Signal Processing in Microsurgery: A Primer on Proactive Application

The foundation of microsurgery is optimizing flow into free flaps. Historically, however, this pursuit was guided more by intuition than by measurement, with surgeons often acting first and interpreting outcomes later. The advent of transit-time flowmetry (TTFM), particularly in combination with signal processing, offers an opportunity to shift this paradigm.

TTFM uses two ultrasound transducers separated by a short longitudinal distance along a vessel, with a reflector positioned on the opposing vessel wall. Each transducer emits ultrasonic waves that traverse the vessel lumen, reflect across, and reach the other probe. By comparing the transit times of upstream and downstream waves, the device computes flow within a vascular segment. Conventionally, TTFM provides two readily interpretable measures of intravascular hemodynamics: mean flow and pulsatility index (PI). PI is often used as a surrogate for vascular resistance.

TTFM is widely used in cardiovascular surgery, particularly in coronary artery bypass grafting (CABG). In these operations, graft patency can mean the difference between life and death. Unsurprisingly, some societies in cardiac surgery now advocate for universal TTFM use.[1] While the stakes in microsurgery are different, achieving successful flap outcomes is no less important. Despite this potential, adoption of TTFM in microsurgery has been slow, in part because there is no definitive evidence that such measurements offer a benefit to microsurgeons. Indeed, the two most concerning scenarios are of limited practical value: low or absent flow is more easily detected with Doppler ultrasonography, and elevated PI offers little actionable guidance.

Beyond these two scalar values, TTFM also provides time-resolved flow across the cardiac cycle, producing a waveform that contains additional physiologic information. Extracting that information is the domain of signal processing, or the analysis and interpretation of signals, such as those generated by EEG, ECG, and TTFM. Signal processing already underpins tools in the microsurgeon's armamentarium; for example, ultrasound devices convert radiofrequency echoes into interpretable audio signals through such methods. These same approaches can be applied directly to the flow waveforms produced by TTFM, revealing information that scalar metrics cannot.

Cardiac surgeons have already explored this approach to optimize intraoperative flow assessment, most notably through the use of the Fourier Transform. This foundational mathematical tool builds on the principle that any complex waveform decomposes into a series of simple sine waves. These sine waves follow a predictable pattern that includes the fundamental frequency, which represents the dominant physiologic signal, and harmonics, which occur at integer multiples of that frequency ([Fig. 1]). The application of this framework to TTFM tracings is among the most relevant lessons microsurgeons can borrow from the cardiac literature.

Fig. 1 Flow tracing (A) and corresponding frequency spectrum from a fast Fourier transform (B) in a two-perforator deep inferior epigastric perforator flap. Flow was measured at the anastomosis using a transit-time flowmeter. The fundamental frequency is 1.65 Hz, corresponding to approximately 99 beats per minute.

The mathematics underlying the Fourier Transform is both complex and elegant, yet its use in understanding blood flow is surprisingly intuitive. Studies in the cardiac literature demonstrate that the relative powers of the fundamental frequency and its harmonics provide insight into flow disturbances within a vessel. This is because the fundamental frequency mirrors the rhythmic pulse of the heart, while the harmonics reveal the added complexity of flow shape and noise.

While flow disturbances increase the risk of thrombosis, it is not by themselves an actionable metric for microsurgeons. More importantly, these disturbances may reflect retrograde waves from a downstream problem, such as vascular thrombosis, which does carry clinical implications. As evidence, studies in the CABG literature have shown that the power ratio of the fundamental frequency to the first harmonic is an independent predictor of graft patency, with a value below one corresponding to poorer outcomes.[2]

Such information could prove invaluable in microsurgery, where vascular problems may be subtle or difficult to localize. A flap may appear well-perfused at the anastomosis on TTFM or Doppler and look healthy intraoperatively, only to develop congestion shortly after inset. Signal processing could help identify these issues earlier. Beyond the fundamental to first harmonic ratio, other Fourier-derived metrics like total harmonic distortion deserve investigation. Moreover, the field should look beyond Fourier analysis to broader applications, including machine learning to derive novel predictive features.

Still, insights from TTFM in cardiovascular surgery may not apply universally to free flaps. Cardiac grafts are high-pressure, high-flow conduits, while flap vessels are smaller, more variable, and more vulnerable to spasm or technical imperfections. Our initial work with signal processing shows promise, but its application in microsurgery remains difficult. In cardiovascular surgeries, universal TTFM use and standardized postoperative CT create a built-in system for linking flow patterns with outcomes. This is an advantage microsurgery currently lacks.

Several questions follow. Can microsurgeons identify flow signatures within TTFM tracings that predict thrombosis along the vascular chain ([Fig. 2])? Can such signatures distinguish arterial from venous compromise? Cardiac surgeons have validated select signal processing approaches; microsurgeons now have the opportunity to test them in a different physiologic domain.

Fig. 2 Flow tracing (A) and corresponding frequency spectrum from a fast Fourier transform (B) in a two-perforator deep inferior epigastric perforator flap with both perforators clamped. Flow was measured at the anastomosis using a transit-time flowmeter. The fundamental frequency is 1.65 Hz, corresponding to approximately 99 beats per minute.

TTFM adoption is growing in our department and is now standard in all free flaps for our senior author. We believe substantial untapped potential lies in applying TTFM to microvascular reconstruction, and we hope this manuscript serves as a call to action for microsurgeons to explore these possibilities. Nevertheless, signal processing should not yet be seen as a diagnostic solution, but as a framework for discovery.

Publication History

Received: 12 November 2025

Accepted: 19 November 2025

Accepted Manuscript online:
21 November 2025

Article published online:
09 December 2025

• References

• 1 Gaudino M, Sandner S, Di Giammarco G. et al. The use of intraoperative transit time flow measurement for coronary artery bypass surgery: systematic review of the evidence and expert opinion statements. Circulation 2021; 144 (14) 1160-1171
• 2 Takami Y, Maekawa A, Yamana K. et al. Fast Fourier transform analysis of intraoperative transit-time flow measurement during coronary artery bypass grafting. J Cardiothorac Surg 2024; 19 (01) 635