This document was primarily created to guide users wishing to reproduce the results from the SRPCA paper: Computational Super-Resolution for Ultrasound Localization Microscopy Through Solving an Inverse Problem (10.1109/TUFFC.2025.3553735). It assumes the user is familiar with the paper material (in particular ULM) and that they are able to run the PALA codes. Furthermore, the use of all the necessary Matlab toolboxes is assumed.
This document was primarily created to guide users wishing to reproduce the results from the SRPCA paper: Computational Super-Resolution for Ultrasound Localization Microscopy Through Solving an Inverse Problem (10.1109/TUFFC.2025.3553735). It assumes the user is familiar with the paper material (in particular ULM) and that they are able to run the PALA codes. Furthermore, the use of all the necessary Matlab toolboxes is assumed.
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If you are using this code, please cite: V. Pustovalov, D. H. Pham, C. Alix, J. -P. Remeniéras and D. Kouamé, "Computational Super-Resolution for Ultrasound Localization Microscopy Through Solving an Inverse Problem," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 72, no. 5, pp. 636-645, May 2025.
If you are using this code, please cite: V. Pustovalov, D. H. Pham, C. Alix, J. -P. Remeniéras and D. Kouamé, "Computational Super-Resolution for Ultrasound Localization Microscopy Through Solving an Inverse Problem," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 72, no. 5, pp. 636-645, May 2025.
## Installation
# Installation
The SRPCA code was developed to integrate into the pipeline provided by PALA. For the overall code structure and ULM parameters, we refer the reader to the original documentation provided by the authors of PALA (see https://github.com/AChavignon/PALA).
The SRPCA code was developed to integrate into the pipeline provided by PALA. For the overall code structure and ULM parameters, we refer the reader to the original documentation provided by the authors of PALA (see https://github.com/AChavignon/PALA).
However, to make our contribution easier to distinguish from the PALA framework, we have grouped our additions into dedicated folders. Please follow the steps below to integrate the SRPCA code:
However, to make our contribution easier to distinguish from the PALA framework, we have grouped our additions into dedicated folders. Please follow the steps below to integrate the SRPCA code:
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2. Transfer the contents of the Addons folder into the PALA_addons directory.
2. Transfer the contents of the Addons folder into the PALA_addons directory.
3. Import the in vivo data into the PALA_data_folder directory.
3. Import the in vivo data into the PALA_data_folder directory.
## Data Retrieval
# Data Retrieval
The simulation “In Silico” OPULM PALA dataset is publicly available at: https://zenodo.org/records/4343435. To reproduce the augmented data used in Figure 3 of the paper, one can run the code located in the AugmentedInSilico/GenSilicoAug directory. Alternatively, the pre-generated augmented datasets for various signal-to-noise ratios (SNRs, in dB) are available in the corresponding SilicoData_*dB folders.
The simulation “In Silico” OPULM PALA dataset is publicly available at: https://zenodo.org/records/4343435. To reproduce the augmented data used in Figure 3 of the paper, one can run the code located in the AugmentedInSilico/GenSilicoAug directory. Alternatively, the pre-generated augmented datasets for various signal-to-noise ratios (SNRs, in dB) are available in the corresponding SilicoData_*dB folders.
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The in vivo “WKY” rat brain dataset used in this study is subject to data usage agreements due to institutional constraints. Please contact Jean-Pierre Remeniéras regarding potential data access under appropriate collaborative or institutional agreements.
The in vivo “WKY” rat brain dataset used in this study is subject to data usage agreements due to institutional constraints. Please contact Jean-Pierre Remeniéras regarding potential data access under appropriate collaborative or institutional agreements.
## Reproducing paper results
# Reproducing paper results
To facilitate comparison without requiring full re-computation, we provide precomputed result files. Moreover, the MatTrack files, which contain the super-resolved microbubble positions, are available for the SRPCA result on the WKY rat dataset. Please note that MatTrack files were not saved for the in vivo PALA dataset.
To facilitate comparison without requiring full re-computation, we provide precomputed result files. Moreover, the MatTrack files, which contain the super-resolved microbubble positions, are available for the SRPCA result on the WKY rat dataset. Please note that MatTrack files were not saved for the in vivo PALA dataset.
# Figure 3: simulated “Augmented In Silico” results
## Figure 3: simulated “Augmented In Silico” results
The ground truth (GT) in Fig.3.a is directly provided in the PALA data repository and corresponds to the MatOut variable in the PALA_InSilicoFlow_v3_config.mat file.
The ground truth (GT) in Fig.3.a is directly provided in the PALA data repository and corresponds to the MatOut variable in the PALA_InSilicoFlow_v3_config.mat file.
Use the Fig3b_silicoaug_RSULM.m script to compute Fig.3.b and the Fig3c_silicoaug_SRPCA.m script to compute Fig.3.c. Note that the temporal band-pass filter was not applied to this dataset, as it would have removed a substantial number of microbubbles. The hyperparameters for both RS-ULM and SRPCA were optimized for the 20 dB SNR case and then kept fixed across all other SNR levels for consistency.
Use the Fig3b_silicoaug_RSULM.m script to compute Fig.3.b and the Fig3c_silicoaug_SRPCA.m script to compute Fig.3.c. Note that the temporal band-pass filter was not applied to this dataset, as it would have removed a substantial number of microbubbles. The hyperparameters for both RS-ULM and SRPCA were optimized for the 20 dB SNR case and then kept fixed across all other SNR levels for consistency.
Metrics of Table II and Table III can be reproduced by running the SilicoAugMetrics.m code.
Metrics of Table II and Table III can be reproduced by running the SilicoAugMetrics.m code.
# Figure 6: In Vivo “WKY” rat results
## Figure 6: In Vivo “WKY” rat results
Although the dataset is not publicly available, we provide the scripts used to generate the corresponding results.
Although the dataset is not publicly available, we provide the scripts used to generate the corresponding results.
The Fig.6.a and Fig.6.c were produced by using the Fig6ac_InVivoTours_RSULM.m script.
The Fig.6.a and Fig.6.c were produced by using the Fig6ac_InVivoTours_RSULM.m script.
For Fig. 6b and Fig. 6d, we used the Fig6db_InVivoTours_SRPCA.m script.
For Fig. 6b and Fig. 6d, we used the Fig6db_InVivoTours_SRPCA.m script.
# Figure 7: PALA supplementary results
## Figure 7: PALA supplementary results
To facilitate data handling, we converted the original HDF5 format of this dataset into the standard Matlab .mat format. The complex-valued IQ data were saved using the script described in the section above.
To facilitate data handling, we converted the original HDF5 format of this dataset into the standard Matlab .mat format. The complex-valued IQ data were saved using the script described in the section above.
Use the Fig7db_InVivoPala_SRPCA.m script to compute the results of Fig.7.b. and Fig.7.d.
Use the Fig7db_InVivoPala_SRPCA.m script to compute the results of Fig.7.b. and Fig.7.d.
