
MR-guided high-intensity focused ultrasound (MR-HIFU) is "sufficiently reliable and safe" to begin clinical trials in the liver. That's according to researchers in Finland and the Netherlands, who have assessed the use of the technique in a clinically acceptable setting.
MR-HIFU works by combining the observational ability of MRI with the use of intense ultrasound to ablate tissue. The ultrasound ablates the tissue by heating it where the sound waves are focused, without needing any surgical incisions. Meanwhile, the MR imaging gives the practitioner several windows to see what is happening: It provides 3D anatomical images for initial targeting; it registers temperature to show where the heating is taking place; and it shows how well blood is perfusing. Because thermal ablation stops blood perfusion, this latter ability of MRI validates whether the technique has been a success.
Despite being invented in the early 90s, MR-HIFU has so far been mainly used clinically on organs that remain essentially motionless when a person is lying down. Some organs, such as the liver, the kidneys, and the pancreas, are close enough to the lungs that breathing alone can make the malignant target move from one place to another.
In the past decade, several ways to compensate for this kind of movement in MR-HIFU have been proposed. For instance, Dr. Wladyslaw Gedroyc of Imperial College London has performed the technique with the patient under anesthesia, so that his or her breathing can be stopped for the short duration of the ultrasound burst.
Now, medical physicist Dr. Mario Ries at University Medical Center Utrecht, together with colleagues from Utrecht and Philips Medical Systems in Vantaa, has gone a step further by assessing the feasibility of MR-HIFU in a more clinically acceptable setting -- specifically, one that is safe, reliable, and straightforward enough to be used by different practitioners. (Investigative Radiology, 5 September 2014).
"Initial solutions -- sometime before 2007 -- [were] really crude, and in the subsequent five years came a wave of overcomplicated approaches," he said. "Hopefully, we are now in the sobering-up phase where we can come up with something that is useful in clinical practice."
Ries and colleagues asked a patient to lie inside a clinical MRI chamber along with a HIFU ablation system. The MR scanner operated continuously, measuring the temperature or the patient's liver tissue while tracking the motion of the diaphragm. When the diaphragm reached a predefined position, the system accompanying the MR scanner triggered the HIFU ablation system, destroying a small, pea-sized volume of tissue. "You have to repeat the ablation cycles very often, to slowly 'chisel away' the entire target volume," he said.
Despite the liver being a moving organ, and despite its situation behind a highly absorbing rib cage, Ries and colleagues found that they could reproducibly ablate small volumes in their clinically acceptable setting. "Despite all the problems, I think we are really a step closer to be able to burn a hole in the liver where we want to have it -- without burning anything else and without leaving anything in the hole alive," he explained.
Ries adds that MR-HIFU is still too slow for widespread use, but he says that a phase I clinical trial -- in which the intervention is performed on a small number of patients, merely to validate clinical safety and therapeutic efficiency -- will run over the next year. "This is only a first step," he noted.
"For general clinical acceptance, the subsequent larger phase II and phase III trials will have to be concluded, which require in general several years."
© IOP Publishing Limited. Republished with permission from medicalphysicsweb, a community website covering fundamental research and emerging technologies in medical imaging and radiation therapy.










![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)






