Fast super resolution ultrasound imaging using the erythrocytes

Jensen, Jørgen Arendt; Schou, Mikkel; Andersen, S. B.; Søgaard, Stinne Byrholdt; Sørensen, Charlotte Mehlin; Nielsen, Michael Bachmann; Gundlach, Carsten; Kjer, Hans Martin; Dahl, Anders Bjorholm; Stuart, Matthias Bo; Tomov, Borislav Gueorguiev

doi:10.1117/12.2612245

Cited by 16 publications

(23 citation statements)

References 14 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The frame rate was 208.3 Hz. 8,9 This data was beamformed on a GPU card, and the motion vector field was found from the envelope of signal. 10 The images were spatially aligned, and singular value decomposition (SVD) processing was used for removing stationary signals.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, 16,000 frames of the acquired data were used for tracking. 8,9 The positions of these detected peaks are used as targets for tracking, where the simple and fast nearest-neighbor (NN) tracker was used. In the NN method, a target in the next frame was found to determine the closest target to the current target.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, a new SUper Resolution ultrasound imaging approach using Erythrocytes (SURE) has been introduced . 8,9 This approach uses erythrocytes (red blood cells) as targets instead of fragile MBs. Hence, the acquisition of the SURE data can be accomplished in seconds, due to the abundance of erythrocytes as targets instead of several minutes, if MBs were used as targets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast super resolution ultrasound imaging by tracking of erythrocytes using different velocity constraints

Amin-Naji¹,

Taghavi²,

Søgaard³

et al. 2022

Medical Imaging 2022: Ultrasonic Imaging and Tomography

Self Cite

View full text Add to dashboard Cite

One of the integral parts of super-resolution ultrasound imaging (SRI) is particle tracking, and researchers are trying to improve SRI with different tracking methods. This paper presents tracking for a new approach for SUper Resolution ultrasound imaging using Erythrocytes (SURE), which uses the erythrocytes as the target instead of fragile microbubbles (MBs). The acquisition of the SURE data can be accomplished in seconds due to the abundance of erythrocytes as targets. The nearest-neighbor (NN) algorithm was used to track erythrocytes, since this is one of the successful approaches for MB tracking in SRI. The erythrocyte targets were tracked to create SURE intensity map by three NN trackers with a constraint on the maximum velocities of 20, 40, 80 mm/s. Each tracker generated a different track map that depict different details. By combining the outputs of three trackers, and inserting them into one map, it was demonstrated that the combination of trajectories from different velocities carried more information from all the maps. Also, an image fusion method using discrete wavelet transform is applied on the intensity maps of these three different velocities. Qualitative and quantitative experiments were conducted to exhibit the enhancements of the combined and the fused intensity map. Comparing three intensity maps with their combination and fusion and also a magnified area showed the fused one had better visual appearance than the others. Standard deviation (SD), average gradient (AG), entropy (H) and spatial frequency (SF) were used for quantitative comparison. According to SD, AG, H, and SF, the fused intensity map had 10%, 46%, 2%, and 46% higher scores compared to the combined intensity map. Also, the fused intensity map had 19% and 22% higher scores compared to the intensity map with maximum velocities of 80 mm/s according to AG and SF, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast super resolution ultrasound imaging by tracking of erythrocytes using different velocity constraints

Amin-Naji¹,

Taghavi²,

Søgaard³

et al. 2022

Medical Imaging 2022: Ultrasonic Imaging and Tomography

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the past decade, a substantial body of literature has reported the combination of long Doppler ensembles with advanced clutter filters (e.g., the ones based on singular value decomposition (SVD)) to increase Doppler sensitivity to small vessels [36][37][38]. In addition to improving Doppler sensitivity (e.g., SNR), several methods were recently proposed to improve the spatial resolution of contrast-free power Doppler [39][40][41][42]. For example, Bar-Zion et al [39] used hundreds of narrowband filters to slice the Doppler signal in the temporal dimension and performed sparsitybased reconstruction on individual signal slices.…”

Section: Introductionmentioning

confidence: 99%

Contrast-free Super-resolution Doppler (CS Doppler) based on Deep Generative Neural Networks

You

Lowerison

Shin

et al. 2022

Preprint

View full text Add to dashboard Cite

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micron scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive post-processing times. In this study, we present a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS Doppler could achieve an approximate 2-fold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS Doppler showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio of 25.52. Moreover, CS Doppler was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS Doppler was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep generative network also allows CS Doppler to be implemented for real-time imaging. These features of CS Doppler offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

show abstract

“…Nach wie vor arbeiten mehrere Gruppen an Möglichkeiten, die Aufnahmezeit zu verkürzen, z. B. durch Abbildung sich überlagernder Mikrobläschen [8], durch Scannen ohne Kontrastmittel [29] oder durch die Verwendung von Deep Learning [30]. Die Bewertung von Gefäßen anhand der Trajektorien der Mikrobläschen ist ebenfalls unsicher, da die Anzahl der Mikrobläschen von Scan zu Scan stark variieren kann, was sich natürlich auf die geschätzten Parameter wie die Gefäßdichte auswirkt [31].…”

unclassified