A method to increase the reliability and clinical utility of diffusion tensor imaging (DTI) of traumatic brain injury (TBI) in single subjects and a semi-automated method of identifying and quantifying small hemorrhages using susceptibility-weighted images (SWI) of single subjects include storing an image template formed from control subjects, storing a brain image of the single subject, correcting for image acquisition differences of the control subjects and single subject, and performing regional analysis of the brain image of the single subject. The method may include analysis of fractional anisotropy values that are age-corrected between the control subjects and the single subject before performing voxel-based analysis (VBA), and a hybrid VBA and tract-based spatial statistical (TBSS) analysis with the VBA and TBSS results combined using a statistical calculation. The resulting combined DTI image may be further combined with an SWI image, FLAIR image, and/or T1 image of the single subject.

A stroke detection system analyzes images of a person's face over time to detect asymmetric changes in the position of certain reference points that are consistent with sagging or drooping that may be symptomatic of a stroke or TIA. On detecting possible symptoms of a stroke or TIA, the system may alert caregivers or others, and log the event in a database. Identifying stroke symptoms automatically may enable more rapid intervention, and identifying TIA symptoms may enable diagnostic and preventative care to reduce the risk of a future stroke.

A device for detecting spatial differences in fluid level changes in a tissue of a patient may include a support structure for securing the device to a body part of the patient, a processing element operably connected to the support structure, a wireless networking interface operably connected to the support structure and in communication with the processing element and an external computing device via a network, a first transmission module operably connected to the support structure and in communication with the processing element, a second transmission module and a third transmission module operably connected to the support structure and in communication with the processing element. When activated, the first transmission module transmits a first time varying magnetic field through the tissue of the patient. The second and third transmission modules, which are spatially separated from one another, receive first and second versions, respectively, of the first time varying magnetic field.

A magnetic resonance imaging apparatus according to an embodiment includes a processing circuitry. Regarding the k-space data obtained as a result of performing multi-shot imaging that includes a plurality of shots, the processing circuitry obtains a correction coefficient, based on first-type magnetic resonance images generated using the k-space data, the correction coefficient correcting phase shifting occurring in read out direction among the plurality of shots. Then, the processing circuitry corrects the k-space data based on the correction coefficients. Moreover, the processing circuitry generates a second-type magnetic resonance image using the corrected k-space data.

A magnetic resonance imaging apparatus according to an embodiment includes a processing circuitry. Regarding the k-space data obtained as a result of performing multi-shot imaging that includes a plurality of shots, the processing circuitry obtains a correction coefficient, based on first-type magnetic resonance images generated using the k-space data, the correction coefficient correcting phase shifting occurring in read out direction among the plurality of shots. Then, the processing circuitry corrects the k-space data based on the correction coefficients. Moreover, the processing circuitry generates a second-type magnetic resonance image using the corrected k-space data.

Described here is a deep brain stimulation (“DBS”) approach that targets several relevant nodes within brain circuitry, while monitoring multiple symptoms for efficacy. This approach to multi-symptom monitoring and stimulation therapy may be used as an extra stimulation setting in extant DBS devices, particularly those equipped for both stimulation and sensing. The therapeutic efficacy of DBS devices is extended by optimizing them for multiple symptoms (such as sleep disturbance in addition to movement disorders), thus increasing quality of life for patients.

Abstract: A traumatic brain injury (“TBI”) guideline system employing a patient monitoring sensor (30) and a patient monitoring device (10). In operation, the patient monitoring sensor (30) generates data for monitoring a TBI parameter of a patient (e.g., systolic blood pressure, blood oxygen saturation or carbon dioxide expiration of the patient), and the patient monitoring device (10) generates a TBI indicator derived from a comparison of the TBI parameter data to parameter guideline data associated with a poten -tial TBI of the patient. The patient monitoring device (10) may include a patient data monitor module (17a) to monitor the TBI parameter data, and a TBI monitor module (17b) to generate the TBI indicator. The TBI indicator is informative of a TBI status of the patient (e.g., a hypotension status, a hypoxia status or a ventilation status of the patient), and/or a TBI treatment for the patient (e.g., a ventilation treatment for the patient).

Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.

An optical measurement system and method are provided. Pump sample light and probe sample light are delivered through into an anatomical structure of a user. The anatomical structure has molecules having a resonant vibrational frequency equal to the difference between a first optical frequency of the pump sample light and a second optical frequency of the probe sample light, whereby a portion of the probe sample light is inelastically scattered by the molecules as signal light encoded with a physiological event occurring in the molecules, and whereby sample light comprising the signal light exits the anatomical structure. Signal light in the exiting sample light is detected, and an electrical signal representative of the signal light is outputted. The electrical signal is analyzed, and based on this analysis, the presence and the depth of the physiological event in the anatomical structure is determined.

There is provided a system for determining severity of an impact and/or a post impact risk score for a subject, the system comprising an accelerometer configured to output signals indicative of movement of the subject along one or any combination of an x-axis, a y-axis, and a z-axis; a magnetometer configured to output signals indicative of variations in position of the subject in a space defined by the x-axis, the y-axis, and the z-axis; and a gyroscope configured to output signals indicative of angular velocity of the subject around one or any combination of the x-axis, the y-axis, and the z-axis; and a processor configured to receive the output and analyse the output signals to determine for the subject one or any combination of Impact Force, Stun Time, Sway Time, Slow Time and Sway Score, wherein the x-axis is a horizontal axis to the ground directed forward of the subject's body; the y-axis being a horizontal axis to the ground directed laterally of the subject's body; and the z-axis is vertical axis to ground. Also provided are methods to determine a post impact risk score for a subject.