Columbia University

The State University of New York

Arthur Mikhno

Ramin Parsey

Joseph John Mann

Andrew F Laine

Elsa D Angelini

Todd Ogden

The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from molecular Magnetic Resonance Imaging (MRI) data where two groups differ and measuring MRI signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Voxel-based methods for assessing subjects using molecular magnetic resonance imaging

The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from PET data where two groups differ and measuring PET signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Voxel-based methods for assessing subjects using magnetic resonance imaging

The present disclosure is directed to iterative regularized reconstruction methods. In certain embodiments, the methods incorporate locally-weighted total variation denoising to suppress artifacts induced by PSF modeling. In certain embodiments, the methods are useful for suppressing ringing artifacts while contrast recovery is maintained. In certain embodiments, the weighting scheme can be extended to noisy measures introducing a noise-independent weighting scheme. The present disclosure is also directed to a method for quantifying radioligand binding in a subject without collecting arterial blood. In certain embodiments, the methods incorporate using imaging data and electronic health records to predict one or more anchors, which are used to generate an aterial input function (AIF) for the radioligand.

Regularization of images

Voxel-based methods for assessing subjects using positron emission tomography

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New City, NY, United States of America

Arthur Mikhno