image is acquired, interpolated and reconstructed into 3D.
  • the reconstructed image may then be segmented by any of several segmentation techniques to get a surface outline of the ultrasound prostate.
  • the patient's surface is then aligned with the zones one at a time. In this regard, zones for sampling are selected 604. For each zone selected 606, statistical information from the map/atlas are utilized to identify 608 optimal location for biopsy based on the retrieved statistical data for that zone.
  • a biopsy needle may be guided to the location to obtain a biopsy sample 610. If another zone is selected 612, a 3D atlas for that zone may be obtained 614 and utilized to identify 616 one or more target locations for the current zone. This may be repeated until all selected zones in the prostate are sampled.
  • the disclosed processes alone or in combination, also provide one or more of the following advantages.
  • the maps/atlases include all information necessary to guide a biopsy planning process. Further, as the maps/atlases are prepared offline prior to patient visits, this allows the statistical data of the maps/atlases to be quickly selected (e.g., based on demographics, etc.) and applied to an acquired image. Further, as a result of matching the map/atlas to a patient based on patient specific information, the probability of identifying cancerous cells in improved. Further, the utility may allow for the comparison of images a prostate of a patient where the images are acquired at separate times.
  • the utility may allow for the registration of temporally distinct images together. This may allow, for example, comparison of the overall size of the prostate to identify changes. Further, this may allow for identifying previous biopsy locations, obtaining biopsies form previous locations and/or utilizing old biopsy locations to permit sampling of previously un- sampled regions.
  • cancerous regions derived from the histology data may be reduced to 3-D target locations by computing the center of the originating cancers. These biomarkers may accurately represent changes during which a cancer has evolved or spread over a 3-D region. Further, the computation of biomarkers is an offline process and it does not affect the workflow of urologists for biopsy. Another advantage of having the biomarker strategy is that it avoids the occlusion of the prostate image during biopsy.
  • sextant biopsy can miss 30% of cancers and other biopsy methods have randomly obtained biopsy samples from all zones of the prostate. Since a majority of cancers are found in the peripheral zone of the prostate, following a zonal concept of biopsy sampling can be very efficient. That is, zones having higher likelihood of cancer may provide a majority or all biopsy samples. Further, combining zonal biopsy with biomarkers provides the added advantage of finding target locations accurately and also improves the efficiency of a biopsy process. That is, the areas (zones) targeted for biopsy sampling may be reduced based on patient specific information and locations within the zones may be limited to those identified as having high probability of cancer.

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Abstract

L'invention concerne un système et un procédé (à savoir un utilitaire) pour positionner une aiguille en trois dimensions sur la base de statistiques associées au patient permettant d'extraire un tissu pendant des procédures de biopsie de la prostate. Les aspects de l'utilitaire peuvent s'appliquer indépendamment ou servent d'assistance à l'urologue lorsque les régions intéressantes sont difficiles à discerner dans une image à ultrasons. Les régions intéressantes (200) correspondant aux régions à haut risque pour le cancer (par exemple, de manière statistique) sont superposées automatiquement sur une image à ultrasons d'une prostate (206) en temps réel. En outre une carte statistique sur la base d'un ou plusieurs paramètres démographiques d'un patient et contenant des emplacements de probabilité d'occurrence du cancer peut également être cartographiée automatiquement sur l'image à ultrasons (208) en temps réel pour afficher des emplacements de cancer potentiels.
PCT/US2007/069275 2006-05-18 2007-05-18 systÈme amÉliorÉ et procÉdÉ pour biopsie 3D WO2007137179A2 (fr)

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Cited By (4)

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US10716544B2 (en) 2015-10-08 2020-07-21 Zmk Medical Technologies Inc. System for 3D multi-parametric ultrasound imaging
US10751034B2 (en) 2016-10-17 2020-08-25 The Johna Hopkins University Geometric biopsy plan optimization
JP2021528158A (ja) * 2018-06-29 2021-10-21 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 生検予測及び超音波撮像によるガイド並びに関連するデバイス、システム、及び方法

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US8447384B2 (en) 2008-06-20 2013-05-21 Koninklijke Philips Electronics N.V. Method and system for performing biopsies
US10716544B2 (en) 2015-10-08 2020-07-21 Zmk Medical Technologies Inc. System for 3D multi-parametric ultrasound imaging
US10751034B2 (en) 2016-10-17 2020-08-25 The Johna Hopkins University Geometric biopsy plan optimization
JP2021528158A (ja) * 2018-06-29 2021-10-21 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 生検予測及び超音波撮像によるガイド並びに関連するデバイス、システム、及び方法
JP7357015B2 (ja) 2018-06-29 2023-10-05 コーニンクレッカ フィリップス エヌ ヴェ 生検予測及び超音波撮像によるガイド並びに関連するデバイス、システム、及び方法

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