An EUV collector is rotated between or during operations of an EUV photolithography system. Rotating the EUV collector causes contamination to distribute more evenly over the collector's surface. This reduces the rate at which the EUV photolithography system loses image fidelity with increasing contamination and thereby increases the collector lifetime. Rotating the collector during operation of the EUV photolithography system can induce convection and reduce the contamination rate. By rotating the collector at sufficient speed, some contaminating debris can be removed through the action of centrifugal force.

A system and method for incorporating partially coherent illumination models into the problem of phase and amplitude retrieval from a stack of intensity images. The recovery of phase could be realized by many methods, including Kalman filters or other nonlinear optimization algorithms that provide least squares error between the measurement and estimation.

Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of objects. Here, a diffractive QPI network architecture is disclosed that can synthesize the quantitative phase image of an object by converting the input phase information of a scene or object(s) into intensity variations at the output plane. A diffractive QPI network is a specialized all-optical device designed to perform a quantitative phase-to-intensity transformation through passive diffractive/reflective surfaces that are spatially engineered using deep learning and image data. Forming a compact, all-optical network that axially extends only 200-300 (=illumination wavelength), this framework replaces traditional QPI systems and related digital computational burdens with a set of passive substrate layers. All-optical diffractive QPI networks can potentially enable power-efficient, high frame-rate and compact phase imaging systems that might be useful for various applications, including, e.g., on-chip microscopy and sensing.

Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of objects. Here, a diffractive QPI network architecture is disclosed that can synthesize the quantitative phase image of an object by converting the input phase information of a scene or object(s) into intensity variations at the output plane. A diffractive QPI network is a specialized all-optical device designed to perform a quantitative phase-to-intensity transformation through passive diffractive/reflective surfaces that are spatially engineered using deep learning and image data. Forming a compact, all-optical network that axially extends only 200-300 (=illumination wavelength), this framework replaces traditional QPI systems and related digital computational burdens with a set of passive substrate layers. All-optical diffractive QPI networks can potentially enable power-efficient, high frame-rate and compact phase imaging systems that might be useful for various applications, including, e.g., on-chip microscopy and sensing.

The invention generally provides systems and methods for performing vessel segmentation from flow data, such as but not limited to 4D flow Magnetic Resonance Imaging (MRI) data. In certain aspects, the systems and methods of the invention may involve receiving flow data representative of flow in a vessel (such as 4D MRI flow data); identifying net flow effects in the flow data (such as 4D MRI flow data) according to a standardized difference of means (SDM) velocity that involves quantifying a ratio between net flow and observed flow pulsatility in each voxel of the received flow data (such as 4D MRI flow data); and identifying voxels with higher SDM velocity values than stationary tissue voxels, thereby performing vessel segmentation from flow data (such as 4D MRI flow data).