Histotripsy is a relatively new therapeutic ultrasound technology to mechanically liquefy tissue into subcellular debris using high-amplitude focused ultrasound pulses. In contrast to conventional high-intensity focused ultrasound thermal therapy, histotripsy has specific clinical advantages: the capacity for real-time monitoring using ultrasound imaging, diminished heat sink effects resulting in lesions with sharp margins, effective removal of the treated tissue, a tissue-selective feature to preserve crucial structures, and immunostimulation. The technology is being evaluated in small and large animal models for treating cancer, thrombosis, hematomas, abscesses, and biofilms; enhancing tumor-specific immune response; and neurological applications. Histotripsy has been recently approved by the US Food and Drug Administration to treat liver tumors, with clinical trials undertaken for benign prostatic hyperplasia and renal tumors. This review outlines the physical principles of various types of histotripsy; presents major parameters of the technology and corresponding hardware and software, imaging methods, and bioeffects; and discusses the most promising preclinical and clinical applications.

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated with tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and its optimization for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested in silico with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filter-singular value decomposition (SVD) and infinite impulse response (IIR) high-pass filter. DMD was then applied to an ex vivo tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. In silico DMD outperformed SVD and IIR high-pass filter and ex vivo provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.