UK Biobank's cardiovascular magnetic resonance protocol.
ABSTRACT: UK Biobank's ambitious aim is to perform cardiovascular magnetic resonance (CMR) in 100,000 people previously recruited into this prospective cohort study of half a million 40-69 year-olds.We describe the CMR protocol applied in UK Biobank's pilot phase, which will be extended into the main phase with three centres using the same equipment and protocols. The CMR protocol includes white blood CMR (sagittal anatomy, coronary and transverse anatomy), cine CMR (long axis cines, short axis cines of the ventricles, coronal LVOT cine), strain CMR (tagging), flow CMR (aortic valve flow) and parametric CMR (native T1 map).This report will serve as a reference to researchers intending to use the UK Biobank resource or to replicate the UK Biobank cardiovascular magnetic resonance protocol in different settings.
Project description:<h4>Background</h4>Final infarct size following coronary occlusion is determined by the duration of ischemia, the size of myocardium at risk (MaR) and reperfusion injury. The reference method for determining MaR, single-photon emission computed tomography (SPECT) before reperfusion, is impractical in an acute setting. The aim of the present study was to evaluate whether MaR can be determined from the contrast enhanced myocardium using steady-state free precession (SSFP) cine cardiovascular magnetic resonance (CMR) performed one week after the acute event in ST-elevation myocardial infarction (STEMI) patients with total coronary occlusion.<h4>Results</h4>Sixteen patients with STEMI (age 64 +/- 8 years) received intravenous 99 m-Tc immediately before primary percutaneous coronary intervention. SPECT was performed within four hours. MaR was defined as the non-perfused myocardial volume derived with SPECT. CMR was performed 7.8 +/- 1.2 days after the myocardial infarction using a protocol in which the contrast agent was administered before acquisition of short-axis SSFP cines. MaR was evaluated as the contrast enhanced myocardial volume in the cines by two blinded observers. MaR determined from the enhanced region on cine CMR correlated significantly with that derived with SPECT (r2 = 0.78, p < 0.001). The difference in MaR determined by CMR and SPECT was 0.5 +/- 5.1% (mean +/- SD). The interobserver variability of contrast enhanced cine SSFP measurements was 1.6 +/- 3.7% (mean +/- SD) of the left ventricle wall volume.<h4>Conclusions</h4>Contrast enhanced SSFP cine CMR performed one week after acute infarction accurately depicts MaR prior to reperfusion in STEMI patients with total occlusion undergoing primary PCI. This suggests that a single CMR examination might be performed for determination of MaR and infarct size.
Project description:INTRODUCTION:Pediatric z scores are necessary to describe size and structure of the heart in growing children, however, development of an accurate z score calculator requires robust normal datasets, which are difficult to obtain with cardiovascular magnetic resonance (CMR) in children. Motion-corrected (MOCO) cines from re-binned, reconstructed real-time cine offer a free-breathing, rapid acquisition resulting in cines with high spatial and temporal resolution. In combination with child-friendly positioning and entertainment, MOCO cine technique allows for rapid cine volumetry in patients of all ages without sedation. Thus, our aim was to prospectively enroll normal infants and children birth-12?years for creation and validation of a z score calculator describing normal right ventricular (RV) and left ventricular (LV) size. METHODS:With IRB approval and consent/assent, 149 normal children successfully underwent a brief noncontrast CMR on a 1.5?T scanner including MOCO cines in the short axis, and RV and LV volumes were measured. 20% of scans were re-measured for interobserver variability analyses. A general linear modeling (GLM) framework was employed to identify and properly represent the relationship between CMR-based assessments and anthropometric data. Scatter plots of model fit and Akaike's information criteria (AIC) results were used to guide the choice among alternative models. RESULTS:A total of 149 subjects aged 22?days-12?years (average 5.1?±?3.6?years), with body surface area (BSA) range 0.21-1.63?m2 (average 0.8?±?0.35?m2) were scanned. All ICC values were?>?95%, reflecting excellent agreement between raters. The model that provided the best fit of volume measure to the data included BSA with higher order effects and gender as independent variables. Compared with earlier z score models, there is important additional growth inflection in early toddlerhood with similar z score prediction in later childhood. CONCLUSIONS:Free-breathing, MOCO cines allow for accurate, reliable RV and LV volumetry in a wide range of infants and children while awake. Equations predicting fit between LV and RV normal values and BSA are reported herein for purposes of creating z scores. TRIAL REGISTRATION:clinicaltrials.gov NCT02892136, Registered 7/21/2016.
Project description:OBJECTIVES:Although myocardial strain analysis is a potential tool to improve patient selection for cardiac resynchronization therapy (CRT), there is currently no validated clinical approach to derive segmental strains. We evaluated the novel segment length in cine (SLICE) technique to derive segmental strains from standard cardiovascular MR (CMR) cine images in CRT candidates. METHODS:Twenty-seven patients with left bundle branch block underwent CMR examination including cine imaging and myocardial tagging (CMR-TAG). SLICE was performed by measuring segment length between anatomical landmarks throughout all phases on short-axis cines. This measure of frame-to-frame segment length change was compared to CMR-TAG circumferential strain measurements. Subsequently, conventional markers of CRT response were calculated. RESULTS:Segmental strains showed good to excellent agreement between SLICE and CMR-TAG (septum strain, intraclass correlation coefficient (ICC) 0.76; lateral wall strain, ICC 0.66). Conventional markers of CRT response also showed close agreement between both methods (ICC 0.61-0.78). Reproducibility of SLICE was excellent for intra-observer testing (all ICC ?0.76) and good for interobserver testing (all ICC ?0.61). CONCLUSIONS:The novel SLICE post-processing technique on standard CMR cine images offers both accurate and robust segmental strain measures compared to the 'gold standard' CMR-TAG technique, and has the advantage of being widely available. KEY POINTS:• Myocardial strain analysis could potentially improve patient selection for CRT. • Currently a well validated clinical approach to derive segmental strains is lacking. • The novel SLICE technique derives segmental strains from standard CMR cine images. • SLICE-derived strain markers of CRT response showed close agreement with CMR-TAG. • Future studies will focus on the prognostic value of SLICE in CRT candidates.
Project description:<h4>Objectives</h4>This study sought to clinically validate a novel 3-dimensional (3D) ultrafast cardiac magnetic resonance (CMR) protocol including cine (anatomy and function) and late gadolinium enhancement (LGE), each in a single breath-hold.<h4>Background</h4>CMR is the reference tool for cardiac imaging but is time-consuming.<h4>Methods</h4>A protocol comprising isotropic 3D cine (Enhanced sensitivity encoding [SENSE] by Static Outer volume Subtraction [ESSOS]) and isotropic 3D LGE sequences was compared with a standard cine+LGE protocol in a prospective study of 107 patients (age 58 ± 11 years; 24% female). Left ventricular (LV) mass, volumes, and LV and right ventricular (RV) ejection fraction (LVEF, RVEF) were assessed by 3D ESSOS and 2D cine CMR. LGE (% LV) was assessed using 3D and 2D sequences.<h4>Results</h4>Three-dimensional and LGE acquisitions lasted 24 and 22 s, respectively. Three-dimensional and LGE images were of good quality and allowed quantification in all cases. Mean LVEF by 3D and 2D CMR were 51 ± 12% and 52 ± 12%, respectively, with excellent intermethod agreement (intraclass correlation coefficient [ICC]: 0.96; 95% confidence interval [CI]: 0.94 to 0.97) and insignificant bias. Mean RVEF 3D and 2D CMR were 60.4 ± 5.4% and 59.7 ± 5.2%, respectively, with acceptable intermethod agreement (ICC: 0.73; 95% CI: 0.63 to 0.81) and insignificant bias. Both 2D and 3D LGE showed excellent agreement, and intraobserver and interobserver agreement were excellent for 3D LGE.<h4>Conclusions</h4>ESSOS single breath-hold 3D CMR allows accurate assessment of heart anatomy and function. Combining ESSOS with 3D LGE allows complete cardiac examination in <1 min of acquisition time. This protocol expands the indication for CMR, reduces costs, and increases patient comfort.
Project description:OBJECTIVES:This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. BACKGROUND:Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. METHODS:The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps' ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. RESULTS:Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ? 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. CONCLUSIONS:The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
Project description:<h4>Background</h4>Numerous self-gated cardiac imaging techniques have been reported in the literature. Most can track either cardiac or respiratory motion, and many incur some overhead to imaging data acquisition. We previously described a Cartesian cine imaging technique, pseudo-projection motion tracking with golden-step phase encoding, capable of tracking both cardiac and respiratory motion at no cost to imaging data acquisition. In this work, we describe improvements to the technique by dramatically reducing its vulnerability to eddy current and flow artifacts and demonstrating its effectiveness in expanded cardiovascular applications.<h4>Methods</h4>As with our previous golden-step technique, the Cartesian phase encodes over time were arranged based on the integer golden step, and readouts near k<sub>y</sub>?=?0 (pseudo-projections) were used to derive motion. In this work, however, the readouts were divided into equal and consecutive temporal segments, within which the readouts were sorted according to k<sub>y</sub>. The sorting reduces the phase encode jump between consecutive readouts while maintaining the pseudo-randomness of k<sub>y</sub> to sample both cardiac and respiratory motion without comprising the ability to retrospectively set the temporal resolution of the original technique. On human volunteers, free-breathing, electrocardiographic (ECG)-free cine scans were acquired for all slices of the short axis stack and the 4-chamber view of the long axis. Retrospectively, cardiac motion and respiratory motion were automatically extracted from the pseudo-projections to guide cine reconstruction. The resultant image quality in terms of sharpness and cardiac functional metrics was compared against breath-hold ECG-gated reference cines.<h4>Results</h4>With sorting, motion tracking of both cardiac and respiratory motion was effective for all slices orientations imaged, and artifact occurrence due to eddy current and flow was efficiently eliminated. The image sharpness derived from the self-gated cines was found to be comparable to the reference cines (mean difference less than 0.05?mm<sup>-?1</sup> for short-axis images and 0.075?mm<sup>-?1</sup> for long-axis images), and the functional metrics (mean difference?<?4?ml) were found not to be statistically different from those from the reference.<h4>Conclusions</h4>This technique dramatically reduced the eddy current and flow artifacts while preserving the ability of cost-free motion tracking and the flexibility of choosing arbitrary navigator zone width, number of cardiac phases, and duration of scanning. With the restriction of the artifacts removed, the Cartesian golden-step cine imaging can now be applied to cardiac imaging slices of more diverse orientation and anatomy at greater reliability.
Project description:<h4>Background</h4>Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e') are key metrics of systolic and diastolic function, but not often measured by cardiovascular magnetic resonance (CMR). Its derivation is possible with manual, precise annotation of the mitral valve (MV) insertion points along the cardiac cycle in both two and four-chamber long-axis cines, but this process is highly time-consuming, laborious, and prone to errors. A fully automated, consistent, fast, and accurate method for MV plane tracking is lacking. In this study, we propose MVnet, a deep learning approach for MV point localization and tracking capable of deriving such clinical metrics comparable to human expert-level performance, and validated it in a multi-vendor, multi-center clinical population.<h4>Methods</h4>The proposed pipeline first performs a coarse MV point annotation in a given cine accurately enough to apply an automated linear transformation task, which standardizes the size, cropping, resolution, and heart orientation, and second, tracks the MV points with high accuracy. The model was trained and evaluated on 38,854 cine images from 703 patients with diverse cardiovascular conditions, scanned on equipment from 3 main vendors, 16 centers, and 7 countries, and manually annotated by 10 observers. Agreement was assessed by the intra-class correlation coefficient (ICC) for both clinical metrics and by the distance error in the MV plane displacement. For inter-observer variability analysis, an additional pair of observers performed manual annotations in a randomly chosen set of 50 patients.<h4>Results</h4>MVnet achieved a fast segmentation (<1 s/cine) with excellent ICCs of 0.94 (MAPSE) and 0.93 (LV e') and a MV plane tracking error of -0.10 ± 0.97 mm. In a similar manner, the inter-observer variability analysis yielded ICCs of 0.95 and 0.89 and a tracking error of -0.15 ± 1.18 mm, respectively.<h4>Conclusion</h4>A dual-stage deep learning approach for automated annotation of MV points for systolic and diastolic evaluation in CMR long-axis cine images was developed. The method is able to carefully track these points with high accuracy and in a timely manner. This will improve the feasibility of CMR methods which rely on valve tracking and increase their utility in a clinical setting.
Project description:BACKGROUND: Cine cardiovascular magnetic resonance (CMR) is challenging in patients who cannot perform repeated breath holds. Real-time, free-breathing acquisition is an alternative, but image quality is typically inferior. There is a clinical need for techniques that achieve similar image quality to the segmented cine using a free breathing acquisition. Previously, high quality retrospectively gated cine images have been reconstructed from real-time acquisitions using parallel imaging and motion correction. These methods had limited clinical applicability due to lengthy acquisitions and volumetric measurements obtained with such methods have not previously been evaluated systematically. METHODS: This study introduces a new retrospective reconstruction scheme for real-time cine imaging which aims to shorten the required acquisition. A real-time acquisition of 16-20s per acquired slice was inputted into a retrospective cine reconstruction algorithm, which employed non-rigid registration to remove respiratory motion and SPIRiT non-linear reconstruction with temporal regularization to fill in missing data. The algorithm was used to reconstruct cine loops with high spatial (1.3-1.8?×?1.8-2.1 mm²) and temporal resolution (retrospectively gated, 30 cardiac phases, temporal resolution 34.3?±?9.1 ms). Validation was performed in 15 healthy volunteers using two different acquisition resolutions (256?×?144/192?×?128 matrix sizes). For each subject, 9 to 12 short axis and 3 long axis slices were imaged with both segmented and real-time acquisitions. The retrospectively reconstructed real-time cine images were compared to a traditional segmented breath-held acquisition in terms of image quality scores. Image quality scoring was performed by two experts using a scale between 1 and 5 (poor to good). For every subject, LAX and three SAX slices were selected and reviewed in the random order. The reviewers were blinded to the reconstruction approach and acquisition protocols and scores were given to segmented and retrospective cine series. Volumetric measurements of cardiac function were also compared by manually tracing the myocardium for segmented and retrospective cines. RESULTS: Mean image quality scores were similar for short axis and long axis views for both tested resolutions. Short axis scores were 4.52/4.31 (high/low matrix sizes) for breath-hold vs. 4.54/4.56 for real-time (paired t-test, P?=?0.756/0.011). Long axis scores were 4.09/4.37 vs. 3.99/4.29 (P?=?0.475/0.463). Mean ejection fraction was 60.8/61.4 for breath-held acquisitions vs. 60.3/60.3 for real-time acquisitions (P?=?0.439/0.093). No significant differences were seen in end-diastolic volume (P?=?0.460/0.268) but there was a trend towards a small overestimation of end-systolic volume of 2.0/2.5 ml, which did not reach statistical significance (P?=?0.052/0.083). CONCLUSIONS: Real-time free breathing CMR can be used to obtain high quality retrospectively gated cine images in 16-20s per slice. Volumetric measurements and image quality scores were similar in images from breath-held segmented and free breathing, real-time acquisitions. Further speedup of image reconstruction is still needed.
Project description:Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.
Project description:Cardiovascular magnetic resonance (CMR) is increasingly used to assess patients with mitral regurgitation. Its advantages include quantitative determination of ventricular volumes and function and the mitral regurgitant fraction, and in ischemic mitral regurgitation, regional myocardial function and viability. In addition to these, identification of leaflet prolapse or restriction is necessary when valve repair is contemplated. We describe a systematic approach to the evaluation of mitral regurgitation using CMR which we have used in 149 patients with varying etiologies and severity of regurgitation over a 15 month period. Following standard ventricular cine acquisitions, including 2, 3 and 4 chamber long axis views and a short axis stack for biventricular function, we image movements of all parts of the mitral leaflets using a contiguous stack of oblique long axis cines aligned orthogonal to the central part of the line of coaptation. The 8-10 slices in the stack, orientated approximately parallel to a 3-chamber view, are acquired sequentially from the superior (antero-lateral) mitral commissure to the inferior (postero-medial) commissure, visualising each apposing pair of anterior and posterior leaflet scallops in turn (A1-P1, A2-P2 and A3-P3). We use balanced steady state free precession imaging at 1.5 Tesla, slice thickness 5 mm, with no inter-slice gaps. Where the para-commissural coaptation lines curve relative to the central region, two further oblique cines are acquired orthogonal to the line of coaptation adjacent to each commissure. To quantify mitral regurgitation, we use phase contrast velocity mapping to measure aortic outflow, subtracting this from the left ventricular stroke volume to calculate the mitral regurgitant volume which, when divided by the left ventricular stroke volume, gives the mitral regurgitant fraction. In patients with ischemic mitral regurgitation, we further assess regional left ventricular function and, with late gadolinium enhancement, myocardial viability. Comprehensive assessment of mitral regurgitation using CMR is feasible and enables determination of mitral regurgitation severity, associated leaflet prolapse or restriction, ventricular function and viability in a single examination and is now routinely performed at our centre. The mitral valve stack of images is particularly useful and easy to acquire.