Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories.

Machine learning RF shimming: Prediction by iteratively projected ridge regression.

Purpose: To obviate online slice-by-slice RF shim optimization and reduce B1+ mapping requirements for patient-specific RF shimming in high-field magnetic resonance imaging.

Theory And Methods: RF Shim Prediction by Iteratively Projected Ridge Regression (PIPRR) predicts patient-specific, SAR-efficient RF shims with a machine learning approach that merges learning with training shim design. To evaluate it, a set of B1+ maps was simulated for 100 human heads for a 24-element coil at 7T.

Ghost reduction in echo-planar imaging by joint reconstruction of images and line-to-line delays and phase errors.

Purpose: To correct line-to-line delays and phase errors in echo-planar imaging (EPI).

Theory And Methods: EPI-trajectory auto-corrected image reconstruction (EPI-TrACR) is an iterative maximum-likelihood technique that exploits data redundancy provided by multiple receive coils between nearby lines of k-space to determine and correct line-to-line trajectory delays and phase errors that cause ghosting artifacts. EPI-TrACR was efficiently implemented using a segmented FFT and was applied to in vivo brain data acquired at 7 T across acceleration (1×-4×) and multishot factors (1-4 shots), and in a time series.

Electrophysiological measurement of information flow during visual search.

The temporal relationship between different stages of cognitive processing is long debated. This debate is ongoing, primarily because it is often difficult to measure the time course of multiple cognitive processes simultaneously. We employed a manipulation that allowed us to isolate ERP components related to perceptual processing, working memory, and response preparation, and then examined the temporal relationship between these components while observers performed a visual search task.

Trajectory Auto-Corrected image reconstruction.

Purpose: To estimate k-space trajectory errors in non-Cartesian acquisitions and reconstruct distortion-free images, without trajectory measurements or gradient calibrations.

Theory And Methods: The Trajectory Auto-Corrected image Reconstruction method jointly estimates k-space trajectory errors and images, based on SENSE and SPIRiT parallel imaging reconstruction. The underlying idea is that parallel imaging and oversampling in the center of k-space provides data redundancy that can be exploited to simultaneously reconstruct images and correct trajectory errors.