P.167 Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinson’s Disease

Background: Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications. A point-based set of anatomical fiducials (AFIDs) was recently developed and validated to provide quantitative measures of image registration. We applied the AFIDs protocol to magnetic resonance images (MRIs) obtained from patients with Parkinson’s Disease (PD). Methods: Two expert and three novice raters placed AFIDs on MRIs of 39 PD patients. Localization and registration errors were calculated. To investigate for unique morphometric features, pairwise distances between AFIDs were calculated and compared to 30 controls who previously had AFIDs placed. Wilcoxon rank-sum tests with Bonferroni corrections were used. Results: 6240 AFIDs were placed with a mean localization error (±SD) of 1.57mm±1.16mm and mean registration error of 3.34mm±1.94mm. Out of the 496 pairwise distances, 40 were statistically significant (p<0.05/496). PD patients had a decreased pairwise distance between the left temporal horn, brainstem and pineal gland. Conclusions: AFIDs can be successfully applied with millimetric accuracy in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence, facilitating accurate aggregation of imaging datasets and comparisons between various neurological conditions.

Decoding intra-tumoral spatial heterogeneity on radiological images using the Hilbert curve

Background Current intra-tumoral heterogeneous feature extraction in radiology is limited to the use of a single slice or the region of interest within a few context-associated slices, and the decoding of intra-tumoral spatial heterogeneity using whole tumor samples is rare. We aim to propose a mathematical model of space-filling curve-based spatial correspondence mapping to interpret intra-tumoral spatial locality and heterogeneity. Methods A Hilbert curve-based approach was employed to decode and visualize intra-tumoral spatial heterogeneity by expanding the tumor volume to a two-dimensional (2D) matrix in voxels while preserving the spatial locality of the neighboring voxels. The proposed method was validated using three-dimensional (3D) volumes constructed from lung nodules from the LIDC-IDRI dataset, regular axial plane images, and 3D blocks. Results Dimensionality reduction of the Hilbert volume with a single regular axial plane image showed a sparse and scattered pixel distribution on the corresponding 2D matrix. However, for 3D blocks and lung tumor inside the volume, the dimensionality reduction to the 2D matrix indicated regular and concentrated squares and rectangles. For classification into benign and malignant masses using lung nodules from the LIDC-IDRI dataset, the Inception-V4 indicated that the Hilbert matrix images improved accuracy (85.54% vs. 73.22%, p < 0.001) compared to the original CT images of the test dataset. Conclusions Our study indicates that Hilbert curve-based spatial correspondence mapping is promising for decoding intra-tumoral spatial heterogeneity of partial or whole tumor samples on radiological images. This spatial-locality-preserving approach for voxel expansion enables existing radiomics and convolution neural networks to filter structured and spatially correlated high-dimensional intra-tumoral heterogeneity.

Revisiting kinorhynch segmentation: variation of segmental patterns in the nervous system of three aberrant species

Background Kinorhynch segmentation differs from the patterns found in Chordata, Arthropoda and Annelida which have coeloms and circulatory systems. Due to these differences and their obsolete status as ‘Aschelminthes’, the microscopic kinorhynchs are often not acknowledged as segmented bilaterians. Yet, morphological studies have shown a conserved segmental arrangement of ectodermal and mesodermal organ systems with spatial correspondence along the anterior-posterior axis. However, a few aberrant kinorhynch lineages present a worm-like body plan with thin cuticle and less distinct segmentation, and thus their study may aid to shed new light on the evolution of segmental patterns within Kinorhyncha. Results Here we found the nervous system in the aberrant Cateria styx and Franciscideres kalenesos to be clearly segmental, and similar to those of non-aberrant kinorhynchs; hereby not mirroring their otherwise aberrant and posteriorly shifted myoanatomy. In Zelinkaderes yong, however, the segmental arrangement of the nervous system is also shifted posteriorly and misaligned with respect to the cuticular segmentation. Conclusions The morphological disparity together with the distant phylogenetic positions of F. kalenesos, C. styx and Z. yong support a convergent origin of aberrant appearances and segmental mismatches within Kinorhyncha.

Effects of Thresholding on Voxel-Wise Correspondence of Breath-Hold and Resting-State Maps of Cerebrovascular Reactivity

Functional magnetic resonance imaging for presurgical brain mapping enables neurosurgeons to identify viable tissue near a site of operable pathology which might be at risk of surgery-induced damage. However, focal brain pathology (e.g., tumors) may selectively disrupt neurovascular coupling while leaving the underlying neurons functionally intact. Such neurovascular uncoupling can result in false negatives on brain activation maps thereby compromising their use for surgical planning. One way to detect potential neurovascular uncoupling is to map cerebrovascular reactivity using either an active breath-hold challenge or a passive resting-state scan. The equivalence of these two methods has yet to be fully established, especially at a voxel level of resolution. To quantitatively compare breath-hold and resting-state maps of cerebrovascular reactivity, we first identified threshold settings that optimized coverage of gray matter while minimizing false responses in white matter. When so optimized, the resting-state metric had moderately better gray matter coverage and specificity. We then assessed the spatial correspondence between the two metrics within cortical gray matter, again, across a wide range of thresholds. Optimal spatial correspondence was strongly dependent on threshold settings which if improperly set tended to produce statistically biased maps. When optimized, the two CVR maps did have moderately good correspondence with each other (mean accuracy of 73.6%). Our results show that while the breath-hold and resting-state maps may appear qualitatively similar they are not quantitatively identical at a voxel level of resolution.

A Comparative Analysis of Two Major Approaches for Mapping the Wildland-Urban Interface: A Case Study in California

The Wildland Urban Interface (WUI) is where human settlements border or intermingle with undeveloped land, often with multiple detrimental consequences. Therefore, mapping the WUI is required in order to identify areas-at-risk. There are two main WUI mapping methods, the point-based approach and the zonal approach. Both differ in data requirements and may produce considerably different maps, yet they were never compared before. My objective was to systematically compare the point-based and the zonal-based WUI maps of California, and to test the efficacy of a new database of building locations in the context of WUI mapping. I assessed the spatial accuracy of the building database, and then compared the spatial patterns of WUI maps by estimating the effect of multiple ancillary variables on the amount of agreement between maps. I found that the building database is highly accurate and is suitable for WUI mapping. The point-based approach estimated a consistently larger WUI area across California compared to the zonal approach. The spatial correspondence between maps was low-to-moderate, and was significantly affected by building numbers and by their spatial arrangement. The discrepancy between WUI maps suggests that they are not directly comparable within and across landscapes, and that each WUI map should serve a distinct practical purpose.

Person Re-Identification Based on Significant Color With the Spatial Correspondence

Video surveillance has played a key role to find an individual just in case of a criminal offense. More studies were done to make the surveillance process autonomous. In this, the person re-identification technique helps to identify people. The surveillance cameras are normally mounted at a height above the head of a person. With such a position of camera, it is difficult to identify the person. Therefore, video surveillance is an application in real time. The images of the same individual may vary appreciably based on different camera field of view. Color content in an image remains an important cue to identify a person. Under the assumption that the clothing color remains unchanged over the period of surveillance, a method based on significant colors with its spatial correspondence in image is proposed. The method is applied on standard data sets like GRID, PRID450s and VIPER. The results are plotted as cumulative matching characteristic curve and compared with other methods. The approach is both computationally efficient and delivers better performance.

Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinson’s Disease

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. In the absence of other quantitative approaches, a point-based set of anatomical fiducials (AFIDs) was recently developed and validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, specifically focussing on structural magnetic resonance images (MRI) obtained from patients with Parkinson’s Disease (PD). We first confirmed that AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. With localization error established, we evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow for MRI scans. We found that registration errors from this workflow as measured using AFIDs were higher than previously reported suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFID points as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs around the left temporal horn, brainstem and pineal gland in the clinical group, structures that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating accurate aggregation of imaging datasets and comparisons between various neurological conditions.