公开(公告)号：US20180098700A1

公开(公告)日：2018-04-12

申请号：US15291959

申请日：2016-10-12

Applicant: Mint Labs Inc.

Inventor： Vesna Prchkovska ,  Paulo Reis Rodrigues ,  Marc Ramos Bruach ,  Pablo Villoslada ,  Álvaro Abella de Bascarán

IPC: A61B5/00 ,  G06F19/00 ,  A61B5/055

CPC classification number: A61B5/0042 ,  A61B5/055 ,  A61B5/4064 ,  A61B5/4082 ,  A61B2576/026 ,  G01R33/4806 ,  G01R33/50 ,  G01R33/56341 ,  G06F19/321 ,  G06T7/0012 ,  G06T2207/10084 ,  G06T2207/10092 ,  G06T2207/30016 ,  G16H50/20

Abstract: The present disclosure discusses systems and methods for identifying biomarkers that can help with the diagnosis, prognosis, and treatment choices of patients with neurodegenerative diseases. Diffusion based magnetic resonance imaging can often fail for patients with a neurodegenerative disease because parameters fractional anisotropy, mean diffusivity, and radial diffusivity are based on simple models that can fail in the presence of neurodegeneration, such as demyelination. The present disclosure discusses systems and methods that enhance dMRI images and enable tractography to be performed on images of a damaged nervous system. The damaged tracks identified by the present system can be used as a biomarker for the assessment of patients. In some implementations, the biomarkers are converted into clinical scales that can be used to compare patients to one another or over time.

公开(公告)号：US09916648B1

公开(公告)日：2018-03-13

申请号：US15403128

申请日：2017-01-10

Applicant: NATIONAL TAIWAN UNIVERSITY

Inventor： Wen-Yih Isaac Tseng ,  Chien-Feng Huang

IPC: G06K9/00 ,  G06T5/50 ,  G06T5/00 ,  G06T5/40 ,  G06T7/20 ,  G01R33/563 ,  G01R33/565

CPC classification number: G06T5/50 ,  G01R33/5608 ,  G01R33/56341 ,  G01R33/56509 ,  G06T5/005 ,  G06T2207/10092 ,  G06T2207/30016

Abstract: The present invention is related to a method for correcting a diffusion image having an artifact, the method comprising: (a) providing a set of diffusion images comprising the diffusion image having the artifact; (b) calculating a first signal intensity of each image in the set of diffusion images; (c) plotting a graph of serial number of slice of the set of diffusion images versus the first signal intensity; (d) calculating a second signal intensity of the diffusion image having the artifact by performing interpolation on the graph; and (e) correcting the diffusion image having the artifact base on the second signal intensity.

公开(公告)号：US20180055408A1

公开(公告)日：2018-03-01

申请号：US15691119

申请日：2017-08-30

Applicant: Washington University

Inventor： Sheng-Kwei Song ,  Zezhong Ye ,  William Spees ,  Tsen-Hsuan Lin

IPC: A61B5/055 ,  G01R33/563 ,  G01R33/56 ,  A61B5/00 ,  A61B10/02 ,  A61B5/20 ,  A61B10/00 ,  G06T7/00

CPC classification number: A61B5/055 ,  A61B5/0042 ,  A61B5/0044 ,  A61B5/201 ,  A61B5/202 ,  A61B5/4041 ,  A61B5/425 ,  A61B5/4312 ,  A61B5/4331 ,  A61B5/4381 ,  A61B5/7267 ,  A61B10/0041 ,  A61B10/0241 ,  A61B2010/045 ,  A61B2576/023 ,  A61B2576/026 ,  G01R33/5602 ,  G01R33/5608 ,  G01R33/56341 ,  G06K9/00127 ,  G06K9/00147 ,  G06K9/00201 ,  G06K9/6256 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/10092 ,  G06T2207/20081 ,  G06T2207/30016 ,  G06T2207/30024 ,  G06T2207/30048 ,  G06T2207/30081 ,  G06T2207/30096

Abstract: D-Histo, a non-invasive diagnostic method, renovated from diffusion basis spectrum imaging (DBSI) is provided for quantitatively detecting and distinguishing inflammation from solid tumors, heart and nerve injury. For example, the D-Histo methods disclosed herein provide an accurate diagnosis of prostate cancer, distinguishing it from prostatitis and BPH that missed by currently available methods of diagnosing prostate cancer (multiparameter MRI, needle biopsy). The disclosed D-Histo method also provides metrics to reflect reversible vs. irreversible damages in heart and central/peripheral nerves. For central and peripheral nerves, D-Histo also provides metrics to assess nerve functionality. The at least one D-Histo biomarker obtained using diffusion weighted MRI has excellent test-retest stability, high sensitivity to disease progression and close correlation with currently available techniques.

公开(公告)号：US20180018790A1

公开(公告)日：2018-01-18

申请号：US15508794

申请日：2014-09-16

Applicant: Brainlab AG

Inventor： Rainer LACHNER ,  Balint VARKUTI

IPC: A61B5/00 ,  A61B5/055 ,  G06T7/60 ,  G01R33/48

CPC classification number: G06T7/75 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/4064 ,  A61B2576/026 ,  G01R33/4806 ,  G06T7/0014 ,  G06T7/11 ,  G06T7/143 ,  G06T7/149 ,  G06T7/251 ,  G06T7/60 ,  G06T2207/10092 ,  G06T2207/20076 ,  G06T2207/20128 ,  G06T2207/20156 ,  G06T2207/20221 ,  G06T2207/30016 ,  G06T2207/30024 ,  G06T2207/30241 ,  G06T2210/41

Abstract: 1. A medical data processing method of determining information describing the probable position of a neural fibre in a patient's brain, the method comprising the following steps which are constituted to be executed by a computer: a) acquiring patient-specific medical image data describing the brain of the patient; b) acquiring atlas data defining an image-based model of a human brain; c) determining, based on the patient-specific medical image data and the atlas data, seed region data describing seed regions (A, B) in the patient-specific medical image data in which the ends of neural fibres of the patient's brain may be located; d) determining, based on the patient-specific medical image data and the seed region data, neural fibre tract data describing a plurality of potential tracts (T1, T2, T3) which a specific neural fibre may take through the patient's brain; e) determining, based on the atlas data and the neural fibre tract data, a figure of merit for each one of the potential tracts (T1, T2, T3).

公开(公告)号：US20170363702A1

公开(公告)日：2017-12-21

申请号：US15618979

申请日：2017-06-09

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Xi PENG ,  Dong LIANG ,  Xin LIU ,  Hairong ZHENG

IPC: G01R33/563 ,  G01R33/56

CPC classification number: G01R33/56341 ,  A61B5/055 ,  A61B5/7203 ,  G01R33/5602 ,  G06T5/002 ,  G06T2207/10092

Abstract: The application provides a method, apparatus and computer program product for denoising a magnetic resonance diffusion tensor, wherein the method comprises: collecting data of K space; calculating a maximum likelihood estimator of a diffusion tensor according to the collected data of K space; calculating a maximum posterior probability estimator of the diffusion tensor by using sparsity of the diffusion tensor and sparsity of a diffusion parameter and taking the calculating maximum likelihood estimator as an initial value; and calculating the diffusion parameter according to the calculated maximum posterior probability estimator. The application solves the technical problem in the prior art of how to realize high precision denoising of diffusion tensor while not increasing scanning time and affecting spatial resolution, achieves the technical effects of effectively suppressing noises in the diffusion tensor and improving the estimation accuracy of the diffusion tensor.

公开(公告)号：US09741114B2

公开(公告)日：2017-08-22

申请号：US15034077

申请日：2013-11-05

Applicant: Brainlab AG

Inventor： Bálint Varkuti

IPC: G06K9/00 ,  G06T7/00 ,  G06T17/00 ,  A61B6/03 ,  A61B5/055 ,  A61B5/00 ,  A61B8/08 ,  G06F17/16 ,  A61B5/04 ,  G06F19/00 ,  A61B6/00

CPC classification number: G06T7/0012 ,  A61B5/055 ,  A61B5/4064 ,  A61B6/032 ,  A61B6/037 ,  A61B6/481 ,  A61B6/501 ,  A61B8/0808 ,  A61B8/488 ,  G06F17/16 ,  G06F19/00 ,  G06F19/321 ,  G06T17/005 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/10092 ,  G06T2207/10104 ,  G06T2207/30016 ,  G06T2207/30241 ,  G06T2210/41 ,  G16H50/50

Abstract: The invention relates to a medical data processing method for determining a vulnerability field of a brain of a patient, the steps of the method being constituted to be executed by a computer and comprising: a) acquiring a nerve-indicating dataset comprising information about the brain of the patient suitable for identifying neural fibers in the brain of the patient; b) determining nodes within the brain preferably being neuron-rich grey matter parts of the brain; c) determining the axonal linkage of the nodes based on the nerve-indicating dataset to obtain edges connecting the nodes, the nodes and edges constituting a connectivity graph; d) determining a weight for each of the edges depending on centrality graph theoretical statistical measure of the respective edge in the connectivity graph; e) determining, for each of the edges, which voxels in a dataset of the brain of the patient belong to the edges or are passed by the edges and assigning or adding the determined weight of the respective edges to all of the voxels belonging to the respective edge to obtain a weighted voxel-based dataset of the brain of the patient defining the vulnerability field of the brain.

公开(公告)号：US20160284080A1

公开(公告)日：2016-09-29

申请号：US14671008

申请日：2015-03-27

Applicant: Sabanci University

Inventor： Gozde Unal ,  Suheyla Cetin

IPC: G06T7/00 ,  G06T17/20 ,  G06T5/00 ,  A61B5/02

CPC classification number: G06T17/20 ,  A61B5/02007 ,  G06T7/13 ,  G06T7/162 ,  G06T2207/10092 ,  G06T2207/30101 ,  G06T2207/30172

Abstract: The present invention relates to modeling of vascular structures, and in particular to extracting a vessel tree from medical-images of vascular anatomy. A respective method comprises among others the steps of providing an image of at least one vessel, obtaining multiple measurements from the image for a first point of the image, and fitting a four-dimensional tensor to the measurements. Based on said four-dimensional tensor fitted to the measurements, a vessel direction in a vessel tree is determined and a model of the vascular structures is generated based on at least the determined vessel direction.

Abstract translation: 本发明涉及血管结构的建模，特别涉及从血管解剖学的医学图像中提取血管树。 相应的方法包括以下步骤：提供至少一个容器的图像，从图像中获取图像的第一点的多个测量值，以及将四维张量拟合到测量中。 基于装配到测量的所述四维张量，确定容器树中的血管方向，并且基于至少所确定的血管方向产生血管结构的模型。

公开(公告)号：US20160253825A1

公开(公告)日：2016-09-01

申请号：US15032938

申请日：2014-10-22

Applicant: Joshua D. TRZASKO ,  Armando MANDUCA

Inventor： Joshua D. Trzasko ,  Armando Manduca

IPC: G06T11/00 ,  A61B5/00 ,  G06T7/00

CPC classification number: G06T11/003 ,  A61B5/743 ,  G01R33/5608 ,  G06T7/0012 ,  G06T7/0016 ,  G06T2207/10076 ,  G06T2207/10088 ,  G06T2207/10092

Abstract: A system and method for estimating a physiological parameter from data acquired with a medical imaging system includes acquiring data with the medical imaging system. A physiological parameter is estimated from the acquired data using an iterative estimation in which a model of the medical imaging system is decoupled from a physics-based model of the acquired data.

Abstract translation: 从用医学成像系统获取的数据估计生理参数的系统和方法包括用医学成像系统获取数据。 使用迭代估计从所获取的数据估计生理参数，其中医学成像系统的模型与所获取的数据的基于物理的模型解耦。

公开(公告)号：US09402562B2

公开(公告)日：2016-08-02

申请号：US13635575

申请日：2011-03-25

Applicant: Van J. Weeden

Inventor： Van J. Weeden

IPC: G06T9/00 ,  A61B5/055 ,  G06T7/00

CPC classification number: A61B5/055 ,  G06T7/12 ,  G06T7/73 ,  G06T2207/10092 ,  G06T2207/30016

Abstract: A coordinate system particular to a subject's tissue, such as a subject's brain, is provided. Furthermore, a system and method for multi-dimensional, interrelated tractography is provided. Images of the subject are acquired that include diffusion information and tracts and/or vectors potentially associated with tracts are determined therefrom. With respect to the coordinate system, this information is used along with an basis that the tracts and/or vectors generally conform to a substantially orthogonal grid, such that white matter tissue fibers are arranged as one of substantially parallel or substantially orthogonal to other fibers. This coordinate system may be provided to a user along with reconstructed images, or may be used to process images. Similarly, in multi-dimensional, interrelated tractography, a new predictive ability and new metrics are provided along with an improved ability to reconstructed or process images.

Abstract translation: 提供特定于受试者的组织的坐标系统，例如受试者的大脑。 此外，提供了一种用于多维，相互关联的造影的系统和方法。 获取包含扩散信息的对象的图像，并且由其确定潜在地与束相关的束和/或矢量。 关于坐标系，该信息与基于这样的基础一起使用，其中，束和/或矢量通常符合基本上正交的网格，使得白质组织纤维被布置为基本上平行或基本上与其它纤维正交的一个。 该坐标系可以与重建图像一起提供给用户，或者可以用于处理图像。 类似地，在多维，相互关联的动态成像中，提供了新的预测能力和新的指标以及改进的重建或处理图像的能力。

公开(公告)号：US20160133037A1

公开(公告)日：2016-05-12

申请号：US14871138

申请日：2015-09-30

Applicant: Siemens Healthcare GmbH

Inventor： Raviteja Vemulapalli ,  Hien Nguyen ,  Shaohua Kevin Zhou

IPC: G06T11/60 ,  A61B5/055 ,  G06K9/62 ,  G06K9/52 ,  G06T7/00 ,  G06K9/46

CPC classification number: G06T11/60 ,  A61B5/0035 ,  A61B5/0042 ,  A61B5/055 ,  A61B5/7425 ,  A61B2576/026 ,  G01R33/50 ,  G01R33/5602 ,  G01R33/5608 ,  G06K9/6244 ,  G06K9/6289 ,  G06K2209/05 ,  G06T5/50 ,  G06T7/0012 ,  G06T11/008 ,  G06T2200/04 ,  G06T2200/32 ,  G06T2207/10081 ,  G06T2207/10092 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20212 ,  G06T2207/30016

Abstract: A method and apparatus for unsupervised cross-modal medical image synthesis is disclosed, which synthesizes a target modality medical image based on a source modality medical image without the need for paired source and target modality training data. A source modality medical image is received. Multiple candidate target modality intensity values are generated for each of a plurality of voxels of a target modality medical image based on corresponding voxels in the source modality medical image. A synthesized target modality medical image is generated by selecting, jointly for all of the plurality of voxels in the target modality medical image, intensity values from the multiple candidate target modality intensity values generated for each of the plurality of voxels. The synthesized target modality medical image can be refined using coupled sparse representation.

Abstract translation: 公开了一种用于无监督的跨模态医学图像合成的方法和装置，其基于源模态医学图像来合成目标模态医学图像，而不需要配对的源和目标模态训练数据。 接收源模态医学图像。 基于源模态医学图像中的相应体素，为目标模态医学图像的多个体素中的每一个生成多个候选目标模态强度值。 通过针对目标模态医学图像中的所有多个体素共同选择针对多个体素中的每一个生成的多个候选目标模态强度值的强度值来生成合成目标模态医学图像。 可以使用耦合稀疏表示来改进合成的目标模态医学图像。