Cone-beam scanners (CBCT) enable CT to be performed under weight-bearing – notably for the foot and ankle. The technology is not new: it has been used since 1996 in dental surgery, where it has come to replace panoramic X-ray. What is new is placing the scanner on the ground, so as to have 3D weight-bearing images, initially of the foot and ankle, and later for the knee and pelvis. This saves time, radiation and money. It is now increasingly used, but is unfortunately limited by not having specific national health insurance cover in France, and by the psychological reticence that goes with any technological breakthrough. A review of the topic is indispensable, as it is essential to become properly acquainted with this technique. To this end, we shall be addressing 5 questions. What biases does conventional radiography incur? Projecting a volume onto a plane incurs deformation, precluding true measurement. Conventional CT is therefore often associated with an increased dose of radiation. What is the impact of CBCT on radiation dose, costs and the care pathway? The conical beam turns around the limb (under weight-bearing if so desired) in less than a minute, making the radiation dose no greater than in standard X-ray. What does the literature have to say about CBCT, and what are the indications? CBCT is indicated in all foot and ankle pathologies, and indications now extend to the upper limb and the knee, and will soon include the pelvis. How are angles measured on this 3D technique? The recently developed concept of 3D biometry uses dedicated software to identify anatomic landmarks and automatically segment the bones, thereby enabling every kind of measurement. What further developments are to be expected? CBCT may become indispensable to lower-limb surgical planning. Artificial Intelligence will reveal novel diagnostic, prognostic and therapeutic solutions.
Level of evidence
V; expert opinion.Le texte complet de cet article est disponible en PDF.
Keywords : CT, Cone-beam, Weight-bearing, 3D biometrics, Artificial intelligence, Deep learning