TY - JOUR
T1 - Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images
AU - Jong, Lynn-Jade S.
AU - Appelman, Jelmer G. C.
AU - Sterenborg, Henricus J. C. M.
AU - Ruers, Theo J. M.
AU - Dashtbozorg, Behdad
N1 - Publisher Copyright: © 2024 by the authors.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - (1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
AB - (1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.
KW - breast tissue
KW - breast-conserving surgery
KW - deep learning
KW - hyperspectral imaging
KW - resection margin assessment
KW - super-resolution reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85187534563&partnerID=8YFLogxK
U2 - 10.3390/s24051567
DO - 10.3390/s24051567
M3 - Article
C2 - 38475103
SN - 1424-8220
VL - 24
JO - SENSORS
JF - SENSORS
IS - 5
M1 - 1567
ER -