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Wuttisarnwattana P, Eck BL, Gargesha M, Wilson DL. Optimal slice thickness for improved accuracy of quantitative analysis of fluorescent cell and microsphere distribution in cryo-images. Sci Rep 2023; 13:10907. [PMID: 37407807 DOI: 10.1038/s41598-023-37927-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/29/2023] [Indexed: 07/07/2023] Open
Abstract
Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (X), fluorescent cell intensity (Ifluo), effective tissue attenuation coefficient (μT), and a detection threshold (T). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability.
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Affiliation(s)
- Patiwet Wuttisarnwattana
- Biomedical Engineering Institute, Department of Computer Engineering, Excellence Center in Infrastructure Technology and Transportation Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Brendan L Eck
- Imaging Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | | | - David L Wilson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Milidonis X, Nazir MS, Schneider T, Capstick M, Drost S, Kok G, Pelevic N, Poelma C, Schaeffter T, Chiribiri A. Pixel-wise assessment of cardiovascular magnetic resonance first-pass perfusion using a cardiac phantom mimicking transmural myocardial perfusion gradients. Magn Reson Med 2020; 84:2871-2884. [PMID: 32426854 PMCID: PMC7611223 DOI: 10.1002/mrm.28296] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/05/2020] [Accepted: 04/02/2020] [Indexed: 01/31/2023]
Abstract
PURPOSE Cardiovascular magnetic resonance first-pass perfusion for the pixel-wise detection of coronary artery disease is rapidly becoming the clinical standard, yet no widely available method exists for its assessment and validation. This study introduces a novel phantom capable of generating spatially dependent flow values to enable assessment of new perfusion imaging methods at the pixel level. METHODS A synthetic multicapillary myocardial phantom mimicking transmural myocardial perfusion gradients was designed and manufactured with high-precision 3D printing. The phantom was used in a stationary flow setup providing reference myocardial perfusion rates and was scanned on a 3T system. Repeated first-pass perfusion MRI for physiological perfusion rates between 1 and 4 mL/g/min was performed using a clinical dual-sequence technique. Fermi function-constrained deconvolution was used to estimate pixel-wise perfusion rate maps. Phase contrast (PC)-MRI was used to obtain velocity measurements that were converted to perfusion rates for validation of reference values and cross-method comparison. The accuracy of pixel-wise maps was assessed against simulated reference maps. RESULTS PC-MRI indicated excellent reproducibility in perfusion rate (coefficient of variation [CoV] 2.4-3.5%) and correlation with reference values (R2 = 0.985) across the full physiological range. Similar results were found for first-pass perfusion MRI (CoV 3.7-6.2%, R2 = 0.987). Pixel-wise maps indicated a transmural perfusion difference of 28.8-33.7% for PC-MRI and 23.8-37.7% for first-pass perfusion, matching the reference values (30.2-31.4%). CONCLUSION The unique transmural perfusion pattern in the phantom allows effective pixel-wise assessment of first-pass perfusion acquisition protocols and quantification algorithms before their introduction into routine clinical use.
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Affiliation(s)
- Xenios Milidonis
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Torben Schneider
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.,Philips Healthcare, Guilford, United Kingdom
| | | | - Sita Drost
- Laboratory for Aero- and Hydrodynamics, Technische Universiteit Delft, Delft, Netherlands
| | | | | | - Christian Poelma
- Laboratory for Aero- and Hydrodynamics, Technische Universiteit Delft, Delft, Netherlands
| | | | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
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Whitaker J, Neji R, Byrne N, Puyol-Antón E, Mukherjee RK, Williams SE, Chubb H, O’Neill L, Razeghi O, Connolly A, Rhode K, Niederer S, King A, Tschabrunn C, Anter E, Nezafat R, Bishop MJ, O’Neill M, Razavi R, Roujol S. Improved co-registration of ex-vivo and in-vivo cardiovascular magnetic resonance images using heart-specific flexible 3D printed acrylic scaffold combined with non-rigid registration. J Cardiovasc Magn Reson 2019; 21:62. [PMID: 31597563 PMCID: PMC6785908 DOI: 10.1186/s12968-019-0574-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 09/02/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Ex-vivo cardiovascular magnetic resonance (CMR) imaging has played an important role in the validation of in-vivo CMR characterization of pathological processes. However, comparison between in-vivo and ex-vivo imaging remains challenging due to shape changes occurring between the two states, which may be non-uniform across the diseased heart. A novel two-step process to facilitate registration between ex-vivo and in-vivo CMR was developed and evaluated in a porcine model of chronic myocardial infarction (MI). METHODS Seven weeks after ischemia-reperfusion MI, 12 swine underwent in-vivo CMR imaging with late gadolinium enhancement followed by ex-vivo CMR 1 week later. Five animals comprised the control group, in which ex-vivo imaging was undertaken without any support in the LV cavity, 7 animals comprised the experimental group, in which a two-step registration optimization process was undertaken. The first step involved a heart specific flexible 3D printed scaffold generated from in-vivo CMR, which was used to maintain left ventricular (LV) shape during ex-vivo imaging. In the second step, a non-rigid co-registration algorithm was applied to align in-vivo and ex-vivo data. Tissue dimension changes between in-vivo and ex-vivo imaging were compared between the experimental and control group. In the experimental group, tissue compartment volumes and thickness were compared between in-vivo and ex-vivo data before and after non-rigid registration. The effectiveness of the alignment was assessed quantitatively using the DICE similarity coefficient. RESULTS LV cavity volume changed more in the control group (ratio of cavity volume between ex-vivo and in-vivo imaging in control and experimental group 0.14 vs 0.56, p < 0.0001) and there was a significantly greater change in the short axis dimensions in the control group (ratio of short axis dimensions in control and experimental group 0.38 vs 0.79, p < 0.001). In the experimental group, prior to non-rigid co-registration the LV cavity contracted isotropically in the ex-vivo condition by less than 20% in each dimension. There was a significant proportional change in tissue thickness in the healthy myocardium (change = 29 ± 21%), but not in dense scar (change = - 2 ± 2%, p = 0.034). Following the non-rigid co-registration step of the process, the DICE similarity coefficients for the myocardium, LV cavity and scar were 0.93 (±0.02), 0.89 (±0.01) and 0.77 (±0.07) respectively and the myocardial tissue and LV cavity volumes had a ratio of 1.03 and 1.00 respectively. CONCLUSIONS The pattern of the morphological changes seen between the in-vivo and the ex-vivo LV differs between scar and healthy myocardium. A 3D printed flexible scaffold based on the in-vivo shape of the LV cavity is an effective strategy to minimize morphological changes in the ex-vivo LV. The subsequent non-rigid registration step further improved the co-registration and local comparison between in-vivo and ex-vivo data.
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Affiliation(s)
- John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Siemens Healthcare Limited, Frimley, UK
| | - Nicholas Byrne
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
- Medical Physics, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK
| | - Esther Puyol-Antón
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Rahul K. Mukherjee
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Henry Chubb
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Louisa O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Adam Connolly
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Kawal Rhode
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Andrew King
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Cory Tschabrunn
- Cardiology Department, University of Pennsylvania, Philadelphia, PA USA
| | - Elad Anter
- Cardiology Department, Beth Israel Deaconess Medical Centre / Harvard Medical School, Boston, MA USA
| | - Reza Nezafat
- Cardiology Department, Beth Israel Deaconess Medical Centre / Harvard Medical School, Boston, MA USA
| | - Martin J. Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, Fourth Floor Lambeth Wing, St Thomas’ Hospital, London, SE1 7EH UK
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Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients. Med Image Anal 2017; 43:169-185. [PMID: 29112879 DOI: 10.1016/j.media.2017.10.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Revised: 08/11/2017] [Accepted: 10/11/2017] [Indexed: 12/24/2022]
Abstract
Abnormal cardiac motion can indicate different forms of disease, which can manifest at different spatial scales in the myocardium. Many studies have sought to characterise particular motion abnormalities associated with specific diseases, and to utilise motion information to improve diagnoses. However, the importance of spatial scale in the analysis of cardiac deformation has not been extensively investigated. We build on recent work on the analysis of myocardial strains at different spatial scales using a cardiac motion atlas to find the optimal scales for estimating different cardiac biomarkers. We apply a multi-scale strain analysis to a 43 patient cohort of cardiac resynchronisation therapy (CRT) patients using tagged magnetic resonance imaging data for (1) predicting response to CRT, (2) identifying septal flash, (3) estimating QRS duration, and (4) identifying the presence of ischaemia. A repeated, stratified cross-validation is used to demonstrate the importance of spatial scale in our analysis, revealing different optimal spatial scales for the estimation of different biomarkers.
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Abstract
A body of finite size is moving freely inside, and interacting with, a channel flow. The description of this unsteady interaction for a comparatively dense thin body moving slowly relative to flow at medium-to-high Reynolds number shows that an inviscid core problem with vorticity determines much, but not all, of the dominant response. It is found that the lift induced on a body of length comparable to the channel width leads to differences in flow direction upstream and downstream on the body scale which are smoothed out axially over a longer viscous length scale; the latter directly affects the change in flow directions. The change is such that in any symmetric incident flow the ratio of slopes is found to be [Formula: see text], i.e. approximately 0.900969, independently of Reynolds number, wall shear stresses and velocity profile. The two axial scales determine the evolution of the body and the flow, always yielding instability. This unusual evolution and linear or nonlinear instability mechanism arise outside the conventional range of flow instability and are influenced substantially by the lateral positioning, length and axial velocity of the body.
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Affiliation(s)
- Frank T Smith
- Department of Mathematics , UCL , Gower Street, London WC1E 6BT, UK
| | - Edward R Johnson
- Department of Mathematics , UCL , Gower Street, London WC1E 6BT, UK
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Sinclair MD, Lee J, Cookson AN, Rivolo S, Hyde ER, Smith NP. Measurement and modeling of coronary blood flow. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:335-56. [PMID: 26123867 DOI: 10.1002/wsbm.1309] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Revised: 05/19/2015] [Accepted: 05/21/2015] [Indexed: 01/10/2023]
Abstract
Ischemic heart disease that comprises both coronary artery disease and microvascular disease is the single greatest cause of death globally. In this context, enhancing our understanding of the interaction of coronary structure and function is not only fundamental for advancing basic physiology but also crucial for identifying new targets for treating these diseases. A central challenge for understanding coronary blood flow is that coronary structure and function exhibit different behaviors across a range of spatial and temporal scales. While experimental studies have sought to understand this feature by isolating specific mechanisms, in tandem, computational modeling is increasingly also providing a unique framework to integrate mechanistic behaviors across different scales. In addition, clinical methods for assessing coronary disease severity are continuously being informed and updated by findings in basic physiology. Coupling these technologies, computational modeling of the coronary circulation is emerging as a bridge between the experimental and clinical domains, providing a framework to integrate imaging and measurements from multiple sources with mathematical descriptions of governing physical laws. State-of-the-art computational modeling is being used to combine mechanistic models with data to provide new insight into coronary physiology, optimization of medical technologies, and new applications to guide clinical practice.
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Affiliation(s)
- Matthew D Sinclair
- Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, King's College London, London, UK
| | - Jack Lee
- Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, King's College London, London, UK
| | - Andrew N Cookson
- Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, King's College London, London, UK
| | - Simone Rivolo
- Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, King's College London, London, UK
| | - Eoin R Hyde
- Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, King's College London, London, UK
| | - Nicolas P Smith
- Division of Imaging Sciences and Biomedical Engineering, British Heart Foundation (BHF) Centre of Excellence, King's College London, London, UK.,Department of Engineering, University of Auckland, Auckland, New Zealand
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