Background Blood Perfusion is a key factor in the development and healing of wounded tissues including Diabetic Foot Ulcers (DFU), a harmful chronic wound caused by diabetic neuropathy. Recent works have explored the use of hyperspectral imaging (HSI) to non-invasively quantify the quality of blood perfusion with high spatial resolution. Later works consider the use of hyperspectral reconstruction (HSR) to provide the same capability using unmodified commodity hardware, such as smartphone cameras, using computational methods to yield full hyperspectral images from RGB ones. However, these HSR perfusion systems require profiles for each camera they are used with and furthermore require radiometric calibration to account for environmental lighting conditions before each use. Methods In this work we demonstrate MobiPerf which extracts oxygenation signals/images along with high fidelity remote PPG signals while overcoming these challenges. To eliminate the need for camera profiles, our system uses deep learning HSR models that have been shown to generalize well across different cameras. Then to overcome the need for reference image calibration, we utilize a custom algorithm Calibration Free Skin Compensation Estimation. Results Evaluated under 5 different simulated lighting conditions from the CIE Standard Illuminates, our system maintains strong agreement with oxygenation images/signals extracted directly from HSI cameras. Our testing on in-the-wild RGB data from a publicly available dataset of diabetic foot ulcer images (N <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44398_2025_11_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(\approx\)</EquationSource> </InlineEquation> 6000) shows an acute sensitivity to Ischemia conditions (p <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44398_2025_11_Article_IEq2.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="85" /> </InlineMediaObject> <EquationSource Format="TEX">\(<\ 1 \times 10^{-5}\)</EquationSource> </InlineEquation>) as well as a more limited sensitivity to infection complications. Along with a dataset of videos with contract PPG (N = 56) which shows rPPG performance on par or better than other state-of-the-art algorithms. Conclusions Our results demonstrate that a HSR system can be used to monitor diabetic foot ulcers using just images/videos minimizing the need for procedures prior to or during use and with mobile hardware patients already have. We anticipate that in the future our advancements in HSR can be used for other smart health applications that relate to perfusion, and we anticipate that similar HSR based systems can be used to monitor other tissue parameters such as sweat concentrations.

Hyperspectral reconstruction for mobile diabetic foot blood perfusion monitoring

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2025年9月22日

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Alexander Gherardi,
Wei Bo,
Ahmet Demirbas,
Ye Zhan,
Wenyao Xu

摘要

Background

Blood Perfusion is a key factor in the development and healing of wounded tissues including Diabetic Foot Ulcers (DFU), a harmful chronic wound caused by diabetic neuropathy. Recent works have explored the use of hyperspectral imaging (HSI) to non-invasively quantify the quality of blood perfusion with high spatial resolution. Later works consider the use of hyperspectral reconstruction (HSR) to provide the same capability using unmodified commodity hardware, such as smartphone cameras, using computational methods to yield full hyperspectral images from RGB ones. However, these HSR perfusion systems require profiles for each camera they are used with and furthermore require radiometric calibration to account for environmental lighting conditions before each use.

Methods

In this work we demonstrate MobiPerf which extracts oxygenation signals/images along with high fidelity remote PPG signals while overcoming these challenges. To eliminate the need for camera profiles, our system uses deep learning HSR models that have been shown to generalize well across different cameras. Then to overcome the need for reference image calibration, we utilize a custom algorithm Calibration Free Skin Compensation Estimation.

Results

Evaluated under 5 different simulated lighting conditions from the CIE Standard Illuminates, our system maintains strong agreement with oxygenation images/signals extracted directly from HSI cameras. Our testing on in-the-wild RGB data from a publicly available dataset of diabetic foot ulcer images (N \(\approx\) 6000) shows an acute sensitivity to Ischemia conditions (p \(<\ 1 \times 10^{-5}\) ) as well as a more limited sensitivity to infection complications. Along with a dataset of videos with contract PPG (N = 56) which shows rPPG performance on par or better than other state-of-the-art algorithms.

Conclusions

Our results demonstrate that a HSR system can be used to monitor diabetic foot ulcers using just images/videos minimizing the need for procedures prior to or during use and with mobile hardware patients already have. We anticipate that in the future our advancements in HSR can be used for other smart health applications that relate to perfusion, and we anticipate that similar HSR based systems can be used to monitor other tissue parameters such as sweat concentrations.