Combined Therapy Planning, Real-Time Monitoring, and Low Intensity Focused Ultrasound Treatment Using a Diagnostic Imaging Array

Straightforward Magnetic Resonance Temperature Measurements Combined with High Frame Rate and Magnetic Susceptibility Correction

Proton resonance frequency shift (PRFS) is an MRI-based simple temperature mapping method that exhibits higher spatial and temporal resolution than temperature mapping methods based on T1 relaxation time and diffusion. PRFS temperature measurements are validated against fiber-optic thermal sensors (FOSs). However, the use of FOSs may introduce temperature errors, leading to both underestimation and overestimation of PRFS measurements, primarily due to material susceptibility changes caused by the thermal sensors. In this study, we demonstrated susceptibility-corrected PRFS (scPRFS) with a high frame rate and accuracy for suitably distributed temperatures. A single-echo-based background removal technique was employed for phase variation correction, primarily owing to magnetic susceptibility, which enabled fast temperature mapping. The scPRFS was used to validate the temperature fidelity by comparing the temperatures of fiber-optic sensors and conventional PRFS through phantom-mimicked human and ex vivo experiments. This study demonstrates that scPRFS measurements in agar-gel are in good agreement with the thermal sensor readings, with a root mean square error (RMSE) of 0.33-0.36 °C in the phantom model and 0.12-0.16 °C in the ex vivo experiment. These results highlight the potential of scPRFS for precise thermal monitoring and ablation in both low- and high-temperature non-invasive therapies.

Improving the quality of ultrasound images acquired using a therapeutic transducer

To enhance the effectiveness and safety of focused ultrasound (FUS) therapy, ultrasound image-based guidance and treatment monitoring are crucial. However, the use of FUS transducers for both therapy and imaging is impractical due to their low spatial resolution, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). To address this issue, we propose a new method that significantly improve the quality of images obtained by a FUS transducer. The proposed method employs coded excitation to enhance SNR and Wiener deconvolution to solve the problem of low axial resolution resulting from the narrow spectral bandwidth of FUS transducers. Specifically, the method eliminates the impulse response of a FUS transducer from received ultrasound signals using Wiener deconvolution, and pulse compression is performed using a mismatched filter. Simulation and commercial phantom experiments confirmed that the proposed method significantly improves the quality of images acquired by the FUS transducer. The -6 dB axial resolution was improved 1.27 mm to 0.37 mm that was similar to the resolution achieved by the imaging transducer, i.e., 0.33 mm. SNR and CNR also increased from 16.5 dB and 0.69 to 29.1 dB and 3.03, respectively, that were also similar to those by the imaging transducer (27.8 dB and 3.16). Based on the results, we believe that the proposed method has great potential to enhance the clinical utility of FUS transducers in ultrasound image-guided therapy.

Soft Tissue Aberration Correction for Histotripsy Using Acoustic Emissions From Cavitation Cloud Nucleation and Collapse

OBJECTIVE

Phase aberration from soft tissue limits the efficacy of histotripsy, a therapeutic ultrasound technique based on acoustic cavitation. Previous work has shown that the acoustic emissions from cavitation can serve as "point sources" for aberration correction (AC). This study compared the efficacy of soft tissue AC for histotripsy using acoustic cavitation emissions (ACE) from bubble cloud nucleation and collapse.

METHODS

A 750-kHz, receive-capable histotripsy array was pulsed to generate cavitation in ex vivo porcine liver through an intervening abdominal wall. Received ACE signals were used to determine the arrival time differences to the focus and compute corrective delays. Corrections from single pulses and from the median of multiple pulses were tested.

DISCUSSION

On average, ACE AC obtained 96% ± 3% of the pressure amplitude obtained by hydrophone-based correction (compared with 71% ± 5% without AC). Both nucleation- and collapse-based corrections obtained >96% of the hydrophone-corrected pressure when using medians of ≥10 pulses. When using single-pulse corrections, nucleation obtained a range of 49%-99% of the hydrophone-corrected pressure, while collapse obtained 95%-99%.

CONCLUSION

The results suggest that (i) ACE AC can recover nearly all pressure amplitude lost owing to soft tissue aberration and that (ii) the collapse signal permits robust AC using a small number of pulses.

Assessment of bubble activity generated by histotripsy combined with echogenic liposomes

Objective.Histotripsy is a form of focused ultrasound therapy that uses the mechanical activity of bubbles to ablate tissue. While histotripsy alone degrades the cellular content of tissue, recent studies have demonstrated it effectively disrupts the extracellular structure of pathologic conditions such as venous thrombosis when combined with a thrombolytic drug. Rather than relying on standard administration methods, associating thrombolytic drugs with an ultrasound-triggered echogenic liposome vesicle will enable targeted, systemic drug delivery. To date, histotripsy has primarily relied on nano-nuclei inherent to the medium for bubble cloud generation, and microbubbles associated with echogenic liposomes may alter the histotripsy bubble dynamics. The objective of this work was to investigate the interaction of histotripsy pulse with echogenic liposomes.Approach.Bubble clouds were generated using a focused source in anin vitromodel of venous flow. Acoustic emissions generated during the insonation were passively acquired to assess the mechanical activity of the bubble cloud. High frame rate, pulse inversion imaging was used to track the change in echogenicity of the liposomes following histotripsy exposure.Main results.For peak negative pressures less than 20 MPa, acoustic emissions indicative of stable and inertial bubble activity were observed. As the peak negative pressure of the histotripsy excitation increased, harmonics of the excitation were observed in OFP t-ELIP solutions and plasma alone. Additional observations with high frame rate imaging indicated a transition of bubble behavior as the pulse pressure transitioned to shock wave formation.Significance.These observations suggest that a complex interaction between histotripsy pulses and echogenic liposomes that may be exploited for combination treatment approaches.

Augmentation-Consistent Clustering Network for Diabetic Retinopathy Grading with Fewer Annotations

Diabetic retinopathy (DR) is currently one of the severe complications leading to blindness, and computer-aided, diagnosis technology-assisted DR grading has become a popular research trend especially for the development of deep learning methods. However, most deep learning-based DR grading models require a large number of annotations to provide data guidance, and it is laborious for experts to find subtle lesion areas from fundus images, making accurate annotation more expensive than other vision tasks. In contrast, large-scale unlabeled data are easily accessible, becoming a potential solution to reduce the annotating workload in DR grading. Thus, this paper explores the internal correlations from unknown fundus images assisted by limited labeled fundus images to solve the semisupervised DR grading problem and proposes an augmentation-consistent clustering network (ACCN) to address the above-mentioned challenges. Specifically, the augmentation provides an efficient cue for the similarity information of unlabeled fundus images, assisting the supervision from the labeled data. By mining the consistent correlations from augmentation and raw images, the ACCN can discover subtle lesion features by clustering with fewer annotations. Experiments on Messidor and APTOS 2019 datasets show that the ACCN surpasses many state-of-the-art methods in a semisupervised manner.