Super-resolution of clinical CT: Revealing microarchitecture in whole bone clinical CT image data.

Autor: Frazer LL; Southwest Research Institute, USA. Electronic address: lance.frazer@swri.org., Louis N; Southwest Research Institute, USA; University of Michigan, USA., Zbijewski W; John Hopkins University, USA., Vaishnav J; Canon Medical Systems, USA., Clark K; University of Texas Health Science Center at San Antonio, USA., Nicolella DP; Southwest Research Institute, USA.
Jazyk: angličtina
Zdroj: Bone [Bone] 2024 Aug; Vol. 185, pp. 117115. Date of Electronic Publication: 2024 May 11.
DOI: 10.1016/j.bone.2024.117115
Abstrakt: Osteoporotic fractures, prevalent in the elderly, pose a significant health and economic burden. Current methods for predicting fracture risk, primarily relying on bone mineral density, provide only modest accuracy. If better spatial resolution of trabecular bone in a clinical scan were available, a more complete assessment of fracture risk would be obtained using microarchitectural measures of bone (i.e. trabecular thickness, trabecular spacing, bone volume fraction, etc.). However, increased resolution comes at the cost of increased radiation or can only be applied at small volumes of distal skeletal locations. This study explores super-resolution (SR) technology to enhance clinical CT scans of proximal femurs and better reveal the trabecular microarchitecture of bone. Using a deep-learning-based (i.e. subset of artificial intelligence) SR approach, low-resolution clinical CT images were upscaled to higher resolution and compared to corresponding MicroCT-derived images. SR-derived 2-dimensional microarchitectural measurements, such as degree of anisotropy, bone volume fraction, trabecular spacing, and trabecular thickness were within 16 % error compared to MicroCT data, whereas connectivity density exhibited larger error (as high as 1094 %). SR-derived 3-dimensional microarchitectural metrics exhibited errors <18 %. This work showcases the potential of SR technology to enhance clinical bone imaging and holds promise for improving fracture risk assessments and osteoporosis detection. Further research, including larger datasets and refined techniques, can advance SR's clinical utility, enabling comprehensive microstructural assessment across whole bones, thereby improving fracture risk predictions and patient-specific treatment strategies.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lance Frazer reports financial support was provided by Canon Medical Systems USA Inc. Jay Vaishnav reports a relationship with Canon Medical Systems USA Inc. that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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