TY - JOUR
AU - Athanasiou,, Thanos
AB - Abstract Open in new tabDownload slide Open in new tabDownload slide The death rate from thoracic aortic disease is on the rise and represents a growing global health concern as patients are often asymptomatic before acute events, which have devastating effects on health-related quality of life. Biomechanical factors have been found to play a major role in the development of both acquired and congenital aortic diseases. However, much is still unknown and translational benefits of this knowledge are yet to be seen. Phase-contrast cardiovascular magnetic resonance imaging of thoracic aortic blood flow has emerged as an exceptionally powerful non-invasive tool enabling visualization of complex flow patterns, and calculation of variables such as wall shear stress. This has led to multiple new findings in the areas of phenotype-dependent bicuspid valve flow patterns, thoracic aortic aneurysm formation and aortic prosthesis performance assessment. Phase-contrast cardiovascular magnetic resonance imaging has also been used in conjunction with computational fluid modelling techniques to produce even more sophisticated analyses, by allowing the calculation of haemodynamic variables with exceptional temporal and spatial resolution. Translationally, these technologies may potentially play a major role in the emergence of precision medicine and patient-specific treatments in patients with aortic disease. This clinically focused review will provide a systematic overview of key insights from published studies to date. Magnetic resonance imaging, Phase-contrast magnetic resonance imaging, Thoracic aorta disease INTRODUCTION The evolutionary development of the circulatory system in vertebrates has contributed significantly to the success of our species. It plays a key role in gas exchange, energy distribution and excretion of waste products. The development of endothelium in our ancestors over half a billion years ago helped further optimize flow dynamics, barrier function, coagulation and localized immune responses [1]. Although circulatory system developments were driven by the time and distance constraints of diffusion, most evolutionary progress has been associated with trade-offs and limitations. Abnormal cardiovascular biomechanical forces have been implicated in many pathological conditions including atherosclerosis, aortic dissection and bicuspid aortic valve (BAV)-mediated aortic dilatation [2]. There has been a growing interest among scientists in studying these biomechanical factors over the past 50 years. This progress has been enabled by the rapid development of techniques within fields such as non-invasive imaging, endothelial cell biology, genomics, longitudinal clinical research and multidisciplinary approaches combining engineering with imaging and vascular biology. Although most developments have occurred in the 21st century, the relationship between flow and physiological function was hypothesized by great philosophers such as Leonardo Da Vinci in the 16th century and Rudolf Virchow in the 19th century [3]. Da Vinci famously postulated that the sinuses of Valsalva play a key role in creating retrograde flow and vortices that aid aortic valve closure and coronary perfusion in diastole (illustrated together with modern day imaging techniques in Fig. 1). Virchow highlighted the spatial relationship between abnormal blood flow and atherosclerotic lesions, long before this was proven more substantially by the work of modern-day scientists [4]. Figure 1: Open in new tabDownload slide (A) Velocity-encoded images showing laminar flow through the aortic valve in a healthy male volunteer. (B) Leonardo Da Vinci’s illustration of vortices in the sinuses of Valsalva. (C) Leonardo’s highly accurate illustration of vortices in the aortic root, consistent with an image from modern-day computational fluid dynamics techniques [from Bissell et al. [5], published by Oxford University Press (STM Signatory)]. Figure 1: Open in new tabDownload slide (A) Velocity-encoded images showing laminar flow through the aortic valve in a healthy male volunteer. (B) Leonardo Da Vinci’s illustration of vortices in the sinuses of Valsalva. (C) Leonardo’s highly accurate illustration of vortices in the aortic root, consistent with an image from modern-day computational fluid dynamics techniques [from Bissell et al. [5], published by Oxford University Press (STM Signatory)]. This review is focused on phase-contrast cardiovascular magnetic resonance imaging (PC-CMR) of the thoracic aorta. Techniques in this field are developing at an astounding pace, and have emerged as exceptionally powerful ways of assessing thoracic aortic blood flow for both scientists and clinicians. They are translationally relevant given the increasing burden and prevalence of ‘silent, but lethal’ thoracic aortic disease, in both the developed and developing countries [6]. This clinical review focuses on all studies that assess thoracic aortic blood flow patterns with PC-CMR and related techniques such as computational fluid dynamics (CFD). The readers interested in the processes involved are directed to the included supplement that provides an overview of the workflows involved in PC-CMR and CFD analysis (Supplementary Material 1). METHODS The Medline and Google Scholar databases (up to January 2018) were used to search for clinically focused studies in English, which assessed blood flow patterns in the thoracic aorta of adults using PC-CMR ± CFD. The following search algorithm was used: (‘4d mri’ or ‘4d flow mri’ or ‘time resolved 3d mri’ or ‘2d mri’ or ‘2d flow mri’ or ‘phase contrast mri’ or ‘computational modelling’ or ‘flow sensitive mri’ or ‘computational fluid dynamics’) and (‘thoracic aorta’ or ‘ascending aorta’ or ‘aortic root’ or ‘aortic arch’ or ‘descending thoracic aorta’) The reference lists of key papers identified through this process were also checked for relevant references. Studies were only included if they used patient-specific boundary conditions (i.e. flow was modelled using the patients own blood flow characteristics, and not just a uniform flow pattern). The following studies were excluded: studies published before the year 2000, patients focusing purely on magnetic resonance imaging (MRI) and CFD technique methodology, patients based on idealized (i.e. non-patient-specific) boundary conditions, repetitive studies from the same group, studies with <5 patients (MRI papers) or 2 simulations (CFD papers) with aortic pathology, and studies based on computed tomography image. Description of scientific terms used in papers A number of scientific terms are used in papers. A brief description of the key terms is given in order to help interpretation of the subsequent results. Wall shear stress (WSS). Within the aorta, there are 2 categories of stresses on the wall. First, there is WSS due to the impact of blood flow immediately next to the vessel wall. Second, there are circumferential, axial and radial stresses on the vessel wall because of pulse pressure variation generated by cyclical blood flow. There are 2 key differences in these 2 categories of stresses. First, WSSs are applied to the superficial layer of the internal lumen of the aorta (i.e. the endothelium/intimal layers), whereas circumferential, axial and radial stresses are transferred to all vessel wall layers. Second, WSS is significantly smaller in magnitude as compared with circumferential stress. Despite this difference in magnitude, there has been significantly more research interest in WSS because of its role in altering endothelial cell biology, and the wide-ranging impact of this. Oscillatory shear stress/index (OSI). OSI is a way of assessing the cyclic variation of WSS. It assesses how much the WSS vector deviates from its predominant axial direction of flow during 1 cardiac cycle. Its range is between 0 and 0.5, with 0 indicating that WSS is directed in the primary direction of blood flow and 0.5 that the instantaneous vector of flow is never aligned with the overall time-averaged vector, indicating oscillatory behaviour. Reynolds number. The state of turbulence of blood flow is often described in terms of the Reynolds number (Re), which is a dimensionless parameter that describes the ratio of inertial to viscous flows. Helical flow. Helical flow is the cork-screw-like flow of blood in the aorta. Vortical flow. Vortical flow is a region in the blood in which the flow revolves around an axis line, which may be straight or curved. RESULTS A total of 83 studies are summarized in Supplementary Material 2, Tables S1–S6. These are categorized into the following studies: assessing blood flow patterns in healthy patients (7 studies), aortic valve disease (26 studies), thoracic aortic aneurysmal disease (14 studies), aortic coarctation (6 studies), aortic dissection (6 studies) and related to aortic prosthesis implantation (24 studies). If studies included multiple patient groups, they were categorized according to the size of the largest subgroup. A summary of these studies demonstrated by each of these categories is given below. Healthy patients A total of 7 studies were found which examined flow patterns in healthy patients of varying age groups; all of them used 4D flow MRI (Supplementary Material, Table S1), and none used CFD. Several typical and consistent findings were observed across age groups. Great segmental variation in patterns of WSS were observed throughout the aorta, with the typical pattern showing a region of high WSS at the outer curve, and a region of low WSS (with high OSI) at the inner curve of the ascending aorta (AA). The region of high WSS was seen to move to the outer curve of the arch, and inner curve of the descending aorta. In addition, the atherogenic pattern of low WSS and high OSI was commonly observed at the origin of the head and neck vessels [7, 8]. One group created population-averaged reference maps of WSS, enabling the easy visualization of patients who have abnormal segmental regions of WSS [7]. Helical flow was considered a normal finding in most volunteers, with the common pattern being right-handed in the AA and left-handed in the descending thoracic aorta [7, 9]. Helical flow tended to appear in the AA following peak net forwards flow, with more coherence during the deceleration phase in the distal AA and proximal arch. There was very little evidence of vortical flow in young patients, but this increased with age. This finding was consistent with the fact that the elderly showed lower systolic and higher retrograde velocities than the young [7, 10]. Figure 2 shows the observation of 2 commonly described secondary flow patterns: helix and vortex formation. Figure 2: Open in new tabDownload slide Secondary flow patterns with helicity seen in (A) and (B) and vortices depicted in (C) and (D) [from Frydrychowicz et al. [10], published by Springer Publishing Company (STM Signatory)]. Figure 2: Open in new tabDownload slide Secondary flow patterns with helicity seen in (A) and (B) and vortices depicted in (C) and (D) [from Frydrychowicz et al. [10], published by Springer Publishing Company (STM Signatory)]. One study examined the regional differences in flow instability, through calculation of the Reynolds number. Flow instability (based on critical Reynolds number defining instability) was seen in over half of healthy patients and observed more commonly in men. Instability was highest in the AA, followed by the descending thoracic aorta and uncommonly seen in the arch [11]. Only 1 study examined the influence of aortic geometry on flow patterns in the healthy, and surprisingly found it had less influence on flow patterns than age and aortic diameter [12]. Finally, 1 study observed differences of flow velocity and velocity distribution in different age groups and between genders. There were significantly different peak velocities between age groups, and >40-year olds had greater velocity distribution than <21-year olds. Significant differences in all parameters were observed between men and women [13]. Aortic valve disease Altered flow patterns secondary to aortic valve disease was the commonest studied area, with a total of 26 studies found, of which 3 used CFD (Supplementary Material, Table S2). Most studies explored the influence of BAV, through comparisons with age- and size-matched controls. The peak and mean WSS was universally higher in patients with BAV [14–22]. WSS patterns were also more circumferentially heterogeneous in this population in terms of position and magnitude [22–24]. Authors often suggested that this was because of the BAV patients having a greater systolic flow angle, eccentric outflow jets and flow asymmetry due to restricted leaflet opening [15, 16, 19, 24–28]. Generally, patients with right–left BAV had distinct impinging jet flow patterns leading to regions of highest peak WSS on the right anterior aspect of the AA [17, 19, 20, 25, 29, 30]. However, patients with right–non-BAV had impingement and regions of highest WSS on the right and left posterior aspects of the AA [17, 19, 25, 30]. The right–non-group was also associated with more severe flow abnormalities than right–left in 3 studies [15, 27, 28]. Figure 3 shows the visualization of blood flow in bicuspid patients as compared with healthy controls. A single novel study by Guzzardi et al. [31] examined the relationship between WSS and tissue remodelling in BAV patients by assessing histology and protein expression in resected aortic specimens—regions of increased WSS corresponded with extracellular matrix dysregulation and elastic fibre degeneration. Figure 3: Open in new tabDownload slide Visualization of blood flow in bicuspid valves as compared with controls [from Mahadevia et al. [19], published by Wolters Kluwer Health—Lippincott Williams & Wilkins (STM Signatory)]. (A) Healthy volunteer, (B) aortia size control, (C) RL-BAV and (D) RN-BAV. AAo: ascending aorta; BAV: bicuspid aortic valve; DAo: descending aorta; LVOT: left ventricular out flow tract; RL: right-left; RN: right-non. Figure 3: Open in new tabDownload slide Visualization of blood flow in bicuspid valves as compared with controls [from Mahadevia et al. [19], published by Wolters Kluwer Health—Lippincott Williams & Wilkins (STM Signatory)]. (A) Healthy volunteer, (B) aortia size control, (C) RL-BAV and (D) RN-BAV. AAo: ascending aorta; BAV: bicuspid aortic valve; DAo: descending aorta; LVOT: left ventricular out flow tract; RL: right-left; RN: right-non. BAV patients were also observed to have significantly greater abnormal helical flow, in terms of both quantity and magnitude [15–17, 22, 32, 33]. This helical flow was often observed to be right-handed in the AA (similar to in the healthy), but more commonly seen to be left-handed in patients with right–non-valves [15, 17]. Type-2 aortopathy was more prevalent in patients with right–left, whereas type-1/3 aortopathy was more common in patients with right–non-BAV [19, 20, 30]. Several studies examined the influence of additional aortic valve disease on flow patterns in patients with BAV. The addition of aortic stenosis or regurgitation tended to increase the peak and mean WSS further [14, 15, 22, 27], as did aortic diameter [14, 23]. In patients with bicuspid regurgitant valves, WSS patterns tended to be increased significantly, whereas in patients with bicuspid stenotic valves, WSS patterns had more eccentric patterns [20]. A single study examined the influence of β-blocker therapy in patients with BAV and found that WSS and peak velocity were not significantly different in the treatment group [14]. Only 2 small studies longitudinally assessed the influence of flow patterns on the long-term outcomes and found that both the conjoint cusp opening angle and abnormal flow patterns predicted aortic growth at follow-up [24, 34]. A study by Allen et al. [35] suggests that left ventricular abnormalities (hypertrophic obstructive cardiomyopathy) may also generate abnormal flow patterns and showed that helical flow in the AA is significantly higher in patients with basal-septal phenotype. The severity of changes was found to correlate with the outflow tract gradient and the presence of systolic anterior motion of the mitral valve. A study by Schnell et al. [36] also suggested that aortic geometry (gothic and cubic arch) may influence the formation of vortex and helix formation. Interestingly, such helix- and vortex-inducing geometry was more common in first-degree relatives (with TAV) of patients with BAV. Thoracic aortic aneurysmal disease Fourteen studies examined flow patterns in patients with thoracic aortic disease (none of them used CFD), predominantly focusing on the proximal aorta (Supplementary Material, Table S3). Two key patterns were observed in several studies. First, the peak systolic WSS in the AA was lower in patients with aneurysms and often seen to be inversely related to aortic size [37–39]. However, time-averaged WSS was found to be higher in these patients than in healthy controls due to a decreased systolic-to-diastolic WSS ratio and late onset of peak WSS [37, 38]. The highest peak WSS was often observed on the anterior wall of the proximal aorta and the lowest one on the proximal inner curve, and the right outer curve just before the arch [37–40]. One study suggested that the addition of valvular disease increases WSS further [39]. In patients with Marfan syndrome, eccentric WSS patterns were often observed, even in the absence of aneurysmal or valve disease. Patients with Marfan syndrome were also observed in 2 studies to have greater eccentricity of WSS patterns, which was highest at the inner curve of the proximal aorta, and more anteriorly in its distal part [41, 42]. The higher OSI was observed at the STJ and in the arch, whereas the lower OSI was observed in the descending thoracic aorta [41, 42]. Second, patients with proximal aortic aneurysms had significantly greater frequency of helical and vortical flow. This was often related to aneurysm size, with appearance often occurring earlier in the cardiac cycle as compared with healthy controls [37, 38, 41, 43–45]. Given this fact, it was unsurprising to see that greater viscous energy loss and retrograde flow were also observed in some studies [43, 46]. In patients with Marfan syndrome, greater frequency of helical and vortical flows was also observed, even in those without any evidence of aneurysm or valve disease [41]. Aortic coarctation Six studies (of which 4 used CFD) examined flow patterns in patients pre- and post-treatment for aortic coarctation (Supplementary Material, Table S4). The majority of these suggested that patients have the following differences as compared with healthy individuals: greater evidence of helix and vortex formation (especially in the proximal aorta, the head and neck vessels, and in the region of the coarctation), greater turbulence, greater WSS magnitude in the proximal aorta, and raised WSS or OSI in the region of the coarctation [47–51]. Such changes were often noted to be related to the degree of coarctation stenosis severity [51] and were often still observed following coarctation repair [47, 48]. Aortic dissection Six studies (of which 4 used CFD) examined flow patterns in patients with mostly type-B dissection (Supplementary Material, Table S5). They showed that around the point of the primary entry tear, there is often a region of increased velocity, WSS, pressure differential and flow abnormalities [52–54]. Within the false lumen, slower velocities were observed, with more disturbed and complex flow patterns including vortex formation and systolic retrograde flow which occurred earlier than in the true lumen [54–56]. While these findings improved following stent treatment in 2 studies, flow abnormalities were often still present especially at the proximal end of the stent graft [53, 56]. Aortic prosthesis insertion Twenty-four studies (of which 7 used CFD) examined flow patterns following a range of mostly proximal aortic intervention (Supplementary Material, Table S6). Many of these studies examined flow patterns in patients following aortic valve replacement (AVR) as compared with healthy or presurgical controls. These studies showed 2 key findings. First, while post-AVR patients often showed less-turbulent flow than presurgical controls [57], flow was often still eccentric as compared with healthy controls [58]. Second, the choice of AVR prosthesis had a clear impact, with some studies suggesting that mechanical valves produce more vorticity, and bioprosthetic valves more helicity [58]. The design of mechanical AVRs was reflected in their flow patterns—St. Jude mechanical prostheses showed flow acceleration corresponding to jet orifices, and the development of reverse flow and vortices around the hinge housing [59, 60]. Furthermore, 1 study used CFD to compare bioprosthetic and mechanical valves, showing higher WSS, transvalvular pressure drop and turbulent kinetic energy with bioprostheses [61]. One study suggested that more modern mechanical valve designs (On-X) produce more physiological flow patterns, more similar to that of healthy controls [62]. Bioprostheses design also affected luminal velocity—higher central but lower outer lumen velocities were observed with stentless bioprostheses as compared with stented valves [63]. Several studies also compared flow patterns in patients following aortic root replacement, either with composite-valved prostheses or valve-sparing root replacement (Fig. 4). Valve-sparing root replacement appeared to produce less asymmetry in flow, less helix formation and superior haemodynamics as compared with aortic root replacement [60, 64–67]. However, both groups did not appear to be completely comparable to healthy controls, because of evidence of higher velocities, acceleration, jet formation, greater retrograde flow and non-physiological helix and vortex formation [65, 67–73]. The material properties of grafts clearly had an impact on flow patterns, as aortic geometry was often not normal following intervention, with evidence of energy loss and high levels of WSS at the distal anastomosis [64, 68] or distal to graft [74]. One novel study examined the impact of implantation of a personalized external aortic support (PEARS) for Marfan syndrome. CFD analysis suggested that preoperative high levels of peak stress present in the proximal aorta were transferred to the arch after implantation of the support, especially in the region of supported and unsupported aorta [75–77]. Figure 4: Open in new tabDownload slide Visualization of blood flow in valve-sparing root replacement (A and B) versus aortic root replacement (C) and controls (D) [from Collins et al. [69], published by Elsevier (STM Signatory)]. Figure 4: Open in new tabDownload slide Visualization of blood flow in valve-sparing root replacement (A and B) versus aortic root replacement (C) and controls (D) [from Collins et al. [69], published by Elsevier (STM Signatory)]. Limitations The limitations of these technologies must also be acknowledged. PC-CMR has limited temporal and spatial resolution. Several variables it produces are estimations, with accuracy dependent on acquisition parameters and multiple mathematical assumptions. The accuracy and reliability of WSS (which is estimated by near-wall velocity gradients) has been questioned given the challenges in vessel wall segmentation. Because of the low spatial resolution of MRI, blood flow velocities cannot be measured any closer to the aortic wall than 1000–1200 µm (affecting the calculation of velocity gradients), which is why the technique underestimates WSS. The temporal resolution of MRI also limits the ability to study the true dynamic and subtle nature of WSS patterns in animal vessels. In addition, the technical quality of scans can be compromised by artefact from prostheses, and arrhythmias. CFD deals with some of these issues but comes at incredible computational cost. It also calculates variables based on multiple assumptions (e.g. it is often assumed that the aortic wall is rigid). Further technological advancements are required to address these limitations. CONCLUSIONS Our understanding behind the haemodynamic and biomechanical factors influencing aortic disease has increased exponentially over the past 50 years. PC-CMR and CFD have emerged as valuable non-invasive technologies for assessing aortic flow patterns in complex detail, both quantitatively and qualitatively. On the basis of the published literature, these technologies have led to 3 main benefits: First, it has led to several new findings which go beyond what scientists have learnt from other technologies such as Doppler ultrasound and hot film anemometry. Key advances have been made in the characterization of phenotype-dependent flow patterns in BAV, creation of population norm WSS maps, better descriptions of physiological and non-physiological helical flow patterns, and significantly more complex understanding behind the non-valvular factors influencing blood flow (e.g. LV outflow tract anatomy and aortic geometry). A comparative assessment of different aortic interventions (such as AVR, aortic root replacement, valve-sparing root replacement and endovascular stent insertion) has highlighted prosthesis-related factors that lead to both the physiological and non-physiological blood flows. Second, the colour visualization of blood flow patterns has helped scientists and clinicians understanding of complex blood flow, which, we believe, has increased both creativity and interest from other disciplines and the public. Third, we believe that in the very near future these technologies will support precision medicine developments and allow patients with aortic disease to be treated on an individual basis guided by the variables such as WSS, peak wall stress, flow displacement and others. Despite the above facts, most current MRI and CFD aortic research is only hypothesis generating. Most studies are cross-sectional and small to medium in size, without longitudinal follow-up. Before benefits can truly be reaped, further large population-based studies are required from which normalized population maps and ranges can be generated for the rich quantitative and qualitative variables produced. Consequently, longitudinal studies will be required to assess whether these variables are truly predictive of certain end points of interest. Correlation of these with patient-reported outcome measures and changes at an omics level has great potential to further develop this field. From a practical point of view, it is unclear how and which of these imaging technologies will transition in to the aortic clinic and multidisciplinary team meetings. Funding This work was jointly supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (based at Imperial College London and Imperial College Healthcare NHS Trust) and the NIHR Cardiovascular Biomedical Research Unit (based at the Royal Brompton and Harefield NHS Foundation Trust). S.P. is supported by the H2020 EU Project MUSICARE—Multisectoral Integrative Approaches to Cardiac Care, Project ID MSCA-ITN 642458. Conflict of interest: none declared. Author contributions Omar A. 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Phase-contrast magnetic resonance imaging and computational fluid dynamics assessment of thoracic aorta blood flow: a literature review
JF - European Journal of Cardio-Thoracic Surgery
DO - 10.1093/ejcts/ezz280
DA - 2020-03-01
UR - https://www.deepdyve.com/lp/oxford-university-press/phase-contrast-magnetic-resonance-imaging-and-computational-fluid-0ZIdLfMGc0
SP - 438
VL - 57
IS - 3
DP - DeepDyve
ER -