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Researchers have developed non-invasive palpation for the brain.

Many diseases involve structural changes in tissues, which are reflected in a change in their mechanical properties, such as elasticity. Using the sensitivity of their hands, and their detailed knowledge of the body, physicians, through an examination known as palpation, can assess the size and stiffness of a tumour, the presence of inflamed lymph nodes, or the size and position of the foetus in a pregnant woman, to mention a few examples.

Therefore, palpation is used by the physician to explore the human body during every medical examination in order to make a diagnosis or prescribe further tests. By its nature, however, the brain cannot be palpated without using a highly invasive procedure such as a craniotomy, or opening the skull, which is limited to rare cases.  Now, Inserm researchers have developed a noninvasive brain imaging method that provides the same information as physical palpation by drawing on seismology. The team state that ultimately, it could be used in the early diagnosis of brain tumours or Alzheimer’s disease. This study is published in the journal PNAS.

Previous studies show that the modern techniques that have supplemented or replaced palpation, give the physician an indication of the elasticity of a biological tissue. They are based on the generation and detection of waves that propagate through the body at varying speeds depending on the stiffness of the organs, for example, the stiffer the tissue, the slower the wave propagation, and vice versa. However, this method cannot be applied to the brain, which, doubly protected by the cranium and cerebrospinal fluid, is difficult for externally applied waves to access. This is the despite the fact that the brain is the seat of natural vibrations created by the blood pulsating in the arteries and the circulating cerebrospinal fluid.  The lab state that as it is impossible to directly or indirectly palpate the brain there remained a significant challenge on how to capture this complex field of natural shear waves, and represent it on a computer screen.

The current study used MRI to successfully detect natural shear waves in the brain using computational techniques borrowed from seismologists, known as ‘noise correlation’.  The group were able to build images of the brain’s elasticity using this technique.  The researchers explain that Alzheimer’s disease, epilepsy, multiple sclerosis and hydrocephalus all involve changes in the stiffness of the brain tissues, therefore, this new technique will allow their detection, and could be used to avoid brain biopsies. They go on to conclude that if this method can be developed for clinical use, it will be a boon for both the patient and the physician, since making the brain vibrate is quite painful at the moment.

The team surmise that this method will be complementary to those that already exist, and foresee a multimodal medical diagnosis.  For the future, the researchers state that this method for palpating the brain could have other areas of application, such as mapping the progression of neurodegeneration, the impact of a brain lesion or treatment evaluation, just as a start.

Source: INSERM (Institut national de la santé et de la recherche médicale)

Comparison between two volunteers of (Left) the axial view of the T2-weighted image and (Right) its corresponding shear-wavelength tomography.  The tomography on volunteer 2 was acquired 1 mo after the one presented in the manuscript. Although in different regions of the brain, the similarity between reconstructions is striking.  Brain palpation from physiological vibrations using MRI.  Catheline et al 2015.
Comparison between two volunteers of (Left) the axial view of the T2-weighted image and (Right) its corresponding shear-wavelength tomography. The tomography on volunteer 2 was acquired 1 mo after the one presented in the manuscript. Although in different regions of the brain, the similarity between reconstructions is striking. Brain palpation from physiological vibrations using MRI. Catheline et al 2015.

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