Multispectral optoacoustic tomography of peripheral arterial disease based on muscle hemoglobin gradients-a pilot clinical study

Angelos Karlas, Max Masthoff, Michael Kallmayer, Anne Helfen, Michail Bariotakis, Nikolina Alexia Fasoula, Michael Schäfers, Max Seidensticker, Hans-Henning Eckstein, Vasilis Ntziachristos, Moritz Wildgruber, Angelos Karlas, Max Masthoff, Michael Kallmayer, Anne Helfen, Michail Bariotakis, Nikolina Alexia Fasoula, Michael Schäfers, Max Seidensticker, Hans-Henning Eckstein, Vasilis Ntziachristos, Moritz Wildgruber

Abstract

Background: Current imaging assessment of peripheral artery disease (PAD) relies on anatomical cross-sectional visualizations of the affected arteries. Multispectral optoacoustic tomography (MSOT) is a novel molecular imaging technique that provides direct and label-free visualizations of soft tissue perfusion and oxygenation.

Methods: MSOT was prospectively assessed in a pilot trial in healthy volunteers (group n1=4, mean age 31, 50% male and group n3=4, mean age 37.3, 75% male) and patients with intermittent claudication (group n2=4, mean age 72, 75% male, PAD stage IIb). We conducted cuff-induced ischemia (group n1) and resting state measurements (groups n2 and n3) over the calf region. Spatially resolved mapping of oxygenated (HbO2), deoxygenated (Hb) and total (THb) hemoglobin, as well as oxygen saturation (SO2), were measured via hand-held hybrid MSOT-Ultrasound based purely on hemoglobin contrast.

Results: Calf measurements in healthy volunteers revealed distinct dynamics for HbO2, Hb, THb and SO2 under cuff-induced ischemia. HbO2, THb and SO2 levels were significantly impaired in PAD patients compared to healthy volunteers (P<0.05 for all parameters). Revascularization led to significant improvements in HbO2 of the affected limb.

Conclusions: Clinical MSOT allows for non-invasive, label-free and real-time imaging of muscle oxygenation in health and disease with implications for diagnostics and therapy assessment in PAD.

Keywords: Medical imaging; peripheral arterial disease; photoacoustic techniques.

Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-3321). Prof. VN reports to be a stock owner of iThera Medical GmbH, Munich, Germany. The other authors have no conflicts of interest to declare.

2021 Annals of Translational Medicine. All rights reserved.

Figures

Figure 1
Figure 1
MSOT-US principle of operation and data workflow. (A) Calf muscle is illuminated with pulsed light and US waves are generated upon light absorption. Both illumination and US detection take place by means of the same hand-held MSOT scanner. (B) For each near-infrared light pulse, an MSOT and a co-registered US frame are recorded. The frame rate is approximately 25 Hz for MSOT and 8 Hz for US. (C) The recorded raw MSOT data are further unmixed in HbO2 and Hb images and the corresponding total hemoglobin THb (HbO2+Hb) and hemoglobin saturation SO2 (HbO2/THb) are calculated. (D) Finally, a set of 5 images (US, HbO2, Hb, THb, SO2) is produced for the extraction of the image-based biomarkers. (E) Exemplary US image of the calf showing the main anatomical regions: skin surface, subcutaneous fat and muscle area. Upper white dashed line: skin surface. Lower white dashed line: interface between subcutaneous fat and muscle or else muscle line. Muscle area: image area below the muscle line. (F) Schematic of the exemplary US (E) and MSOT (G) images of the calf. (G) Exemplary MSOT image at 800 nm of the same region as in (E). This representation and annotation are followed throughout the text for all skeletal muscle images. Scale bars: 0.5 cm. NIR, near-infrared range; PC, computer; DAQ, data acquisition card; MSOT, multispectral optoacoustic tomography; US, ultrasonography.
Figure 2
Figure 2
Cuff-induced arterial occlusion measurements. (A). Exemplary plots of Hb (deoxygenated hemoglobin), HbO2 (oxygenated hemoglobin), THb (total hemoglobin) and SO2 (oxygen saturation) within the calf skeletal muscle of a healthy volunteer. The blue line represents the Hb changes, the red line represents the HbO2 changes, the purple line the THb changes and the green line the SO2 changes over time. All three lines were normalized to their respective baseline values. B is the baseline period (30 s before the cuff inflation), O1 is the first 60 s of cuff occlusion, O2 is the next 60 s of cuff occlusion, O3 is the last 60 s of cuff occlusion, R1 is the first 60 s after cuff deflation and R2 is the next 60 s after the deflation of the cuff. Statistics for changes of (B) Hb, (C) HbO2, (D) THb and (E) SO2 within the calf muscles of all (n1=4) healthy volunteers for each time period described in (A). For the panels (B,C,D,E), a subject is represented within the box of each time period by the average value of the plotted parameter during the specific time period. The images in (F) show the changes of intramuscular Hb (1st line), HbO2 (2nd line), THb (3rd line) and SO2 (4th line) distribution for the same time periods and subject as in (A). Each image represents the last second of the corresponding time period. The color bars represent the range of the values for a specific parameter for the whole set of the depicted images (the maximum value is the maximum of the set of all image maxima, the minimum value is the minimum of the set of all image minima). Upper white dashed line: skin surface. Lower white dashed line: interface between subcutaneous fat and muscle or else muscle line. Muscle area: Image area below the muscle line. For all images, the intensity of the area above the muscle has been lightly suppressed for visualization purposes. The scale bar is 1 cm.
Figure 3
Figure 3
PAD measurements: comparison between patients and healthy volunteers as well as between patients before and patients after intervention. (A) Statistics for fluctuations in Hb (deoxygenated hemoglobin), (C) statistics for fluctuations in HbO2 (oxygenated hemoglobin), (D) statistics for fluctuations in THb (total hemoglobin) and (E) statistics for fluctuations SO2 (oxygen saturation) within the calf muscles of all (n2=4) patients with PAD before and after intervention, as well as, all (n3=4) healthy volunteers at rest. For the panels (A,B,C,D), each subject is represented by the average value of the plotted parameter within the calf muscle region as measured by means of MSOT. The exemplary images in (E) show the intramuscular Hb, HbO2, THb and SO2 for a patient with PAD before (1st line) and after (2nd line) intervention. ns: statistically non-significant difference, *, P>0.05 (marginally); **, P<0.05. The color bars represent the range of the values for a specific parameter for the both depicted images (the maximum value is the maximum of the both image maxima, the minimum value is the minimum of both image minima). The skeletal muscle region is the area below the white dashed line. For all images, the intensity of the area above the muscle has been lightly suppressed for visualization purposes. The scale bar is 1 cm.

References

    1. Reinecke H, Unrath M, Freisinger E, et al. Peripheral arterial disease and critical limb ischaemia: still poor outcomes and lack of guideline adherence. Eur Heart J 2015;36:932-8. 10.1093/eurheartj/ehv006
    1. Eberhardt RT, Coffman JD. Cardiovascular morbidity and mortality in peripheral arterial disease. Curr Drug Targets Cardiovasc Haematol Disord 2004;4:209-17. 10.2174/1568006043336230
    1. Aday AW, Kinlay S, Gerhard-Herman MD. Comparison of different exercise ankle pressure indices in the diagnosis of peripheral artery disease. Vasc Med 2018;23:541-8. 10.1177/1358863X18781723
    1. Pollak AW, Norton PT, Kramer CM. Multimodality imaging of lower extremity peripheral arterial disease: current role and future directions. Circ Cardiovasc Imaging 2012;5:797-807. 10.1161/CIRCIMAGING.111.970814
    1. Harvey C. Ultrasound with microbubbles. Cancer Imaging 2015;15:O19. 10.1186/1470-7330-15-S1-O19
    1. Suo S, Zhang L, Tang H, et al. Evaluation of skeletal muscle microvascular perfusion of lower extremities by cardiovascular magnetic resonance arterial spin labeling, blood oxygenation level-dependent, and intravoxel incoherent motion techniques. J Cardiovasc Magn Reson 2018;20:18. 10.1186/s12968-018-0441-3
    1. Jones S, Chiesa ST, Chaturvedi N, et al. Recent developments in near-infrared spectroscopy (NIRS) for the assessment of local skeletal muscle microvascular function and capacity to utilise oxygen. Artery Res 2016;16:25-33. 10.1016/j.artres.2016.09.001
    1. Keller E, Nadler A, Alkadhi H, et al. Noninvasive measurement of regional cerebral blood flow and regional cerebral blood volume by near-infrared spectroscopy and indocyanine green dye dilution. Neuroimage 2003;20:828-39. 10.1016/S1053-8119(03)00315-X
    1. Khalil MA, Kim HK, Hoi JW, et al. Detection of Peripheral Arterial Disease Within the Foot Using Vascular Optical Tomographic Imaging: A Clinical Pilot Study. Eur J Vasc Endovasc Surg 2015;49:83-9. 10.1016/j.ejvs.2014.10.010
    1. Karlas A, Reber J, Diot G, et al. Flow-mediated dilatation test using optoacoustic imaging: a proof-of-concept. Biomedical Optics Express 2017;8:3395-403. 10.1364/BOE.8.003395
    1. Yang H, Jüstel D, Prakash J, et al. Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging. Photoacoustics 2020;19:100172. 10.1016/j.pacs.2020.100172
    1. Karlas A, Fasoula NA, Paul-Yuan K, et al. Cardiovascular optoacoustics: From mice to men - A review. Photoacoustics 2019;14:19-30. 10.1016/j.pacs.2019.03.001
    1. Karlas A, Kallmayer M, Fasoula NA, et al. Multispectral Optoacoustic Tomography of Muscle Perfusion and Oxygenation under Arterial and Venous Occlusion: A Human Pilot Study. J Biophotonics 2020;13:e201960169. 10.1002/jbio.201960169
    1. Taruttis A, Ntziachristos V. Advances in real-time multispectral optoacoustic imaging and its applications. Nat Photonics 2015;9:219-27. 10.1038/nphoton.2015.29
    1. Masthoff M, Helfen A, Claussen J, et al. Use of Multispectral Optoacoustic Tomography to Diagnose Vascular Malformations. JAMA Dermatol 2018;154:1457-62. 10.1001/jamadermatol.2018.3269
    1. Masthoff M, Helfen A, Claussen J, et al. Multispectral optoacoustic tomography of systemic sclerosis. J Biophotonics 2018;11:e201800155. 10.1002/jbio.201800155
    1. Reber J, Willershäuser M, Karlas A, et al. Non-invasive Measurement of Brown Fat Metabolism Based on Optoacoustic Imaging of Hemoglobin Gradients. Cell Metabolism 2018;27:689-701.e4. 10.1016/j.cmet.2018.02.002
    1. Roll W, Markwardt NA, Masthoff M, et al. Multispectral optoacoustic tomography of benign and malignant thyroid disorders: a pilot study. J Nucl Med 2019;60:1461-6. 10.2967/jnumed.118.222174
    1. Diot G, Metz S, Noske A, et al. Multispectral Optoacoustic Tomography (MSOT) of Human Breast Cancer. Clin Cancer Res 2017;23:6912-22. 10.1158/1078-0432.CCR-16-3200
    1. Knieling F, Neufert C, Hartmann A, et al. Multispectral Optoacoustic Tomography for Assessment of Crohn's Disease Activity. N Engl J Med 2017;376:1292-4. 10.1056/NEJMc1612455
    1. Becker A, Masthoff M, Claussen J, et al. Multispectral optoacoustic tomography of the human breast: characterisation of healthy tissue and malignant lesions using a hybrid ultrasound-optoacoustic approach. Eur Radiol 2018;28:602-9. 10.1007/s00330-017-5002-x
    1. Ansi Z. American National Standard for Safe Use of Lasers. Laser Institute of America, Orlando; 2000.136.1.
    1. Rosenthal A, Ntziachristos V, Razansky D. Model-based optoacoustic inversion with arbitrary-shape detectors. Med Phys 2011;38:4285-95. 10.1118/1.3589141
    1. Grenon SM, Vittinghoff E, Owens CD, et al. Peripheral artery disease and risk of cardiovascular events in patients with coronary artery disease: Insights from the Heart and Soul Study. Vasc Med 2013;18:176-84. 10.1177/1358863X13493825
    1. Kolls BJ, Sapp S, Rockhold FW, et al. Stroke in Patients With Peripheral Artery Disease. Stroke 2019;50:1356-63. 10.1161/STROKEAHA.118.023534
    1. Shu J, Santulli G. Update on peripheral artery disease: Epidemiology and evidence-based facts. Atherosclerosis 2018;275:379-81. 10.1016/j.atherosclerosis.2018.05.033
    1. Weinberg I, Jaff MR. Nonatherosclerotic arterial disorders of the lower extremities. Circulation 2012;126:213-22. 10.1161/CIRCULATIONAHA.111.060335
    1. Katsui S, Inoue Y, Yamamoto Y, et al. In Patients with Severe Peripheral Arterial Disease, Revascularization-Induced Improvement in Lower Extremity Ischemia Can Be Detected by Laser Speckle Contrast Imaging of the Fluctuation in Blood Perfusion after Local Heating. Ann Vasc Surg 2018;48:67-74. 10.1016/j.avsg.2017.09.022
    1. Yamamoto Y, Inoue Y, Igari K, et al. Assessment of the Severity of Ischaemia and the Outcomes of Revascularisation in Peripheral Arterial Disease Patients Based on the Skin Microcirculatory Response to a Thermal Load Test. EJVES Short Rep 2019;42:21-5. 10.1016/j.ejvssr.2018.12.003
    1. Manfredini F, Lamberti N, Ficarra V, et al. Biomarkers of Muscle Metabolism in Peripheral Artery Disease: A Dynamic NIRS-Assisted Study to Detect Adaptations Following Revascularization and Exercise Training. Diagnostics (Basel) 2020;10:312. 10.3390/diagnostics10050312
    1. Ulrich L, Held KG, Jaeger M, et al. Reliability assessment for blood oxygen saturation levels measured with optoacoustic imaging. J Biomed Opt 2020;25:1-15. 10.1117/1.JBO.25.4.046005
    1. Jeng GS, Li ML, Kim M, et al. Real-time spectroscopic photoacoustic/ultrasound (PAUS) scanning with simultaneous fluence compensation and motion correction for quantitative molecular imaging. bioRxiv 2019. doi: 10.1101/2019.12.20.885251.
    1. Helfen A, Masthoff M, Claussen J, et al. Multispectral Optoacoustic Tomography: Intra- and Interobserver Variability Using a Clinical Hybrid Approach. J Clin Med 2019;8:63. 10.3390/jcm8010063

Source: PubMed

3
Sottoscrivi