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Functional Ultrasound Imaging (fUSI) from scratch
Functional ultrasound imaging (fUSI) is a brain imaging modality that is just starting to be demonstrated in human studies. I’m sure many will be familiar with structural ultrasound imaging, which forms static images using ultrasound, as in a sonogram1. Functional ultrasound instead captures movies of brain tissue very rapidly, tracking subtle changes caused by the blood flow that follows neural activity.
While this is the same source of contrast as functional MRI, it has the potential to be ten times higher linear resolution (1000X the voxels), smaller (no magnet!), cheaper (no magnet!), and higher signal-to-noise. The modality naturally pairs with low-intensity focused ultrasound (LIFU, or confusingly, fUS) which focuses energy on a target area of the brain to modulate it, a new treatment option for neuropsychiatric and neurological disorders. Extensions leveraging contrast agents or gene therapies could go beyond hemodynamic responses to measure vasculature at super-resolution or even directly measure neural activity. And while ultrasound cannot easily penetrate the skull, fUSI has been demonstrated intraoperatively and in a patient that received an acoustically transparent cranial implant following reconstructive skull surgery.
I did a deep dive into fUSI last year, and found that much of the technical information is hidden away in the methods sections of application papers, or spread throughout long books on ultrasound imaging as a whole. fUSI picks up on the hemodynamic response, but how? What’s the source of contrast? Does it measure blood flow or volume? How does it deal with motion of the head? What is ultrasound, anyway?
I went down a rabbit hole—including reading an 800-page textbook pretty much cover-to-cover—so you don’t have to. I will break down how fUSI works physically and how it’s analyzed. This will give you a good basis to study fUSI computationally. Because much of the analysis toolkits available for fUSI sims are locked up in Matlab, I’ve posted a series of Python tutorials on Github so that you (or your agent) can follow along. At the end of this essay, you should understand what fUSI is, how it works, and why it is nowhere near its ceiling.
This is a much-expanded version of an article I posted on X in February. I wasn’t happy with the original, so I took a second pass: this piece includes new images and video, goes deeper into physics, and pinpoints where fUSI signal processing could be improved.
Functional ultrasound imaging, like conventional ultrasound imaging, uses transducers—which act as both transmitters and receivers—to communicate ultrasound through tissue. That ultrasound travels mostly unimpeded through brain tissue2, backscattering where it encounters a change in impedance, like a red blood cell, an air bubble, or the skull. By measuring these backscattered waves, it is possible to computationally reconstruct local changes in impedance. The resolution of the reconstructed images is proportional to the wavelength of the communicated ultrasound; that can mean up to ~100 um linear resolution when sonifying at 18 MHz, at the high end of what’s used clinically.
That explains how we can measure high-resolution structural images, but what about functional imaging? When neurons fire, they consume energy, which triggers vasodilation and a delayed influx of blood to the active region: hemodynamics. That blood has different properties than baseline: there’s more of it, it moves more rapidly, it has a different oxygen content, and it has a different color. Multiple brain imaging modalities take advantage of this fact, measuring changes in some or all of these properties, including fMRI (BOLD) and fNIRS. fUSI uses the fact that moving red blood cells cause shifting patterns of constructive and destructive interference in backscattered wavefronts, the fine-grained texture in ultrasound images known as speckle. fUSI acquires images of the brain very rapidly—over a kHz—and tracks changes in speckle o