System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

A method of imaging an implant device in a computing device is provided. The computing device includes a processor interconnected with a memory and a display. The method includes, at the processor: obtaining a first magnetic resonance (MR) image of a patient tissue, the first MR image containing a first magnetic field strength indicator; responsive to the implant device being inserted in the patient tissue, obtaining a second MR image of the patient tissue, the second MR image containing a second magnetic field strength indicator smaller than the first magnetic field strength indicator; registering the second MR image with the first MR image; generating a composite image from the first MR image and the second MR image; and presenting the composite image on the display.

A method of imaging an implant device in a computing device is provided. The computing device includes a processor interconnected with a memory and a display. The method includes, at the processor: obtaining a first magnetic resonance (MR) image of a patient tissue, the first MR image containing a first magnetic field strength indicator; responsive to the implant device being inserted in the patient tissue, obtaining a second MR image of the patient tissue, the second MR image containing a second magnetic field strength indicator smaller than the first magnetic field strength indicator; registering the second MR image with the first MR image; generating a composite image from the first MR image and the second MR image; and presenting the composite image on the display.

A magnetic resonant imaging (MRI) review workstation includes a control processor, and a display integrated or otherwise operatively coupled with the control processor, wherein the control processor is configured to receive and analyze magnetic resonant imaging information pertaining to an imaged volume of tissue, and to cause to be displayed on the display output information that reflects or is otherwise indicative of an absorption rate of a contrast agent in the volume of tissue.

A magnetic resonant imaging (MRI) review workstation includes a control processor, and a display integrated or otherwise operatively coupled with the control processor, wherein the control processor is configured to receive and analyze magnetic resonant imaging information pertaining to an imaged volume of tissue, and to cause to be displayed on the display output information that reflects or is otherwise indicative of an absorption rate of a contrast agent in the volume of tissue.

Scan time in diffusion-relaxation magnetic resonance imaging (“MRI”) is reduced by implementing time-division multiplexing (TDM). In general, time-shifted radio frequency (“RF”) pulses are used to excite two or more imaging volumes. These RF pulses are applied to induce separate echoes for each slice. Diffusion MRI data can thus be acquired with different echo times, or alternatively with the same echo time, in significantly reduced overall scan time. Multidimensional correlations between diffusion and relaxation parameters can be estimated from the resulting data.

Scan time in diffusion-relaxation magnetic resonance imaging (“MRI”) is reduced by implementing time-division multiplexing (TDM). In general, time-shifted radio frequency (“RF”) pulses are used to excite two or more imaging volumes. These RF pulses are applied to induce separate echoes for each slice. Diffusion MRI data can thus be acquired with different echo times, or alternatively with the same echo time, in significantly reduced overall scan time. Multidimensional correlations between diffusion and relaxation parameters can be estimated from the resulting data.

A method and a system for automated plant surveillance and manipulation are provided. Pursuant to the method and the system, images of target plants are obtained through a machine vision system having multiple cameras. The obtained images of the target plants are processed to determine tissue candidates of the target plants and to determine a position and an orientation of each tissue candidate. A tool is manipulated, based on the position and the orientation of each tissue candidate, to excise each tissue candidate to obtain tissue samples. The tissue samples are transported for subsequently manipulation including live processing of the tissue samples or destructive processing of the tissue samples.

A method for performing magnetic resonance imaging on a subject comprises: injecting a contrast agent into a region of interest of the subject; applying a pulse sequence to the region of interest; collecting auxiliary data for the region of interest, the auxiliary data being related to one or more time-varying parameters of the subject within the region of interest; determining a temporal factor Φ from the auxiliary data; collecting imaging data for the region of interest, the imaging data being related to one or more spatially-varying parameters of the subject within the region of interest; determining a spatial factor Ur from the imaging data; modeling a multi-dimensional image sequence as I=UrΦ; and deriving at least a first metric and a second metric from the multi-dimensional image sequence I, the first metric and the second metric being associated with distinct perfusion-based imaging techniques.

A method for performing magnetic resonance imaging on a subject comprises: injecting a contrast agent into a region of interest of the subject; applying a pulse sequence to the region of interest; collecting auxiliary data for the region of interest, the auxiliary data being related to one or more time-varying parameters of the subject within the region of interest; determining a temporal factor Φ from the auxiliary data; collecting imaging data for the region of interest, the imaging data being related to one or more spatially-varying parameters of the subject within the region of interest; determining a spatial factor Ur from the imaging data; modeling a multi-dimensional image sequence as I=UrΦ; and deriving at least a first metric and a second metric from the multi-dimensional image sequence I, the first metric and the second metric being associated with distinct perfusion-based imaging techniques.