Literature DB >> 32057188

Handheld volumetric manual compression-based quantitative microelastography.

Qi Fang1,2, Luke Frewer1,2, Renate Zilkens1,3, Brooke Krajancich1,2,4, Andrea Curatolo1,2,5, Lixin Chin1,2, Ken Y Foo1,2, Devina D Lakhiani1,2, Rowan W Sanderson1,2, Philip Wijesinghe1,2,6, James D Anstie1,2, Benjamin F Dessauvagie7,8, Bruce Latham7,9, Christobel M Saunders3,10,11, Brendan F Kennedy1,2,12.   

Abstract

Compression optical coherence elastography (OCE) typically requires a mechanical actuator to impart a controlled uniform strain to the sample. However, for handheld scanning, this adds complexity to the design of the probe and the actuator stroke limits the amount of strain that can be applied. In this work, we present a new volumetric imaging approach that utilizes bidirectional manual compression via the natural motion of the user's hand to induce strain to the sample, realizing compact, actuator-free, handheld compression OCE. In this way, we are able to demonstrate rapid acquisition of three-dimensional quantitative microelastography (QME) datasets of a tissue volume (6 × 6 × 1 mm3 ) in 3.4 seconds. We characterize the elasticity sensitivity of this freehand manual compression approach using a homogeneous silicone phantom and demonstrate comparable performance to a benchtop mounted, actuator-based approach. In addition, we demonstrate handheld volumetric manual compression-based QME on a tissue-mimicking phantom with an embedded stiff inclusion and on freshly excised human breast specimens from both mastectomy and wide local excision (WLE) surgeries. Tissue results are coregistered with postoperative histology, verifying the capability of our approach to measure the elasticity of tissue and to distinguish stiff tumor from surrounding soft benign tissue.
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  freehand volumetric imaging; handheld probe; optical coherence elastography; optical coherence tomography; quantitative microelastography

Mesh:

Year:  2020        PMID: 32057188     DOI: 10.1002/jbio.201960196

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  4 in total

1.  Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning.

Authors:  Ken Y Foo; Kyle Newman; Qi Fang; Peijun Gong; Hina M Ismail; Devina D Lakhiani; Renate Zilkens; Benjamin F Dessauvagie; Bruce Latham; Christobel M Saunders; Lixin Chin; Brendan F Kennedy
Journal:  Biomed Opt Express       Date:  2022-05-12       Impact factor: 3.562

2.  Analysis of strain estimation methods in phase-sensitive compression optical coherence elastography.

Authors:  Jiayue Li; Ewelina Pijewska; Qi Fang; Maciej Szkulmowski; Brendan F Kennedy
Journal:  Biomed Opt Express       Date:  2022-03-18       Impact factor: 3.562

3.  Smartphone-based optical palpation: towards elastography of skin for telehealth applications.

Authors:  Rowan W Sanderson; Qi Fang; Andrea Curatolo; Aiden Taba; Helen M DeJong; Fiona M Wood; Brendan F Kennedy
Journal:  Biomed Opt Express       Date:  2021-05-06       Impact factor: 3.732

4.  Repetitive optical coherence elastography measurements with blinking nanobombs.

Authors:  Paul Boerner; Dmitry Nevozhay; Maryam Hatamimoslehabadi; Harshdeep Singh Chawla; Fernando Zvietcovich; Salavat Aglyamov; Kirill V Larin; Konstantin V Sokolov
Journal:  Biomed Opt Express       Date:  2020-10-22       Impact factor: 3.562

  4 in total

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