Provided herein are methods and systems for high-resolution, cerebrospinal fluid-suppressed T2*-weighted magnetic resonance imaging of cortical lesions.

A method and system for controlling neural activity in the brain, including performing a source localization procedure and a neurostimulation procedure, and using the former as a monitor to provide for feedback control of the latter.

The present invention relates to compensating for brain shift in catheter trajectory planning. First brain shift information is determined from an initial brain image dataset, an initial planning dataset, a patient orientation dataset, and first burr hole dataset. The brain image dataset is updated based on the first brain shift information and a trajectory of a first catheter is updated based on the updated brain image dataset. For a subsequent catheter placement, subsequent brain shift information is determined based on the updated brain image dataset, the patient orientation dataset, and a subsequent burr hole dataset. The brain image dataset is updated again based on the subsequent brain shift information. The re-updated brain image dataset is utilized to update trajectories of the subsequent catheter as well as any preceding catheters.

A system for automated navigation to a target neuron is disclosed. The system comprises a recording electrode including a pipette with a hollow glass tip and a headstage for detecting electrical resistance measurements at the glass tip. The system further comprises an actuator, a light source configured to emit light from the glass tip, an ultrasound transducer for detecting photoacoustic signals in response to the light, a light sensor for detecting optical signals in response to the light, and a processor. The processor iteratively receives the photoacoustic and optical feedback and moves the glass tip via the actuator based on a calculated distance of the target neuron. When the distance at or below a predetermined threshold, the processor maintains the position of the hollow glass tip with respect to the target neuron. Upon successful navigation, the recording electrode may be used to perform single-unit neural recording of the target neuron.

A method for measuring an intracranial fluid bioimpedance in a patient's head, to help detect an abnormality, may involve: securing a volumetric integral phase-shift spectroscopy (VIPS) device to the patient's head; measuring the intracranial fluid bioimpedance with the VIPS device by measuring a phase shift between a magnetic field transmitted from a transmitter on one side of a VIPS device and a magnetic field received at a receiver on another side of the VIPS device, at one or more frequencies; and detecting an abnormality in the intracranial bioimpedance fluid, using a processor in the VIPS device.

Embodiments relate to a magnetic resonance imaging (MRI) technique in which the two-dimensional (2D) Displacement Encoding with Stimulated Echoes (DENSE) imaging technique and the multiband technique are combined to provide a 2D multi-slice quantitative assessment of displacement, deformation, and mechanics indices of tissue. The scan time is equivalent to the short scan time of the conventional single slice 2D imaging while providing spatial volumetric coverage similar to three-dimensional (3D) imaging. The techniques are combined in both the sequence (i.e., data acquisition) and reconstruction sides. Quantification of tissue displacement and motion is achieved through the combination and further evaluation of tissue mechanical properties is provided by calculating different indices based on the displacement and motion values.

Systems and methods for high-resolution STAGE imaging can include acquisition of relatively low-resolution k-space datasets with two separate multi-echo GRE sequences. The multi-echo GRE sequences can correspond to separate and distinct flip angles. Various techniques for combining the low-resolution k-space datasets to generate a relatively high-resolution k-space are described. These techniques can involve combining low-resolution k-space datasets associated with various echo types. The STAGE imaging approaches described herein allow for rapid imaging, enhanced image resolution with relatively small or no increase in MR data acquisition time.

An organ evaluation device, system, or method is configured to receive electrophysiological data from a patient or model organism and integrates the data in a computational backend environment with anatomical data input from an external source, spanning a plurality of file formats, where the input parameters are combined to visualize and output current density and/or current flow activity having ampere-based units displayed in the spatial context of heart or other organ anatomy.

A system and method for noncontact ultrasound imagery capable of generating images in a manner that is safer for eyes and skin is provided. A photoacoustic excitation source may be employed to direct light signals with wavelengths of 1400-1600 nanometers into the patient to generate acoustic disturbances that induce propagating photoacoustic waves. The acoustic disturbances may be translated in defined directions to cause coherent summation of the propagating photoacoustic waves and, thereby, generate a resultant acoustic and/or elastic wave to probe structures within the patient. Vibrations created by the scatter of the resultant wave are detected at the surface of the patient and ultrasound images of the structures within the patient may be generated. Detection of the vibrations may be performed using a laser vibrometer. The excitation and detection systems may be used separately or in combination. Ultrasound images can be generated without physically contacting the patient.

Systems and methods for estimating quantitative histological features of a subject's tissue based on medical images of the subject are provided. For instance, quantitative histological features of a tissue are estimated by comparing medical images of the subject to a trained model that relates histological features to multiple different medical image contrast types, whether from one medical imaging modality or multiple different medical imaging modalities. In general, the trained model is generated based on medical images of ex vivo samples, in vitro samples, in vivo samples or combinations thereof, and is based on histological features extracted from those samples. A machine learning algorithm, or other suitable learning algorithm, is used to generate the trained model. The trained model is not patient-specific and thus, once generated, can be applied to any number of different individual subjects.