Laboratoire de Physique des Interfaces et des Couches Minces

CNRS - École polytechnique - Institut Polytechnique de Paris

Software engineer, contract for 2 years, start – April, 2024

 Angelo Pierangelo 

Phone : +33-1-46-69-43-69

Tatiana Novikova

Phone : +33 6 72 14 46 58, +33 1 69 33 43 41


Full-field multispectral Mueller polarimetric imaging for improved surgery of neurological malignancies

Keywords: Optical imaging – Polarized light; Mueller polarimetry – Signal processing – Cancer surgery – Surgical microscope – Surgical exoscope.

Laboratory: Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), Ecole Polytechnique, 91128 Palaiseau (France).

Postdoctoral fellowship (2 years, start – April 2024) at LPICM, Ecole polytechnique, Palaiseau, France within the framework of the SNF Sinergia HORAO project

Problem. Surgery is the crucial step in the treatment of brain tumors, in particular gliomas. While some well-defined tumors, such as metastases, can be removed en bloc, the majority of gliomas, which tend to grow infiltrative in the white matter of brain, are removed in piecemeal. During the surgery, it is essential to identify and respect the boundary between brain tumor and surrounding healthy brain tissue in order to carry out a radical resection of the pathological parts while preserving neurological function. However, solid tumor tissue is often difficult to differentiate from infiltrated white matter during surgery, even using a state-of-the-art intraoperative microscope. A non-complete tumor resection due to poor visualization of tumor margins leads to a worse prognosis for the patients, as the tumors invariably grow back from the remnants. Several imaging techniques (e.g. fluorescence imaging, ultrasound and magnetic resonance imaging) have been implemented for the intraoperative visualization of brain tumors tissue, but all have some drawbacks. In summary, the efforts to visualize brain tumor and reliably identify the interface between healthy and pathological areas during neurosurgery have so far failed for many intrinsic brain tumors.

Approach. Imaging Mueller polarimetry is an optical technique that has been widely used for the studies of biological tissues representing complex depolarizing and optically anisotropic media. This technique is very sensitive to microstructural changes in biological tissues induced by pathology. It is known that brain tumors destroy the highly anisotropic structure characteristic of healthy white matter, made up of very dense, well-organized axon fibers. Accurate detection of the healthy white matter can help surgeons to better identify, by contrast, the pathological areas. In recent years, the optical team of LPICM developed several Mueller polarimetric imaging systems (e. g. multispectral Mueller Colposcope, Mueller laparoscope) that have shown great promise for improving in vivo detection of cancerous lesions, as well as for accurately defining the resection margins of pathological areas during surgery. Prior results of a study conducted within the framework of SNF Sinergia HORAO project by the LPICM, Ecole polytechnique, France and Bern University Hospital, Bern, Switzerland demonstrated that wide-field Mueller imaging system can provide detailed maps of the axonal fiber orientation in healthy brain and can effectively distinguish between healthy and cancerous areas of the brain in just a few seconds. The miniaturized Mueller imaging system based on polarization-sensitive camera will be built for real-time intraoperative use during neurosurgery.

 Responsibilities. The main objective of the software engineer’s work is to develop software for the control of a miniaturized Mueller imaging system intended for in vivo measurements in the operating room. 

In particular, the software of the new imaging system should 

• control the electronics dedicated to the synchronized acquisition of polarimetric images (modulation and analysis of light polarization, triggering of camera, variation of light source intensity, etc.). 

• have an option of the integration of machine learning algorithms capable to provide the relevant information to a neurosurgeon in less than a second. 

• have a “user friendly” graphical interface for easy and rapid use of the new imaging system in the operating room.

A software engineer will work in a close collaboration with a postdoctoral fellow who will develop the hardware part of the new instrument at LPICM. He/she will also interact with neurosurgeons in order to understand their real needs in the operating room as well as with the researchers working on the development of machine learning algorithms in order to integrate them efficiently in the control software of the new imaging system. 

Expected competences. Excellent programming skills for the control of measuring systems, organization of the pipeline for real-time processing of large datasets. Strong motivation for the team work in a multidisciplinary environment, continuous interaction with the medical doctors. Basic knowledge of optics and matrix algebra will be a plus. 

Linguistic skills: good knowledge of English. 

Informatics skills: Excellent knowledge of Matlab, Python, good knowledge of Labview. 

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