Photothermal coherence tomography for 3-D visualization and structural non-destructive imaging of a wood inlay

Tavakolian, Pantea; Сфарра, Стефано; Gargiulo, Giuliano; Sivagurunathan, Koneshwaran; Mandelis, Andreas

doi:10.1016/j.infrared.2018.04.018

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…This technology is an evolution of the original TC-PCT method 27 , which has been shown to be capable of monitoring bone mineral loss 28,29 . The eTC-PCT modality has enabled significantly higher depth-profiling capabilities with industrial materials 26,30,31 compared to the original TC-PCT and to today’s state-of-the-art dynamic photothermal imaging technologies, namely lock-in thermography and photothermal radar. Notably, the capability of eTC-PCT for biomedical imaging, and its advantage over TC-PCT in this application, has been recently demonstrated for in-vivo tumor detection in a mouse 32 .…”

Section: Introductionmentioning

confidence: 99%

3D Dental Subsurface Imaging Using Enhanced Truncated Correlation-Photothermal Coherence Tomography

Roointan

Tavakolian

Sivagurunathan

et al. 2019

Sci Rep

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Development of accurate and sensitive dental imaging technologies is a top priority in the pursuit of high-quality dental care. However, while early dental caries detection and routine monitoring of treatment progress are crucial for effective long-term results, current radiographic technologies fall short of this objective due to low sensitivity for small lesions and use of ionizing radiation which is unsuitable for frequent monitoring. Here we demonstrate the first application of enhanced Truncated Correlation-Photothermal Coherence Tomography (eTC-PCT) to dental imaging. eTC-PCT is non-invasive and non-ionizing, operates well below the maximum permissible exposure (MPE) limit, and features 3D subsurface imaging capability with operator controlled axial resolution. We explore the potential of this method for dental applications and demonstrate its capability for depth-resolved tomographic 3D reconstructions of the details and subsurface extent of a variety of dental defects. To this end, in this proof-of-concept study, dental eTC-PCT imaging results, and its sensitivity to dental caries, are discussed in comparison with visual examination, x-rays and micro-CT imaging.

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Section: Introductionmentioning

confidence: 99%

3D Dental Subsurface Imaging Using Enhanced Truncated Correlation-Photothermal Coherence Tomography

Roointan

Tavakolian

Sivagurunathan

et al. 2019

Sci Rep

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“…To understand how zero-padding can be used to linearize the CC, it is easiest to think of the linear, Equation (9), and circular, Equation (10), operations in the discrete time domain. It is important to distinguish between the two, because when the CC is transformed to the frequency domain, circular CC is always used due to the periodic nature of the discrete Fourier transform [6]:…”

Section: Tc-pct Signal Processingmentioning

confidence: 99%

“…In-phase, quadrature, (A) amplitude and (B) phase responses for a 0.2 to 0.6 Hz 12-s chirp from experimental data. The dashed red lines indicate the truncation region, outside of which, the responses are discarded as they contain aliased-pulse artifacts that was previously imaged using eTC-PCT [10] was chosen due to the complex multi-layer and hidden defect structure within the sample. With a known geometry and abrupt changes in thermal properties, it was deemed to be an ideal sample to test the depth resolution and reconstruction accuracy of the optimized and simplified algorithms.…”

Section: Multi-layered Sample Imagingmentioning

confidence: 99%

Three‐dimensional thermophotonic image optimization modalities of truncated correlation photothermal coherence tomography

Welch

Sivagurunathan

Tavakolian

et al. 2022

Journal of Biophotonics

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“…IRT presents the advantageous features that correspond to a Non-Destructive Testing (NDT) technique, i.e., (i) non-intrusion and non-damage to the integrity of the object as opposed to destructive techniques, and (ii) higher objectivity and speed compared to traditional methods [ 5 ]. In addition, IRT presents added advantageous features such as: (i) The non-contact with the heritage object, (ii) the real time operation, very important for cultural heritage monitoring, (iii) the ability to analyze any surface regardless of the type of object, (iv) the possibility to monitor many points of an object at the same time, (v) the capacity to perform large-scale studies of objects, (vi) the interpretation of the results in two and three dimensions [ 6 , 7 ], and (vii) the possibility of a qualitative and quantitative analysis [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Garrido

Erazo-Aux

Lagüela

et al. 2021

Sensors

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The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

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Photothermal coherence tomography for 3-D visualization and structural non-destructive imaging of a wood inlay

Cited by 13 publications

References 12 publications

3D Dental Subsurface Imaging Using Enhanced Truncated Correlation-Photothermal Coherence Tomography

3D Dental Subsurface Imaging Using Enhanced Truncated Correlation-Photothermal Coherence Tomography

Three‐dimensional thermophotonic image optimization modalities of truncated correlation photothermal coherence tomography

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

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