Functional Ultrasound Imaging (fUSI) from scratch

Functional ultrasound imaging from scratch A new modality is coming to brain imaging Patrick Mineault Sep 30, 2026 214 Share 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. Functional ultrasound imaging was first demonstrated by Macé et al. (2011). An ultrasonic probe is used to image a rat’s brain very rapidly. A computational approach isolates rapidly changing pixels, forming a map of the vasculature. 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. How fUSI works at a high level 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 over time. The amount of change in these images relates to blood flow and volume. The percentage signal change measured with fUSI is remarkably high: up to 20% compared to fMRI’s few-percent BOLD signal. Long exposure of the Brooklyn Bridge, Sarowar2222 / Pixabay Here’s an intuitive analogy3 for how that works. Imagine a nice high-res image of Brooklyn Bridge at night, taken with a long shutter speed. The surface of the water looks smooth and unbroken. But where is the movement in this image? To track movement, we can take a high-speed movie of the scene and measure how the brightness of each pixel varies in a window of time using the standard deviation. Color coding the resulting image, the shimmering surface of the water appears bright red, even though there may be no bulk flow, ignoring the tide. One could use this to quantify the water’s choppiness, or measure how water reacts to a boat speeding across it. Video analysis can thus reveal flow4. If you know anything about photography and videography, I’m sure you can see the flaws in this analysis plan. If I crank up the frame rate and therefore shorten the exposure, won’t that decrease the SNR unacceptably? If I handhold the camera, won’t that mess up the analysis? Aren’t I discarding too much information by just calculating the standard deviation? Indeed, the same problems that plague video-based motion analysis also affect fUSI. Let’s see how these break down in detail. A detailed breakdown of fUSI Ultrafast plane waves and reconstruction Conventional ultrasound forms images by scanning a focused beam line and sweeping it across the field of view, sonar-in-a-submarine style. fUSI uses a different, multiplexed strategy: instead of a focused beam, it transmits unfocused plane waves with flat wavefronts that insonify the whole imaging plane at once, so a single pulse can produce a whole image. Multiple detectors can then resolve the source of the backscatter. The repetition rate is limited by how much time it takes for the echo to come back to the transducer: for a 10 cm round trip and 1500 m/s propagation, the repetition rate reaches 15 kHz. In practice, plane waves transmitted at multiple angles and multiple repetitions are coherently compounded, decreasing the effective framerate to something manageable. The reconstruction problem is then to infer the density of scatterers in the tissue given the time series for each element. To do that, we need to think about how ultrasound propagates through tissue, which can be done using physics™. It’s a fun exercise to derive the wave equation for sound in a fluid from scratch: it is quite similar, in fact, to deriving the Navier-Stokes equation, but with a different set of approximations. If you assume a homogeneous background medium, you can derive the propagation equations analytically, and you can simulate the equations using Fourier transforms. To perform the reconstruction, you need to notice that if you measure a signal s(t) at time t on a given transducer, there is only a small slice of space that the signal could come from: that ambiguity takes the form of a conic section. Each element has a slightly different geometry with respect to the wavefront and the tissue, and the (conic) ambiguity resolves itself by compounding the conic sections. There are a few different ways to do this, with a popular one being the delay-and-sum beamforming algorithm. For each pixel in the image, you calculate how long a sound wave takes to travel from the transducer to that point and back, apply those delays to the raw signals, and sum them up in image space. Constructive interference reveal