Prof. James C. Torner Profile

Prof. James C. Torner

at Univ of Iowa

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Publications (3)

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Proceedings Article | 15 February 2021 Presentation + Paper

Unsupervised GAN-CIRCLE for high-resolution reconstruction of bone microstructure from low-resolution CT scans

Indranil Guha, Syed Ahmed Nadeem, Xiaoliu Zhang, Steven Levy, James Torner, Punam Saha

Proceedings Volume 11600, 116001F (2021) https://doi.org/10.1117/12.2581068

KEYWORDS: Bone, Computed tomography, Scanners, Minerals, In vivo imaging

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Osteoporosis is an age-related disease associated with reduced bone density and increased fracture-risk. It is known that bone microstructural quality is a significant determinant of trabecular bone strength and fracture-risk. Emerging CT technology allows high-resolution in vivo imaging at peripheral sites enabling assessment of bone microstructure at low radiation. Resolution dependence of bone microstructural measures together with varying technologies and rapid upgrades in CT scanners warrants data-harmonization in multi-site as well as longitudinal studies. This paper presents an unsupervised deep learning method for high-resolution reconstruction of bone microstructure from low-resolution CT scans using GAN-CIRCLE. The unsupervised training alleviates the need of registered low- and high-resolution images, which is often unavailable. Low- and high-resolution ankle CT scans of twenty volunteers were used for training, validation, and evaluation. Ten thousand unregistered low- and high-resolution patches of size 64×64 were randomly harvested from CT scans of ten volunteers for training and validation. Five thousand matched pairs of low- and highresolution patches were generated for evaluation after registering CT scan pairs from other ten volunteers. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric derived from low-resolution data. Also, trabecular bone microstructural measures such as thickness and network area measures computed from predicted high-resolution CT images showed higher (CCC = [0.90, 0.84]) agreement with the reference measures from true high-resolution scans compared to the same measures derived from low-resolution images (CCC = [0.66, 0.83]).

Proceedings Article | 28 February 2020 Presentation + Paper

Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution CT scans using GAN-CIRCLE

Indranil Guha, Syed Ahmed Nadeem, Chenyu You, Xiaoliu Zhang, Steven Levy, Ge Wang, James Torner, Punam Saha

Proceedings Volume 11317, 113170U (2020) https://doi.org/10.1117/12.2549318

KEYWORDS: Computed tomography, Bone, X-ray computed tomography, Network architectures, Convolution, CT reconstruction

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Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable in vivo measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and highresolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p < 0.01) with true high-resolution scans as compared to the same metric for low-resolution data. Different Tb microstructural measures such as thickness, spacing, and network area density are also computed from low- and predicted high-resolution images, and compared with the values derived from true high-resolution scans. Thickness and network area measures from predicted images showed higher agreement with true high-resolution CT (CCC = [0.95, 0.91]) derived values than the same measures from low-resolution images (CCC = [0.72, 0.88]).

Proceedings Article | 28 February 2020 Presentation + Paper

CT-based characterization of transverse and longitudinal trabeculae and its applications

Xiaoliu Zhang, Elena Letuchy, Steven Levy, James Torner, Punam Saha

Proceedings Volume 11317, 113171F (2020) https://doi.org/10.1117/12.2549881

KEYWORDS: Bone, Terbium, Computed tomography, Image segmentation, Minerals, Image processing, Magnetic resonance imaging, Algorithm development, X-ray computed tomography, In vivo imaging

Read Abstract +

Osteoporosis is a common age-related disease characterized by reduced bone mineral density (BMD), micro-structural deterioration, and enhanced fracture-risk. Although, BMD is clinically used to define osteoporosis, there are compelling evidences that bone micro-structural properties are strong determinants of bone strength and fracture-risk. Reliable measures of effective trabecular bone (Tb) micro-structural features are of paramount clinical significance. Tb consists of transverse and longitudinal micro-structures, and there is a hypothesis that transverse trabeculae improve bone strength by arresting buckling of longitudinal trabeculae. In this paper, we present an emerging clinical CT-based new method for characterizing transverse and longitudinal trabeculae, validate the method, and examine its application in human studies. Specifically, we examine repeat CT scan reproducibility, and evaluate the relationships of these measures with gender and body size using human CT data from the Iowa Bone Development Study (IBDS) (n = 99; 49 female). Based on a cadaveric ankle study (n = 12), both transverse and longitudinal Tb measures are found reproducible (ICC < 0.94). It was observed in the IBDS human data that males have significantly higher trabecular bone measures than females for both inner (p < 0.05) and outer (p < 0.01) regions of interest (ROIs). For weight, Spearman correlations ranged 0.43-0.48 for inner ROI measures and 0.50-0.52 for outer ROI measures for females versus 0.30-0.34 and 0.23-0.25 for males. Correlation with height was lower (0.36-0.39), but still mostly significant for females. No association of trabecular measures with height was found for males.

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