Clement Apelian

Dynamic full field optical coherence tomography: subcellular metabolic contrast revealed in tissues by interferometric signals temporal analysis.

We developed a new endogenous approach to reveal subcellular metabolic contrast in fresh ex vivo tissues taking advantage of the time dependence of the full field optical coherence tomography interferometric signals. This method reveals signals linked with local activity of the endogenous scattering elements which can reveal cells where other OCT-based techniques fail or need exogenous contrast agents. We benefit from the micrometric transverse resolution of full field OCT to image intracellular features. We used this time dependence to identify different dynamics at the millisecond scale on a wide range of organs in normal or pathological conditions.

En face coherence microscopy [Invited]

En face coherence microscopy or flying spot or full field optical coherence tomography or microscopy (FF-OCT/FF-OCM) belongs to the OCT family because the sectioning ability is mostly linked to the source coherence length. In this article we will focus our attention on the advantages and the drawbacks of the following approaches: en face versus B scan tomography in terms of resolution, coherent versus incoherent illumination and influence of aberrations, and scanning versus full field imaging. We then show some examples to illustrate the diverse applications of en face coherent microscopy and show that endogenous or exogenous contrasts can add valuable information to the standard morphological image. To conclude we discuss a few domains that appear promising for future development of en face coherence microscopy.

Extracting relevant information for cancer diagnosis from dynamic full field OCT through image processing and learning

For a large number of cancer surgeries, the lack of reliable intraoperative diagnosis leads to reoperations or bad outcomes for the patients. To deliver better diagnosis, we developed Dynamic Full Field OCT (D-FFOCT) as a complement to FFOCT. FFOCT already presents interesting results for cancer diagnosis e.g. Mohs surgery and reaching 96% accuracy on prostate cancer. D-FFOCT accesses the dynamic processes of metabolism and gives new tools to diagnose the state of a tissue at the cellular level to complement FFOCT contrast. We developed a processing framework that intends to maximize the information provided by the FFOCT technology as well as D-FFOCT and synthetize this as a meaningful image. We use different time processing to generate metrics (standard deviation of time signals, decorrelation times and more) and spatial processing to sort out structures and the corresponding imaging modality, which is the most appropriate. Sorting was achieved through quadratic discriminant analysis in a N-dimension parametric space corresponding to our metrics. Combining the best imaging modalities for each structure leads to a rich morphology image. This image displaying the morphology is then colored to represent the dynamic behavior of these structures (slow or fast) and to be quickly analyzed by doctors. Therefore, we achieved a micron resolved image, rich of both FFOCT ability of imaging fixed and highly backscattering structures as well as D-FFOCT ability of imaging low level scattering cellular level details. We believe that this morphological contrast close to histology and the dynamic behavior contrast will push forward the limits of intraoperative diagnosis further on.

Skin cancer margin analysis with morphologic and metabolic micro-tomography (Conference Presentation)

Non-melanoma skin cancer is the most common cancer. On cosmetically sensitive areas, Mohs micrographic surgery is the standard of care; intra-operative margin assessment minimizes the surgical defect while helping to reduce the recurrence rate by a factor of 3 compared to surgical excision. The current Mohs technique relies on frozen section tissue slide preparation, which significantly lengthens operative time and requires on-site trained histotechnicians. Full-field micro-tomography is a novel optical imaging technique based on interferometry. It allows the extraction of a full-field optical coherence tomography (FFOCT) image, representative of the morphology of the tissue, and the dynamic cell information, representative of the intra-cellular metabolic activity. Both images are calculated at the micron-level in a few minutes and without tissue preparation. This multi-centric study aimed to evaluate these combined new imaging modalities for the analysis of skin cancer margins during Mohs surgery. Over 200 Mohs specimens were imaged in Drexel University College of Medicine, USA, and GHR Mulhouse Sud Alsace, France. An atlas was established of FFOCT images and corresponding histological slides to reveal FFOCT reading criteria of normal and cancerous structures. Dynamic cell information enhanced visualization of cancerous cells and surrounding immune cells, and yielded metabolic quantification of cancerous area versus benign areas. Finally, deep learning algorithms were developed for preliminary results for the automatic detection of cancerous tissue. Hybrid morphologic and metabolic micro-tomography techniques hold great potential for skin cancer margin assessment. They can potentially reduce recurrence rates and surgery times, optimize clinical workflow, and decrease healthcare costs.

Static and dynamic full field OCT: an endogenous biomaker? (Conference Presentation)

Full Field OCT (FFOCT) is a shot noise limited interferometric microscopy technique that uses incoherent light and has proved to be an effective diagnostic tool in terms of sensitivity and specificity. We have used the FFOCT setup built by LLTech for the analysis of various cancerous tissues corresponding to the following organs: breast, skin, prostate, lungs, ENT, bladder, brain etc. The scores obtained were found in the range between 80 and 98%. To do better and to provide informations that the histology does not carry we have studied, using the same setup, the temporal dependence of our signals which we found to be related to the cellular metabolism. We have used the new high speed and high full well capacity of the Adimec camera to achieve a time analysis ranging between 2 and a few thousands of ms. We thus obtain a new contrast which constitutes a biomarker at the sub-cellular scale. We monitor the characteristic frequencies and amplitude of the signal and display them on the images of the tissues using a new processing code of the time series. This metabolic contrast also reveal the evolution of the activity of cancer cells under treatments such as chemotherapy. We will illustrate this new approach through examples of cancer tissues that are planned to be used as intraoperative tools.

Pancreatic cancer study based on full field OCT and dynamic full field OCT (Conference Presentation)

Pancreatic cancer is one of the most feared cancer types due to high death rates and the difficulty to perform surgery. This cancer outcome could benefit from recent technological developments for diagnosis. We used a combination of standard Full Field OCT and Dynamic Full Field OCT to capture both morphological features and metabolic functions of rodents pancreas in normal and cancerous conditions with and without chemotherapy. Results were compared to histology to evaluate the performances and the specificities of the method. The comparison highlighted the importance of a number of endogenous markers like immune cells, fibrous development, architecture and more.

Dynamic full field OCT: metabolic contrast at subcellular level(Conference Presentation)

Cells shape or density is an important marker of tissues pathology. However, individual cells are difficult to observe in thick tissues frequently presenting highly scattering structures such as collagen fibers. Endogenous techniques struggle to image cells in these conditions. Moreover, exogenous contrast agents like dyes, fluorophores or nanoparticles cannot always be used, especially if non-invasive imaging is required. Scatterers motion happening down to the millisecond scale, much faster than the still and highly scattering structures (global motion of the tissue), allowed us to develop a new approach based on the time dependence of the FF-OCT signals. This method reveals hidden cells after a spatiotemporal analysis based on singular value decomposition and wavelet analysis concepts. It does also give us access to local dynamics of imaged scatterers. This dynamic information is linked with the local metabolic activity that drives these scatterers. Our technique can explore subcellular scales with micrometric resolution and dynamics ranging from the millisecond to seconds. By this mean we studied a wide range of tissues, animal and human in both normal and pathological conditions (cancer, ischemia, osmotic shock…) in different organs such as liver, kidney, and brain among others. Different cells, undetectable with FF-OCT, were identified (erythrocytes, hepatocytes…). Different scatterers clusters express different characteristic times and thus can be related to different mechanisms that we identify with metabolic functions. We are confident that the D-FFOCT, by accessing to a new spatiotemporal metabolic contrast, will be a leading technique on tissue imaging and for better medical diagnosis.

Subcellular metabolic contrast in living tissue using dynamic full field OCT (D-FFOCT)(Conference Presentation)

Cells shape or density is an important marker of tissues pathology. However, individual cells are difficult to observe in thick tissues frequently presenting highly scattering structures such as collagen fibers. Endogenous techniques struggle to image cells in these conditions. Moreover, exogenous contrast agents like dyes, fluorophores or nanoparticles cannot always be used, especially if non-invasive imaging is required. Scatterers motion happening down to the millisecond scale, much faster than the fix and highly scattering structures (global motion of the tissue), allowed us to develop a new approach based on the time dependence of the FF-OCT signals. This method reveals hidden cells after a spatiotemporal analysis based on singular value decomposition and wavelet analysis concepts. It does also give us access to local dynamics of imaged scatterers. This dynamic information is linked with the local metabolic activity that drives these scatterers. Our technique can explore subcellular scales with micrometric resolution and dynamics ranging from the millisecond to seconds. By this mean we studied a wide range of tissues, animal and human in both normal and pathological conditions (cancer, ischemia, osmotic shock…) in different organs such as liver, kidney, and brain among others. Different cells, undetectable with FF-OCT, were identified (erythrocytes, hepatocytes…). Different scatterer clusters express different characteristic times and thus can be related to different mechanisms that we identify with metabolic functions. We are confident that the D-FFOCT, by accessing to a new spatiotemporal metabolic contrast, will be a leading technique on tissue imaging and could lead to better medical diagnosis.