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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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相关实验视频

Updated: Jun 10, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

使用视觉变压器和的边缘探测器绘制MR电气性能映射.

Ilias I Giannakopoulos¹, Giuseppe Carluccio², Mahesh B Keerthivasan³

¹The Bernard and Irene Schwartz Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.

Magnetic resonance in medicine

|October 17, 2024

概括

一个新的3D视觉转换器神经网络从MRI数据中重建电气特性 (EP). 这种方法准确地绘制导电性和导电性,为临床应用铺平了道路.

关键词:

电气性能映射绘制的电气特性.通过电磁模拟进行模拟.转移学习转移学习视觉变压器视觉变压器

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Topographical Estimation of Visual Population Receptive Fields by fMRI

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

相关实验视频

Last Updated: Jun 10, 2025

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Topographical Estimation of Visual Population Receptive Fields by fMRI

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Published on: February 3, 2015

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

科学领域:

医疗成像医学成像
计算神经科学是一种神经科学.
生物物理学的生物物理.

背景情况:

磁共振成像是一种强大的非侵入性工具.
精确的电特性 (EP) 重建对于先进的MR应用至关重要.
目前用于EP重建的方法在速度和准确性方面存在局限性.

研究的目的:

开发一种基于3D视觉变压器的新型神经网络,用于从MR测量中重建电性质 (EP).
使用MR信号特征和对象边界作为EP映射的输入.
为了建立一个临床适用 in vivo EP重建的基础.

主要方法:

一个3D视觉转换器神经网络被设计用于处理MR数据.
输入包括传输磁场大小,转接相,和坎尼边缘面具.
该网络在广泛的合成数据集上进行了训练,并在现实的头部模型上进行了微调,并对实验数据进行了验证.

主要成果:

该模型在重建导电性和导电性方面取得了很高的准确性,实际头部模型的平均峰值正常化绝对误差 (PNAE) 低于3%.
损伤检测和表征是准确的,重建的值接近基本真相.
幽灵和体内扫描的实验验证证明了该方法的稳定性和解剖学保存.

结论:

成功开发了一种基于学习的新方法,用于基于MR的电气物业重建.
该方法对准确和高效的体内EP映射有显著的前景.
这项工作代表了基于MR的EP重建临床翻译的关键进展.