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Continuous Compensation of Brain Shift during Neurosurgery

神经外科手术期间脑转移的持续补偿

基本信息

批准号：
10294312
负责人：
SARAH FRISKEN
金额：
$ 22.39万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2022-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10294312
关键词：
3-Dimensional 3D ultrasound Address Adoption Algorithms Biopsy Brain Brain Neoplasms Breast Cessation of life Clinical Clinical Research Complex Computer Graphics Computer software Consumption Craniotomy Custom Data Devices Evolution Excision Feedback Financial compensation Freedom Functional Imaging Glioma Goals Head Hospitals Image Image-Guided Surgery Knowledge Life Expectancy Liquid substance Literature Liver Location Lung Magnetic Resonance Imaging Manuals Mathematics Measurement Measures Medical Imaging Methods Modeling Modernization Monitor Neurologic Deficit Neuronavigation Neurosurgeon Operative Surgical Procedures Patients Primary Brain Neoplasms Property Prostate Quality of life Reporting Research Risk Sampling Seizures Side Speed Structure Surgeon Surgical Instruments Surgically-Created Resection Cavity System Testing Time Tissues Translations Tumor Tissue Ultrasonography United States Update Visualization anatomic imaging brain shape brain surgery brain tissue clinical practice curve fitting design feature detection image registration imaging facilities improved neurosurgery novel physical model pressure prevent prototype reduce symptoms soft tissue software systems systems research tool tumor

项目摘要

Project Summary / Abstract There are nearly 700,000 people living with primary brain tumors in the United States, with nearly 80,000 new cases and 11,000 deaths expected in 2018. Surgical removal is the first defense against brain tumors because it relieves symptoms, decreases seizure risks and increases life expectancy. Recent studies have shown that extent of tumor resection is strongly correlated with both freedom from progression and survival. However, tumors are often situated in and around critical brain structures and damaging these structures can cause loss of brain function. Thus, the primary goal in brain surgery is to maximize the extent of tumor resection while minimizing damage to surrounding brain tissue. Commercial neuronavigation systems present pre-operative image data to the surgeon during surgery that can help them visualize the location of their surgical instruments relative to these critical structures. Unfortunately, commercial systems do not compensate for progressive deformation of the brain during surgery, known as brain shift, which can be as large as 1-2 centimeters so the accuracy of neuronavigation systems decreases progressively during surgery. What is needed is a way to measure and compensate for brain shift continuously during surgery so that surgeons have timely, up-to-date information about what tissue has been removed, what tissue remains, and where nearby critical brain structures are. Such a system would enable neurosurgeons to make timely decisions that increase life expectancy and improve quality-of-life. While there is promising research in modeling brain deformation during surgery and a few end-to- end research systems that provide brain shift compensation, current approaches have a number of limitations. In particular, they rely on intraoperative data that is only available at a few time-points during surgery and they require many minutes of computation before model updates can be presented to the surgeon. This proposal addresses the three bottlenecks in state-of-the-art brain shift compensation research that prevent its adoption in clinical practice: 1) current method for modeling brain shift do not directly measure what has been removed during tumor resection so they tend to be inaccurate at the resection boundary, which is precisely where accuracy is most needed; 2) algorithms for modeling brain shift require significant preprocessing, computational power, and are too slow to provide timely feedback to the surgeon; and 3) intraoperative image acquisition is disruptive, time consuming and expensive so updates are infrequent. In this proposal we will address these bottlenecks by applying algorithms developed for real-time computer graphics to model the resection cavity and brain shift, and a new device and surgical workflow that will allow us to collect 3D ultrasound without disrupting surgery so we can monitor brain shift at frequent intervals. This project will address these bottlenecks with the following specific aims: Aim 1. Investigate the use of Adaptively Sampled Distance Fields for modeling of tumor resection Aim 2. Extend and investigate the use of 3D Chainmail for real-time brain shift modeling from intraoperative ultrasound Aim 3. Demonstrate continuous brain shift monitoring with a new surgical workflow and a prototype intraoperative ultrasound device

项目摘要/摘要

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries.

用于多标签分割并保留清晰边界的 SurfaceNet。

DOI：
发表时间：
2022
期刊：
The Journal of computer graphics techniques
影响因子：
0
作者：
Frisken,SarahF
通讯作者：
Frisken,SarahF

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor.

DOI：
10.3390/cancers15030825
发表时间：
2023-01-29
期刊：
Cancers
影响因子：
5.2
作者：
通讯作者：

Alignment of Cortical Vessels viewed through the Surgical Microscope with Preoperative Imaging to Compensate for Brain Shift.

通过手术显微镜观察皮质血管与术前成像的对齐，以补偿大脑移位。

DOI：
10.1117/12.2547620
发表时间：
2020
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Haouchine,Nazim;Juvekar,Parikshit;Golby,Alexandra;Wells3rd,WilliamM;Cotin,Stephane;Frisken,Sarah
通讯作者：
Frisken,Sarah

Novice-performed point-of-care ultrasound for home-based imaging.

DOI：
10.1038/s41598-022-24513-x
发表时间：
2022-11-28
期刊：
SCIENTIFIC REPORTS
影响因子：
4.6
作者：
Duggan, Nicole M.;Jowkar, Nick;Ma, Irene W. Y.;Schulwolf, Sara;Selame, Lauren A.;Fischetti, Chanel E.;Kapur, Tina;Goldsmith, Andrew J.
通讯作者：
Goldsmith, Andrew J.

Automatic framework for patient-specific modelling of tumour resection-induced brain shift.