Abstract: For a given MR image having an anatomical landmark and a frame of reference, a method includes automatically selecting one or more series of MR images from a plurality of series of MR images previously acquired and having the anatomical landmark and the frame of reference. The method includes automatically selecting one or more candidate groups from the one or more series of MR images that were selected, wherein selection of the one or more candidate groups is based on predetermined rules. The method includes automatically selecting a respective reference image from each candidate group, wherein selection of each respective reference image is based on the predetermined rules. The method further includes automatically displaying the respective reference image selected for each candidate group, wherein each respective reference image is displayed in reference viewports on a user interface on a display based on the predetermined rules.

Abstract: A computer-implemented method for estimating cartilage includes pre-processing, via a processor, a segmented image of region of interests (ROIs) of a subject to separate the ROIs into individual ROI volumes, wherein the ROI volumes comprise at least one cartilage region. The computer-implemented method also includes performing, via the processor, surface separation between a respective subchondral surface and a respective articular surface for each ROI volume to extract a respective separate subchondral surface and a respective separate articular surface for each ROI volume. The computer-implemented method further includes estimating, via the processor, cartilage statistics for the at least one cartilage region utilizing a nearest neighbor algorithm based on the respective separate subchondral surface and the respective separate articular surface for each ROI volume.

Abstract: Method (1000) and system (100) for image processing for a medical device is provided. The method (1000) includes acquiring (1010) a plurality of images of a subject using an image acquisition system (110) of the medical device. The method (1000) further includes identifying and differentiating (1020) a plurality of background pixels and a plurality of foreground pixels in the images using the deep learning module (125). The method (1000) further includes suppressing (1030) the identified background pixels using a mask and processing the foreground pixels for subsequent reconstruction and/or visualization tasks.

Abstract: A computer-implemented method for estimating cartilage includes pre-processing, via a processor, a segmented image of region of interests (ROIs) of a subject to separate the ROIs into individual ROI volumes, wherein the ROI volumes comprise at least one cartilage region. The computer-implemented method also includes performing, via the processor, surface separation between a respective subchondral surface and a respective articular surface for each ROI volume to extract a respective separate subchondral surface and a respective separate articular surface for each ROI volume. The computer-implemented method further includes estimating, via the processor, cartilage statistics for the at least one cartilage region utilizing a nearest neighbor algorithm based on the respective separate subchondral surface and the respective separate articular surface for each ROI volume.

Abstract: Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, determining at least one spine label seed point on a non-functional image volume, automatically labeling the non-functional image volume with a plurality of spine labels based on the at least one spine label seed point, automatically correcting the geometric misalignments and registering the functional image volume, adjusting the plurality of spine labels and propagating the adjusted spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

Abstract: An automatic field-of-view (FOV) prescription system is provided. The system includes an FOV prescription computing device that includes at least one processor electrically coupled to at least one memory device. The at least one processor is programmed to receive localizer images that depict an anatomy, and generate masks associated with the localizer images, wherein the masks represent part of the localizer images that depict the anatomy. The at least one processor is also programmed to calculate bounding boxes surrounding the anatomy based on the masks, generate an FOV based on the bounding boxes, and output the FOV.

Abstract: Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, determining at least one spine label seed point on a non-functional image volume, automatically labeling the non-functional image volume with a plurality of spine labels based on the at least one spine label seed point, automatically correcting the geometric misalignments and registering the functional image volume, adjusting the plurality of spine labels and propagating the adjusted spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

Abstract: Method (1000) and system (100) for image processing for a medical device is provided. The method (1000) includes acquiring (1010) a plurality of images of a subject using an image acquisition system (110) of the medical device. The method (1000) further includes identifying and differentiating (1020) a plurality of background pixels and a plurality of foreground pixels in the images using the deep learning module (125). The method (1000) further includes suppressing (1030) the identified background pixels using a mask and processing the foreground pixels for subsequent reconstruction and/or visualization tasks.

Abstract: An automatic field-of-view (FOV) prescription system is provided. The system includes an FOV prescription computing device that includes at least one processor electrically coupled to at least one memory device. The at least one processor is programmed to receive localizer images that depict an anatomy, and generate masks associated with the localizer images, wherein the masks represent part of the localizer images that depict the anatomy. The at least one processor is also programmed to calculate bounding boxes surrounding the anatomy based on the masks, generate an FOV based on the bounding boxes, and output the FOV.

Abstract: Methods and systems are provided for reducing background noise in magnetic resonance (MR) image and parametric map visualization using segmentation and intensity thresholding. An example method includes segmenting the MR image or parametric map into foreground which includes a region of anatomy of interest and background which is outside of the region of anatomy of interest, applying an intensity threshold to the background and not applying the intensity threshold to the foreground of the MR image or parametric map to produce a noise reduced MR image or noise reduced parametric map, and displaying the noise reduced MR image or noise reduced parametric map via a display device.

Abstract: Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, automatically labeling the non-functional image volume with spine labels, automatically correcting the geometric misalignments and registering the functional image volume, and propagating the spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

Abstract: A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.

Abstract: Methods and systems are provided for reducing background noise in magnetic resonance (MR) image and parametric map visualization using segmentation and intensity thresholding. An example method includes segmenting the MR image or parametric map into foreground which includes a region of anatomy of interest and background which is outside of the region of anatomy of interest, applying an intensity threshold to the background and not applying the intensity threshold to the foreground of the MR image or parametric map to produce a noise reduced MR image or noise reduced parametric map, and displaying the noise reduced MR image or noise reduced parametric map via a display device.

Abstract: Methods and systems are provided for whole-body spine labeling. In one embodiment, a method comprises acquiring a non-functional image volume of a spine, acquiring a functional image volume of the spine, automatically labeling the non-functional image volume with spine labels, automatically correcting the geometric misalignments and registering the functional image volume, and propagating the spine labels to the functional image volume. In this way, the anatomical details of non-functional imaging volumes may be leveraged to improve clinical diagnoses based on functional imaging, such as diffusion weighted imaging (DWI).

Abstract: A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.