Certain aspects relate to systems and techniques for luminal network navigation. Some aspects relate to incorporating respiratory frequency and/or magnitude into a navigation system to implement patient safety measures. Some aspects relate to identifying, and compensating for, motion caused by patient respiration in order to provide a more accurate identification of the position of an instrument within a luminal network.

An example device for determining one or more transient decelerations includes a memory configured to store a sensed pulse rate signal indicative of one or more sensed pulse rates and processing circuitry. The processing circuitry is configured to determine that an amplitude threshold is crossed by a sensed pulse rate signal indicative of one or more sensed pulse rates. The processing circuitry also is configured to, from a time the amplitude threshold is crossed, determine that a pulse rate returns to within a range of a baseline pulse rate within a number of samples or a time period. The processing circuitry is also configured to, based on the pulse rate returning to within the range of the baseline pulse rate, from the time the amplitude threshold is crossed, within the number of samples or the time period, determine a transient deceleration.

A computer-implemented medical method of determining a breathing signal of a patient is disclosed. The method includes determining a motion trajectory of a structure associated with at least one body part of the patient, the motion trajectory being indicative of a respiratory movement of the structure, acquiring surface data representative of a position of a surface region of the patient, computing an intersection of the determined motion trajectory and the acquired surface data, and determining a breathing signal of the patient based on the computed intersection. The breathing signal is indicative of a breathing state of the patient.

Radiomic analysis of multiparametric magnetic resonance imaging (“MRI”) and magnetic resonance fingerprinting (“MRF”) data enhances delineation and mapping of tumor regions. Radiomic features are extracted from MRI and MRF tumor images. Distinct tumor regions, including but not limited to necrotic core, enhancing tumor, and peritumoral white matter, are segmented and mapped. Whole tumor as well as tumor region characteristics are evaluated. Tumors can also be differentiated and classified by pathology, grading, staging, and so on. Tumor infiltration into peritumoral white matter regions can also be mapped for recurrence prediction

An airway adaptor includes: a gas passage into which a respiratory gas of a subject is to flow; a respiratory gas introducing portion which is configured to guide the respiratory gas expired from at least one of nostrils and a mouth of the subject, to the gas passage; and an airway case on which a temperature sensor that is configured to detect a temperature change of the respiratory gas flowing into the gas passage is mountable.

A method for vascular assessment is disclosed. The method, in some embodiments, comprises receiving a plurality of 2-D angiographic images of a portion of a vasculature of a subject, and processing the images to produce a stenotic model over the vasculature, the stenotic model having measurements of the vasculature at one or more locations along vessels of the vasculature. The method, in some embodiments, further comprises obtaining a flow characteristic of the stenotic model, and calculating an index indicative of vascular function, based, at least in part, on the flow characteristic in the stenotic model.

A method includes determining a heart beat signal during acquisition of MR images obtained at a plurality of cardiac cycles; determining at least one physiological parameter of a heart obtained at the plurality of cardiac cycles; determining a model including, determining, in each of the cardiac cycles, a variable time interval of variable duration and at least one additional time interval based on the heart beat signal and the at least one physiological parameter, the at least one additional time interval having a lower variability in duration than the variable time interval; determining a duration of the variable time interval and a duration of the cardiac cycle for each of the cardiac cycles based on the heart beat signal and the at least one physiological parameter; and assigning the MR images to the different cardiac phases based on the variable time interval and each of the cardiac cycles.

A method for adapting, per cardiac cycle, the parameters governing interpolation of varying and non-interpolation of fixed fractions of each individual cardiac cycle is provided. A time series of data values associated with a cardiac cycle is received, and the time series is scaled to a reference cardiac cycle, wherein the scaling includes applying a model to the time series to generate a scaled time series of data values associated with the first cardiac cycle. The model is trained using the scaled time series.

A bio-signal measuring apparatus includes a sensing apparatus configured to sense electrocardiogram data representing an electrical change according to a pulse of an object and sense heart sound data according to the pulse and a processing apparatus configured to store the electrocardiogram data in a memory. The processing apparatus is further configured to analyze the electrocardiogram data to determine whether or not an abnormal signal is generated in the electrocardiogram data, when the abnormal signal is detected to be generated in the electrocardiogram data, generate a storage control signal for heart sound data associated with the abnormal signal in an abnormal signal section including the abnormal signal, and store the associated heart sound data in the abnormal signal section of the memory in response to the storage control signal.

Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias). The inputs of the algorithm are: a CGM time series; some reference BG measurements; a model of blood to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy, together with (if available) a priori information (e.g. probabilistic distribution) on the parameters of the model. The algorithm first checks for the presence of possible artifacts or outliers on both CGM datastream and BG references, and then rescales the CGM time series by exploiting a retrospective calibration approach based on a regularized deconvolution method subject to the constraint of returning a profile laying within the confidence interval of the reference BG measurements. As output, the retrofitting algorithm produces an improved “retrofitted” quasi-continuous glucose concentration signal that is better (in terms of both accuracy and precision) than the CGM trace originally measured by the sensor. In clinical trials, the so-obtained retrofitted traces can be used to calculate solid outcome measures, avoiding the need of increasing the data collection burden at the patient level.