An image analysis device may include a memory storing learning data for executing image analysis, and may obtain cell image data representing a cell image including a plurality of cell objects, sequentially identify plural pieces of partial image data from the cell image data, sequentially execute a center determination process on each of the plural pieces of partial image data, classify at least one cell corresponding to at least one cell object among a plurality of cell objects by using results of the center determination process on the plural pieces of partial image data and classification data included in learning data, and output a classification result.

A LED controller includes a power distribution module and an interface to an external data bus. An image frame buffer is connected to the interface to receive image data. A separate logic module is connected to the interface and configured to modify image frame buffer output signals sent to an LED pixel array connected to the image frame buffer. The LED pixel array can project light according to a pattern and intensity defined at least in part by the image held in the image frame buffer.

An LED controller system includes an LED controller including an image frame buffer able to receive image data. A sensor processing module is used to receive and process sensor data and a decision module is used to determine actions taken in response to processed sensor data. An image creation module is used to create images to be sent to the image frame buffer of the LED controller.

Example embodiments associated with NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. Sampling is performed in response to a fast imaging with steady state free precession (MRF-FISP) pulse sequence having an unbalanced gradient that dephases transverse magnetization. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals, and a characterization logic that characterizes a resonant species in the object as a result of comparing acquired signals to reference signals. The unbalanced gradient in the MRF-FISP pulse sequence reduces sensitivity to B0 in homogeneity.

Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

Example apparatus and methods perform magnetic resonance fingerprinting (MRF) for arterial spin labeling (ASL) based parameter quantification. ASL with MRF produces a nuclear magnetic resonance signal time course from which simultaneous quantification of ASL perfusion-related parameters can be achieved. The parameters may include cerebral blood flow, transit time, T1, or other parameters. The quantification uses values from a dictionary of signal time courses that were generated or augmented using Bloch simulation, knowledge of the sequence, or previous observations. The dictionary may account for inflow or outflow of labeled spins and may model arterial input. An ASL-MRF pulse sequence may differ from conventional pulse sequences. For example, an ASL-MRF pulse sequence may include non-uniform control pulses, non-uniform label pulses, non-uniform post labeling delay time, non-uniform background suppression pulses, non-uniform acquisition repetition time, or non-uniform acquisition flip angle.