Imaging Science in Art Conservation

Abstract Background Brain atrophy measurement is now a cornerstone in basic neuro-imaging science. While assessment of white matter atrophy by visual inspection is subjective, volumetric approaches are time-consuming and not often feasible. Bi-caudate ratio represents a linear surrogate parameter of brain volume that can be derived from standard imaging sequences. This study highlights the value of the bi-caudate ratio (BCR) as a MRI marker of white matter atrophy in patients with multiple sclerosis and ischemic leukoencephalopathy and set a cut-off value to differentiate between patients with white matter atrophy and normal subjects. Results A total of 115 patients (54 males and 61 females) diagnosed with white matter leukoencephalopathy (MS in 51 patients and ischemic leukoencephalopathy in 64 patients) were included. Another group of 60 subjects with a normal white matter signal was recruited as a control group. BCR for the patient group ranged from 0.13 to 0.27 (mean (± SD) = 0.16 ± 0.02), while for the control group, it ranged from 0.05 mm to 0.13 (mean (± SD) = 0.09 ± 0.01). The difference between the two groups was statistically significant (P value < 0.001). A cut-off value of 0.13 was used to differentiate between the BCR in both patients and control groups with sensitivity, specificity, and accuracy of 99.2%, 100%, and 99%, respectively. The difference in BCR for patients diagnosed with MS and ischemic leukoencephalopathy was also statistically significant (P value < 0.001). Conclusion The bi-caudate ratio represents a linear measurement of subcortical atrophy that can be useful as a surrogate marker of global supra-tentorial white matter atrophy instead of the usually performed visual and therefore subjective assessment. It is an easily obtained measure that can be performed without complex time-consuming volumetric studies. Our findings also revealed that the BCR is higher in patients with ischemic leukoencephalopathy than in patients with MS.

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Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets

Remote Sensing ◽

10.3390/rs13152877 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2877

Author(s):

Yu Tao ◽

Siting Xiong ◽

Susan J. Conway ◽

Jan-Peter Muller ◽

Anthony Guimpier ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Imaging System ◽

Landing Site ◽

Parameter Tuning ◽

Processing Parameters ◽

Input Image ◽

Large Area ◽

Laser Altimeter ◽

Imaging Science

The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above issues. We demonstrate the wide applicability of this technique with the ExoMars Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) 4.6 m/pixel images on Mars. Only a single input image and a coarse global 3D reference are required, without knowing any camera models or imaging parameters, to produce high-quality and high-resolution full-strip Digital Terrain Models (DTMs) in a few seconds. In this paper, we discuss technical details of the MADNet system and provide detailed comparisons and assessments of the results. The resultant MADNet 8 m/pixel CaSSIS DTMs are qualitatively very similar to the 1 m/pixel HiRISE DTMs. The resultant MADNet CaSSIS DTMs display excellent agreement with nested Mars Reconnaissance Orbiter Context Camera (CTX), Mars Express’s High-Resolution Stereo Camera (HRSC), and Mars Orbiter Laser Altimeter (MOLA) DTMs at large-scale, and meanwhile, show fairly good correlation with the High-Resolution Imaging Science Experiment (HiRISE) DTMs for fine-scale details. In addition, we show how MADNet outperforms traditional photogrammetric methods, both on speed and quality, for other datasets like HRSC, CTX, and HiRISE, without any parameter tuning or re-training of the model. We demonstrate the results for Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) and a couple of sites of high scientific interest.

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