Background phase correction for quantitative cardiovascular MRI
定量心血管 MRI 的背景相位校正
基本信息
- 批准号:9182586
- 负责人:
- 金额:$ 18.36万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-08-01 至 2018-07-31
- 项目状态:已结题
- 来源:
- 关键词:AbdomenAcousticsAddressAffectAnatomyAneurysmAreaAtherosclerosisBlood VesselsBlood flowCardiacCardiac OutputCardiovascular systemClinicalCoupledDataDevelopmentDiagnosisDoppler UltrasoundError SourcesEvaluationFunctional disorderHealthHeartHeart DiseasesImageImaging DeviceImaging PhantomsImaging TechniquesIntracranial AneurysmIntracranial Arterial StenosisKidneyLeadLeast-Squares AnalysisLinkLiver CirrhosisLocationLungMagnetic Resonance ImagingManufacturer NameMapsMeasurementMeasuresMechanicsMedical ImagingMethodologyMethodsMorphologyPatientsPelvisPerformancePeripheral arterial diseasePhasePhysiologic pulsePhysiologyPolynomial ModelsPortal HypertensionPositioning AttributeProcessProtocols documentationPulsatile FlowReportingResearch PersonnelScanningSchemeShunt DeviceSliceSpecific qualifier valueStenosisStroke VolumeTimeTissuesUncertaintyValidationVascular DiseasesWorkabstractingbaseclinical sequencingcomputerized data processingcongenital heart disordercostdata acquisitionhealthy volunteerheart imaginghemodynamicshuman subjectimprovedin vivointerestmeetingsnon-invasive imagingtool
项目摘要
Project Summary/Abstract
Alterations in hemodynamics have been linked to wide-ranging cardiac and vascular conditions, including
congenital heart disease, valvular abnormalities, aortic atherosclerosis and aneurysm, renal stenosis, portal
hypertension due to liver cirrhosis, intracranial aneurysm and stenosis, and peripheral arterial disease. Phase-
contrast MRI (PC-MRI) is a noninvasive imaging technique that can potentially provide a comprehensive
evaluation of hemodynamics, which can be coupled with other important MRI-derived information on
cardiovascular anatomy, function, and tissue characterization. However, the credibility of PC-MRI as a
quantitative tool is challenged by the inaccuracies introduced by background phase. Studies have shown that
this background phase can introduce significant errors in the quantification of flow. One method that has been
proposed to quantify and correct for the background phase is to perform a separate scan using a static
phantom. This method, despite being robust, is impractical because of the significant extra time required to
perform phantom imaging for each clinical sequence performed. Another widely reported method to correct
background phase is based on performing polynomial fitting to the pixels that belong to the static tissue. The
accuracy of this method heavily relies on the availability of static tissue in the close vicinity of the region of
interest–a requirement that is often not met when imaging the heart or great vessels.
To address the issue of background phase that invariably impacts every PC-MRI measurement, we propose a
new correction scheme called multi-slice acquisition and processing (mSAP). In mSAP, in addition to the slice
of interest, at least one extra slice is collected using the same slice orientation and gradient waveforms but with
a different table position. By jointly processing the background phase information from multiple slices, mSAP
circumvents the shortcomings associated with existing methods at the cost of slightly prolonged acquisition. In
Specific Aim 1, we will develop a data acquisition and processing method for mSAP. We will modify and
streamline our current PC-MRI acquisition protocol to minimize the overhead associated with mSAP. To jointly
process the multi-slice data, we will develop and implement polynomial regression based on generalized least
squares with an ℓ1-norm penalty imposed on the coefficients of the polynomial. This fitting method is
completely automated and does not require tuning parameters. In Specific Aim 2, we will validate mSAP using
a pulsatile flow phantom and healthy volunteers. By using just one additional slice, we anticipate mSAP to
reduce the background phase errors to the level where miscalculation of flow volume is reduced to below 5%.
Our preliminary data demonstrate the validity of the primary assumption made in mSAP, i.e., background
phase maps collected using the same gradient waveforms but different table positions are identical. We
believe the methods developed in this work can be readily utilized in clinical settings to improve the accuracy of
an otherwise potent imaging tool.
项目总结/文摘
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Rizwan Ahmad其他文献
Rizwan Ahmad的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Rizwan Ahmad', 18)}}的其他基金
A comprehensive valvular heart disease assessment with stress cardiac MRI
通过负荷心脏 MRI 进行全面的瓣膜性心脏病评估
- 批准号:
10664961 - 财政年份:2021
- 资助金额:
$ 18.36万 - 项目类别:
A comprehensive deep learning framework for MRI reconstruction
用于 MRI 重建的综合深度学习框架
- 批准号:
10382334 - 财政年份:2021
- 资助金额:
$ 18.36万 - 项目类别:
A comprehensive deep learning framework for MRI reconstruction
用于 MRI 重建的综合深度学习框架
- 批准号:
10608060 - 财政年份:2021
- 资助金额:
$ 18.36万 - 项目类别:
A comprehensive valvular heart disease assessment with stress cardiac MRI
通过负荷心脏 MRI 进行全面的瓣膜性心脏病评估
- 批准号:
10455412 - 财政年份:2021
- 资助金额:
$ 18.36万 - 项目类别:
A comprehensive deep learning framework for MRI reconstruction
用于 MRI 重建的综合深度学习框架
- 批准号:
10211757 - 财政年份:2021
- 资助金额:
$ 18.36万 - 项目类别:
A New Paradigm for Rapid, Accurate Cardiac Magnetic Resonance Imaging
快速、准确的心脏磁共振成像的新范例
- 批准号:
10171886 - 财政年份:2017
- 资助金额:
$ 18.36万 - 项目类别:
A New Paradigm for Rapid, Accurate Cardiac Magnetic Resonance Imaging
快速、准确的心脏磁共振成像的新范例
- 批准号:
9330525 - 财政年份:2017
- 资助金额:
$ 18.36万 - 项目类别:
MRI T2 mapping for quantitative assessment of venous oxygen saturation
用于定量评估静脉血氧饱和度的 MRI T2 映射
- 批准号:
9325034 - 财政年份:2016
- 资助金额:
$ 18.36万 - 项目类别:
Background phase correction for quantitative cardiovascular MRI
定量心血管 MRI 的背景相位校正
- 批准号:
9297307 - 财政年份:2016
- 资助金额:
$ 18.36万 - 项目类别:
相似海外基金
Nonlinear Acoustics for the conditioning monitoring of Aerospace structures (NACMAS)
用于航空航天结构调节监测的非线性声学 (NACMAS)
- 批准号:
10078324 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
BEIS-Funded Programmes
ORCC: Marine predator and prey response to climate change: Synthesis of Acoustics, Physiology, Prey, and Habitat In a Rapidly changing Environment (SAPPHIRE)
ORCC:海洋捕食者和猎物对气候变化的反应:快速变化环境中声学、生理学、猎物和栖息地的综合(蓝宝石)
- 批准号:
2308300 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
Continuing Grant
University of Salford (The) and KP Acoustics Group Limited KTP 22_23 R1
索尔福德大学 (The) 和 KP Acoustics Group Limited KTP 22_23 R1
- 批准号:
10033989 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
Knowledge Transfer Partnership
User-controllable and Physics-informed Neural Acoustics Fields for Multichannel Audio Rendering and Analysis in Mixed Reality Application
用于混合现实应用中多通道音频渲染和分析的用户可控且基于物理的神经声学场
- 批准号:
23K16913 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
Grant-in-Aid for Early-Career Scientists
Combined radiation acoustics and ultrasound imaging for real-time guidance in radiotherapy
结合辐射声学和超声成像,用于放射治疗的实时指导
- 批准号:
10582051 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
Comprehensive assessment of speech physiology and acoustics in Parkinson's disease progression
帕金森病进展中言语生理学和声学的综合评估
- 批准号:
10602958 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
The acoustics of climate change - long-term observations in the arctic oceans
气候变化的声学——北冰洋的长期观测
- 批准号:
2889921 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
Studentship
Collaborative Research: Estimating Articulatory Constriction Place and Timing from Speech Acoustics
合作研究:从语音声学估计发音收缩位置和时间
- 批准号:
2343847 - 财政年份:2023
- 资助金额:
$ 18.36万 - 项目类别:
Standard Grant
Flow Physics and Vortex-Induced Acoustics in Bio-Inspired Collective Locomotion
仿生集体运动中的流动物理学和涡激声学
- 批准号:
DGECR-2022-00019 - 财政年份:2022
- 资助金额:
$ 18.36万 - 项目类别:
Discovery Launch Supplement
Collaborative Research: Estimating Articulatory Constriction Place and Timing from Speech Acoustics
合作研究:从语音声学估计发音收缩位置和时间
- 批准号:
2141275 - 财政年份:2022
- 资助金额:
$ 18.36万 - 项目类别:
Standard Grant














{{item.name}}会员




