SmartHeart: Next-generation cardiovascular healthcare via integrated image acquisition, reconstruction, analysis and learning
SmartHeart: Next-generation cardiovascular healthcare via integrated image acquisition, reconstruction, analysis and learning
批准号:
EP/P001009/1
负责人:
Daniel Rueckert
金额:
$669.43万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2016
资助国家:
英国
项目状态:
已结题
起止时间:
2016 至 --
中文摘要
点击翻译按钮获取中文摘要
英文摘要
The vision for our research programme is to pave the way for a fundamentally different approach in which cardiovascular diseases (CVD) are diagnosed, monitored and treated: We propose to develop a diagnosis-driven "smart Magnetic Resonance (MR) scanner" that it is no longer a mere imaging device but instead becomes a highly sophisticated diagnostic tool. The output of a patient scan with the proposed smart MR scanner will not be just an image, but instead a comprehensive diagnostic assessment and interpretation of the patient's cardiovascular health/disease, enabling optimal treatment decisions for best patient outcome. The current approach to cardiovascular MR imaging (cMRI) is essentially serial: image acquisition is followed by image analysis and clinical interpretation. In addition, cardiac/respiratory motion is currently resulting in long scanning times for cMRI, with only a small fraction of the data (10-20%) being used for image reconstruction. This leads to breath-holds that are difficult to tolerate by sick patients. Furthermore, the characterization of clinically relevant tissue parameters requires the acquisition of multiple images which is inefficient. The absolute quantification of tissue parameters also remains a major technical challenge, leading to difficulties in interpreting tissue contrast parameters across scanners, clinical centres and patient populations. Finally, the objective interpretation of comprehensive, multi-parametric cMRI in the context of other complex non-imaging data is highly challenging for clinicians. We propose a transformative approach in which acquisition, analysis and interpretation are tightly coupled, with feedback between the different stages in order to optimize the overall objective: Extracting clinically useful information. Developing such an integrated approach to cardiac imaging will enable rapid, continuous and comprehensive imaging that is both simpler and more efficient than current practice, eliminating "dead time" between separate specialized acquisitions and allowing extraction of multiple dynamic as well as tissue contrast parameters simultaneously.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
登录
查看更多内容
DOI:
10.3389/fcvm.2022.894503
发表时间:
2022
期刊:
FRONTIERS IN CARDIOVASCULAR MEDICINE
影响因子:
3.6
作者:
[Abdulkareem, Musa, Kenawy, Asmaa A., Rauseo, Elisa, Lee, Aaron M., Sojoudi, Alireza, Amir-Khalili, Alborz, Lekadir, Karim, Young, Alistair A., Barnes, Michael R., Barckow, Philipp, Khanji, Mohammed Y., Aung, Nay, Petersen, Steffen E.]
通讯作者:
Petersen, Steffen E.
Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment.
使用深度学习和质量控制评估的深度学习对心脏CT图像进行心房体积估算的可通用框架。
DOI:
10.3389/fcvm.2022.822269
发表时间:
2022
期刊:
Frontiers in cardiovascular medicine
影响因子:
3.6
作者:
[Abdulkareem M, Brahier MS, Zou F, Taylor A, Thomaides A, Bergquist PJ, Srichai MB, Lee AM, Vargas JD, Petersen SE]
通讯作者:
Petersen SE
DOI:
10.1016/j.media.2019.02.007
发表时间:
2019-04
期刊:
Medical image analysis
影响因子:
10.9
作者:
[Alansary A, Oktay O, Li Y, Folgoc LL, Hou B, Vaillant G, Kamnitsas K, Vlontzos A, Glocker B, Kainz B, Rueckert D]
通讯作者:
Rueckert D
Statin Prescribing and Dosing-Failure Has Become an Option-Reply.
他汀类药物处方和剂量失败已成为一种选择-答复。
DOI:
10.1001/jamacardio.2021.0838
发表时间:
2021
期刊:
JAMA cardiology
影响因子:
24
作者:
[Adusumalli S]
通讯作者:
Adusumalli S
DOI:
10.3389/fcvm.2021.787614
发表时间:
2021
期刊:
Frontiers in cardiovascular medicine
影响因子:
3.6
作者:
[Asher C, Puyol-Antón E, Rizvi M, Ruijsink B, Chiribiri A, Razavi R, Carr-White G]
通讯作者:
Carr-White G
共 6 条
Efficient and Robust Assessment of Cardiovascular Disease Using Machine Learning and Ultrasound Imaging
-
批准号:EP/R005982/1
-
项目类别:Research Grant
-
资助金额:$50.71万
-
财政年份:2018
-
负责人:Daniel Rueckert
-
依托单位:
Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function
-
批准号:EP/K030523/1
-
项目类别:Research Grant
-
资助金额:$39.88万
-
财政年份:2013
-
负责人:Daniel Rueckert
-
依托单位:
Biomedical Catalyst – Digital Healthcare Platform for Early Dementia Diagnosis
-
批准号:MC_PC_13034
-
项目类别:Research Grant
-
资助金额:$44.07万
-
财政年份:2012
-
负责人:Daniel Rueckert
-
依托单位:
Computer aided diagnosis of neurological damage to improve care for infants born prematurely
-
批准号:EP/I000445/1
-
项目类别:Research Grant
-
资助金额:$130.41万
-
财政年份:2010
-
负责人:Daniel Rueckert
-
依托单位:
Computational Morphometry of the Developing Cortex
-
批准号:EP/F011830/1
-
项目类别:Research Grant
-
资助金额:$73.02万
-
财政年份:2008
-
负责人:Daniel Rueckert
-
依托单位:
Model-based 2D-3D registration and tracking of deformable objects for image-guided minimally invasive cardiac interventions
-
批准号:EP/C523008/1
-
项目类别:Research Grant
-
资助金额:$33.48万
-
财政年份:2006
-
负责人:Daniel Rueckert
-
依托单位:
国内基金
海外基金
Next Generation Majorana Nanowire Hybrids
-
批准号:--
-
项目类别:--
-
资助金额:20万元
-
批准年份:2020
-
负责人:Panagiotis Kotetes
-
依托单位: