Image Co-registration Research Articles

Simultaneous acquisition of water, fat and velocity images using a phase-contrast 3p-Dixon method.

Phase-contrast MRI (2D PC-MRI) and Dixon techniques share the characteristic that the difference in frequency between water and fat, as well as the velocity, are encoded in the phase of the MR signal. We propose to take advantage of this characteristic to obtain both sets of images simultaneously. Such an acquisition will improve efficiency by obtaining both types of images in the same scan and will provide co-registered images of water-fat species and velocity images. This, in turn, will correct fat artifacts due to chemical shift in PC-MRI based measurements. This study presents a novel PC multi-echo (PCME-MRI) sequence jointly with a 3-point (3p-) Dixon pipeline that enables reconstruction of water, fat, and velocity images simultaneously. The proposed 3p-Dixon approach preserves the phase information of water-fat images, while velocity images are obtained from the resulting water components. Numerical phantom tests and 2D MR axial images of the neck acquired in 12 healthy volunteers demonstrated the feasibility of the PC 3p-Dixon method, showing comparable performance to standard techniques. In volunteers the median and range MAE comparing PC 3p-Dixon, and standard 3p-Dixon fat fraction were 0.06 and [0.03, 0.09]. The median and range of velocity for PC 3p-Dixon were 6.15 ml and [3.86, 7.21]ml, compared to 6.43 ml and [4.62, 8.27]ml obtained by 2D PC-MRI. Numerical phantom experiments and acquisitions from healthy volunteers showed promising results in fat fraction and velocity estimation of PC 3p-Dixon compared with standard 3p-Dixon and 2D PC-MRI, obtaining both data sets in similar times as standard 3p Dixon.

Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.

This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. A total of twenty-one patients with biopsy-proven prostate cancer (Gleason score 4+3 or higher) were included. Before transperineal biopsy guided by low-field (58-74mT) MRI, a radiologist annotated suspicious regions of interest (ROIs) on high-field (3T) MRI. Rigid image registration was performed to align corresponding regions on both high- and low-field images, ensuring an accurate propagation of annotations to the co-registered low-field images for texture feature calculations. For each cancerous ROI, a matching ROI of identical size was drawn in a non-suspicious region presumed to be normal tissue. Four Haralick texture features (Energy, Correlation, Contrast, and Homogeneity) were extracted and compared between cancerous and non-suspicious ROIs. Two extraction methods were used: the direct computation of texture measures within the ROIs and a sliding window technique generating texture maps across the prostate from which average values were derived. The results demonstrated statistically significant differences in texture features between cancerous and non-suspicious regions. Specifically, Energy and Homogeneity were elevated (p-values: <0.00001-0.004), while Contrast and Correlation were reduced (p-values: <0.00001-0.03) in cancerous ROIs. These findings suggest that Haralick texture features are both feasible and informative for differentiating abnormalities, offering promise in assisting prostate cancer detection on low-field MRI.

Integrative co-registration of elemental imaging and histopathology for enhanced spatial multimodal analysis of tissue sections through TRACE.

Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of whole slide images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration. Available on the following platforms-GitHub: jlevy44/trace_app, PyPI: trace_app, Docker: joshualevy44/trace_app, Singularity: docker://joshualevy44/trace_app.

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.

Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visible, near-infrared (VNIR), and shortwave infrared (SWIR) regions. To fully utilize the spectral and texture features of the full VNIR and SWIR spectral domains, a computer-vision-aided image co-registration methodology was implemented to seamlessly align the VNIR and SWIR bands. Sensitivity analyses were also conducted to identify the most sensitive bands for seed protein estimation. Convolutional neural networks (CNNs) with attention mechanisms were proposed along with traditional machine learning models based on feature engineering including Random Forest (RF) and Support Vector Machine (SVM) regression for comparative analysis. Additionally, the CNN classification approach was used to estimate low, medium, and high protein concentrations because this type of classification is more applicable for breeding efforts. Our results showed that the proposed CNN with attention mechanisms predicted wheat protein content with R2 values of 0.70 and 0.65 for ventral and dorsal seed orientations, respectively. Although, the R2 of the CNN approach was lower than of the best performing feature-based method, RF (R2 of 0.77), end-to-end prediction capabilities with CNN hold great promise for the automation of wheat protein estimation for breeding. The CNN model achieved better classification of protein concentrations between low, medium, and high protein contents, with an R2 of 0.82. This study's findings highlight the significant potential of hyperspectral imaging and machine learning techniques for advancing precision breeding practices, optimizing seed sorting processes, and enabling targeted agricultural input applications.

Multisegment Parallel Coregistration of Sentinel-1 SAR Time-Series Images by Combining OpenMP With MPI

Multisegment Parallel Coregistration of Sentinel-1 SAR Time-Series Images by Combining OpenMP With MPI

GeoAI Dataset for Urban Water Body Detection Using TerraSAR-X Satellite Radar Imagery

This study presents the generation of a GeoAI dataset for urban water body detection using TerraSAR-X satellite synthetic aperture radar (SAR) imagery. The study area includes urban regions in Seoul and Gyeonggi Province, chosen for their complex structures and frequent flooding, which pose challenges for SAR analysis. The data preprocessing involved generating Sigma0 images, image co-registration, median filtering for speckle noise reduction, decibel conversion, and orthorectification using Copernicus DEM for precise geometric correction. Label data were created using the global river widths from Landsat dataset combined with the Otsu thresholding method and fine-tuned with Google Map imagery. Annotation guidelines were meticulously designed to account for SAR-specific phenomena such as layover, corner reflections, and side lobe effects, ensuring consistent and accurate labeling across different orbits and observation conditions. The resulting dataset supports deep learning models in learning geometric characteristics of SAR imagery, enhancing water body detection capabilities. This work provides a foundational resource for future applications in urban water management and climate-resilient disaster response.

Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning.

Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and limited distinctive cytologic features of lymphocyte subpopulations as seen with hematoxylin and eosin (H&E) staining. Although immunohistochemistry is necessary for definitive characterization of T- and B-cell compartments, routine toxicologic assessments are based solely on H&E slides. Here, a deep learning (DL) model was developed using normal rats to quantify relevant compartments of the spleen, including periarteriolar lymphoid sheaths, follicles, germinal centers, and marginal zones from H&E slides. Slides were scanned, destained, dual labeled with CD3 and CD79a chromogenic immunohistochemistry, and rescanned to generate exact co-registered images that served as the ground truth for training and validation. The DL model identified individual splenic compartments with high accuracy (97.8% Dice similarity coefficient) directly from H&E-stained tissue. The DL model was utilized to study the normal range of lymphoid compartment area and cellularity. Future implementation of our DL model and expanding this approach to other lymphoid tissues have the potential to improve accuracy and precision in enhanced histopathology evaluation of the immune system with concurrent gains in time efficiency for the pathologist.

PET/MRI evaluation of hepatobiliary tumors.

Positron-emission tomography magnetic resonance imaging (PET/MRI) has emerged as a powerful hybrid molecular imaging technique in clinical practice, overcoming initial technical challenges to provide comprehensive anatomic and metabolic information. This advanced modality combines the superior soft tissue contrast of MRI with the metabolic insights of PET, offering advantages in hepatobiliary imaging, including improved detection of small liver metastases and reduced radiation exposure. The evolution of PET/MRI technology has been marked by significant advancements, such as the development of MRI-compatible PET detectors and sophisticated motion compensation techniques. These innovations have enhanced image quality and co-registration accuracy, crucial for hepatobiliary imaging. The integration of time-of-flight capability and silicon photomultipliers has further improved spatial resolution and sensitivity. PET/MRI protocols for liver imaging typically involve a whole-body scan followed by a targeted liver examination, utilizing radiotracers like FDG and DOTATATE. This approach allows for comprehensive staging and detailed liver assessment in a single session, potentially altering management decisions in up to 30% of patients with intrahepatic cholangiocarcinoma. While PET/MRI excels in characterizing various hepatobiliary lesions, including hepatocellular carcinoma and cholangiocarcinoma, challenges remain in differentiating certain benign entities like small hemangiomas from metastases. Ongoing research and clinical experience continue to refine the role of PET/MRI in hepatobiliary imaging, promising improved diagnostic accuracy and patient care.

Ground moving target indication of polarimetric interferometric synthetic aperture radar using joint scattering vector

AbstractGround moving target indication (GMTI) plays a critical role in both civilian applications and military applications. The accuracy of complex images coregistration is a major factor for multichannel synthetic aperture radar (SAR) GMTI. Moreover, polarimetric interferometric SAR (PolInSAR) represents a growing strength in multi‐function radar systems. In response to this, we propose a new SAR‐GMTI approach tailored for the PolInSAR system. To address the deterioration in clutter suppression performance caused by the coregistration errors, we construct a joint scattering vector (JSV) for polarimetric SAR (PolSAR) by incorporating the neighbouring vectors of the central pixel. Subsequently, the covariance matrix of JSV is estimated and subjected to eigen‐decomposed. Clutter suppression of the PolInSAR system for GMTI is performed using eigen‐subspace projection, where the JSV is projected onto noise subspace. This method is robust against coregistration errors since all ground moving target information is encapsulated within the JSV. Furthermore, the improvement factors with different clutter backgrounds (by changing the signal‐to‐clutter‐plus‐noise‐ratio, SCNR) are analysed. The effectiveness of the proposed JSV‐based method is validated using simulated PolInSAR data.

Consensus Guidelines for Delineation of Clinical Target Volumes for Intensity Modulated Radiation Therapy for Intact Cervical Cancer: An Update

PurposeAccurate target delineation is essential when using intensity-modulated radiotherapy (IMRT) for intact cervical cancer. In 2011, the Radiation Therapy Oncology Group (RTOG) published a consensus guideline using magnetic resonance imaging (MRI). The current project expands on the previous atlas by including computed tomography (CT)-based contours, contours with MRI and positron- emission- tomography (PET) registrations, the addition of common and complex scenarios, and to incorporate information on simulation and treatment planning techniques. Methods and MaterialsTwenty-eight experts in gynecologic radiation oncology contoured three cases, first on a non-contrast CT simulation scan, then with registered diagnostic scans. The cases included (1) FIGO IIIC1 with a bulky tumor and vaginal metastasis, (2) FIGO IIB with calcified uterine fibromas, and (3) FIGO IIIC2 with large lymph nodes. The contours on all six datasets (three CT simulations without diagnostic images and three with registered images) were analyzed for consistency of delineation using an expectation-maximization algorithm for simultaneous truth and performance level estimation (STAPLE) with kappa statistics as a measure of agreement. The contours were reviewed, discussed, and edited in a group meeting prior to finalizing. ResultsContours showed considerable agreement between experts in each of the cases with kappa statistics of 0.67-0.72. For each case, diagnostic PET±MRI was associated with an increase in volume. The largest increase was the CTV primary for Case 2 with a 20% increase in volume and 54% increase in STAPLE estimate volume, which may be due to variance in registration priorities. For the third case, 92.9% increased their CTVs based on the addition of the diagnostic PET scan. The main areas of variance were in determining the superior extent of CTV coverage, coverage of the mesorectum, and simulation and planning protocols. ConclusionsThis study shows the value as well as the challenges of using co-registered diagnostic imaging, with an average increase in volumes when incorporating MRI and PET.

Deformable Mapping of Rectal Cancer Whole-Mount Histology with Restaging MRI at Voxel Scale: A Feasibility Study.

Purpose To develop a radiology-pathology coregistration method for 1:1 automated spatial mapping between preoperative rectal MRI and ex vivo rectal whole-mount histology (WMH). Materials and Methods This retrospective study included consecutive patients with rectal adenocarcinoma who underwent total neoadjuvant therapy followed by total mesorectal excision with preoperative rectal MRI and WMH from January 2019 to January 2022. A gastrointestinal pathologist and a radiologist established three corresponding levels for each patient at rectal MRI and WMH, subsequently delineating external and internal rectal wall contours and the tumor bed at each level and defining eight point-based landmarks. An advanced deformable image coregistration model based on the linearized iterative boundary reconstruction (LIBR) approach was compared with rigid point-based registration (PBR) and state-of-the-art deformable intensity-based multiscale spectral embedding registration (MSERg). Dice similarity coefficient (DSC), modified Hausdorff distance (MHD), and target registration error (TRE) across patients were calculated to assess the coregistration accuracy of each method. Results Eighteen patients (mean age, 54 years ± 13 [SD]; nine female) were included. LIBR demonstrated higher DSC versus PBR for external and internal rectal wall contours and tumor bed (external: 0.95 ± 0.03 vs 0.86 ± 0.04, respectively, P < .001; internal: 0.71 ± 0.21 vs 0.61 ± 0.21, P < .001; tumor bed: 0.61 ± 0.17 vs 0.52 ± 0.17, P = .001) and versus MSERg for internal rectal wall contours (0.71 ± 0.21 vs 0.63 ± 0.18, respectively; P < .001). LIBR demonstrated lower MHD versus PBR for external and internal rectal wall contours and tumor bed (external: 0.56 ± 0.25 vs 1.68 ± 0.56, respectively, P < .001; internal: 1.00 ± 0.35 vs 1.62 ± 0.59, P < .001; tumor bed: 2.45 ± 0.99 vs 2.69 ± 1.05, P = .03) and versus MSERg for internal rectal wall contours (1.00 ± 0.35 vs 1.62 ± 0.59, respectively; P < .001). LIBR demonstrated lower TRE (1.54 ± 0.39) versus PBR (2.35 ± 1.19, P = .003) and MSERg (2.36 ± 1.43, P = .03). Computation time per WMH slice for LIBR was 35.1 seconds ± 12.1. Conclusion This study demonstrates feasibility of accurate MRI-WMH coregistration using the advanced LIBR method. Keywords: MR Imaging, Abdomen/GI, Rectum, Oncology Supplemental material is available for this article. © RSNA, 2024.

Robust Sample Information Retrieval in Dark-Field Computed Tomography With a Vibrating Talbot-Lau Interferometer.

X-ray computed tomography (CT) is a crucial tool for non-invasive medical diagnosis that uses differences in materials' attenuation coefficients to generate contrast and provide 3D information. Grating-based dark-field-contrast X-ray imaging is an innovative technique that utilizes small-angle scattering to generate additional co-registered images with additional microstructural information. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. However, the susceptibility of Talbot-Lau interferometers to mechanical vibrations coupled with a need to minimize data acquisition times has hindered its application in clinical routines and the combination of X-ray dark-field imaging and large field-of-view (FOV) tomography in the past. In this work, we propose a processing pipeline to address this issue in a human-sized clinical dark-field CT prototype. We present the corrective measures that are applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. This is achieved by identifying spatially and temporally variable vibrations in air reference scans. By translating the found correlations to the sample scan, we can identify and mitigate relevant fluctuation modes for scans with arbitrary sample sizes. This approach effectively eliminates the requirement for sample-free detector area, while still distinctly separating fluctuation and sample information. As a result, samples of arbitrary dimensions can be reconstructed without being affected by vibration artifacts. To demonstrate the viability of the technique for human-scale objects, we present reconstructions of an anthropomorphic thorax phantom.

Erosion of the temporal bone by vestibular schwannoma: morphometrics and predictive modeling.

To perform a comprehensive morphometric analysis of vestibular schwannomas (VS) using multimodal imaging, focusing on the relationship between tumor characteristics and internal acoustic canal (IAC) changes. We analyzed a cohort of patients undergoing radiosurgery for VS, utilizing high-definition MRI and bone CT for detailed anatomical assessment. Image co-registration and fusion techniques were employed to examine VS and IAC dimensions. Advanced statistical methods, including logistic regression, were applied to identify patterns of IAC dilation and establish predictive indicators for these morphological changes. The study included 659 patients (51.1% female, mean age 56.37years) with evenly distributed tumor lateralization. Koos grades were I (22.9%), II (29.9%), III (38.2%), IVa (8.9%), and IVb (0.3%). Most tumors (90.9%) extended both inside and outside the IAC. Ipsilateral IAC (IIAC) dimensions were significantly larger than contralateral, with IIAC volume 49% greater (p < .0001). Higher Koos grades correlated with increased intra-canalicular lesion volume (icLV), which was strongly associated with IIAC size. Logistic regression identified icLV as the strongest predictor of IIAC dilation (AUC = 0.7674, threshold = 137.52 mm3). The icLV appears central to the pathophysiological development of VS and its impact on IAC anatomy. While limited by a selective patient base and static imaging data, these findings enhance the understanding of VS-IAC interactions, offering insights for improved clinical management and further research.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

Recent Advances in Image-Guided Tissue Sampling.

Recent advances in image-guided tissue sampling have enhanced diagnostic medicine, particularly in oncology. Traditional techniques, such as computed tomography (CT)-, ultrasound (US)-, and magnetic resonance imaging (MRI)-guided biopsies, remain the cornerstone of diagnostic interventions, each offering unique advantages based on tissue characteristics. CT-guided biopsies excel in deeper complex lesions, while US-guided biopsies provide real-time imaging ideal for superficial tissues. MRI-guided biopsies are invaluable for soft tissue evaluations. The emergence of fusion imaging, which combines modalities such as positron emission tomography (PET)/CT or MRI/US, has demonstrated enhanced diagnostic accuracy. Despite these advantages, image co-registration and cost are the main drawbacks. Emerging techniques such as molecular breast imaging (MBI) and shear wave elastography (SWE) have been evaluated, particularly for breast cancer; however, research suggests that US is likely to remain the most effective modality due to both its cost and ease of use. Innovations in biopsy navigation, including augmented reality, "hot needles," and robotic assistance, demonstrate promise in closing the gap between operator dependency and procedural consistency; however, further research is required. While liquid biopsies show promise in non-invasive early cancer detection, they are not yet ready to replace tissue biopsies. Collectively, these advancements indicate a future where image-guided tissue sampling is more targeted, less invasive, and diagnostically accurate, although cost and technology access remain challenges.

Method for co-registration of high-resolution specimen PET-CT with histopathology to improve insight into radiotracer distributions

BackgroundAs the spatial resolution of positron emission tomography (PET) scanners improves, understanding of radiotracer distributions in tissues at high resolutions is important. Hence, we propose a method for co-registration of high-resolution ex vivo specimen PET images, combined with computed tomography (CT) images, and the corresponding specimen histopathology.MethodsWe applied our co-registration method to breast cancer (BCa) specimens of patients who were preoperatively injected with 0.8 MBq/kg [18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F]fluorodeoxyglucose ([18F]FDG). The method has two components. First, we used an image acquisition scheme that minimises and tracks tissue deformation: (1) We acquired sub-millimetre (micro)-PET-CT images of ±2 mm-thick lamellas of the fresh specimens, enclosed in tissue cassettes. (2) We acquired micro-CT images of the same lamellas after formalin fixation to visualise tissue deformation. (3) We obtained 1 hematoxylin and eosin (H&E) stained histopathology section per lamella of which we captured a digital whole slide image (WSI). Second, we developed an automatic co-registration algorithm to improve the alignment between the micro-PET-CT images and WSIs, guided by the micro-CT of the fixated lamellas. To estimate the spatial co-registration error, we calculated the distance between corresponding microcalcifications in the micro-CTs and WSIs. The co-registered images allowed to study standardised uptake values (SUVs) of different breast tissues, as identified on the WSIs by a pathologist.ResultsWe imaged 22 BCa specimens, 13 cases of invasive carcinoma of no special type (NST), 6 of invasive lobular carcinoma (ILC), and 3 of ductal carcinoma in situ (DCIS). While the cassette framework minimised tissue deformation, the best alignment between the micro-PET-CT images and WSIs was achieved after deformable co-registration. We found an overall average co-registration error of 0.74 ± 0.17 mm between the micro-PET images and WSIs. (Pre)malignant tissue (including NST, ILC, and DCIS) generally showed higher SUVs than healthy tissue (including healthy glandular, connective, and adipose tissue). As expected, inflamed tissue and skin also showed high uptake.ConclusionsWe developed a method to co-register micro-PET-CT images of surgical specimens and WSIs with an accuracy comparable to the spatial resolution of the micro-PET images. While currently, we only applied this method to BCa specimens, we believe this method is applicable to a wide range of specimens and radiotracers, providing insight into distributions of (new) radiotracers in human malignancies at a sub-millimetre resolution.

Definition of a coordinate system for multi-modal images of the temporal bone and inner ear.

The position and orientation of the head is maintained to be relatively similar during the CT / MR imaging process. However, the position / orientation dissimilarities present in the resulting images between patients, or between different scans of the same patient, do not allow for direct comparison of the images themselves or features / metrics extracted from them. This paper introduces a method of defining a coordinate system which is consistent between patients and modalities (CT and MR) for images of the temporal bone, using easily identifiable landmarks within the semicircular canals. Cone Beam CT and high resolution MRI (T2) images of the temporal bone from 20 patients with no cochlear or temporal bone pathology in either modality were obtained. Four landmarks within the semicircular canals were defined that can be identified in both modalities. A coordinate system was defined using these landmarks. Reproducibility of landmark selection was assessed using intra- and inter-rater reliability (for three expert raters and two repeats of the landmark selection). Accuracy of the coordinate system was determined by comparing the coordinates of two additional landmarks in CT and MR images after their conversion to the proposed coordinate system. Intraclass Correlation Coefficients at a 95% level of confidence showed significant agreement within and between raters as well as between modalities. The differences between selections, raters, and modalities (as measured using mean, standard deviation, and maximum) were low and acceptable for clinical applications. The proposed coordinate system is suited for use in images of the temporal bone and inner ear. Its multi-modal nature enables the coordinate system to be used in tasks such as image co-registration.

Baseline Blood Pressure Was Associated with Hemispheric Cerebral Blood Flow in Acute Small Subcortical Infarcts

Introduction: While increased baseline blood pressure (BP) is a prevalent comorbidity in the acute phase of ischemic stroke, the association between baseline BP and the state of hemispheric perfusion in patients with acute small subcortical infarcts (SSIs) has not been studied in detail. The aim of this study was to investigate the relationship between baseline BP and hemispheric cerebral blood flow (CBF) in acute SSIs. Methods: This retrospective study included 101 patients with acute SSIs. Baseline hemispheric CBF was assessed through co-registration of baseline CT perfusion imaging and follow-up diffusion-weighted imaging. The association between baseline BP, CBF, and different cerebral small vessel disease (CSVD) biomarkers was assessed. Results: Baseline systolic BP (SBP) and diastolic BP (DBP) were negatively associated with contralateral hemispheric CBF after multivariate-adjusted linear analysis (SBP: β = −0.001, 95% CI: −0.002 to 0.000, p = 0.030; DBP: β = −0.002, 95% CI: −0.003∼0.001, p = 0.006). Among other CSVD biomarkers, the presence of any cerebral microbleeds showed a significant association with lower CBF in the contralateral hemisphere of the infarct lesion (r = −0.270, p = 0.035). Conclusion: In patients with acute SSIs, increased baseline BP was associated with reduced CBF in the contralateral hemisphere of the infarct lesion, which probably could be interpreted by the exacerbation of the CSVD burden, suggesting a potential mechanistic link between BP autoregulation dysfunction and the aggravation of neurovascular impairment in SSIs.

Rectifying Discrepancies Between GB-SAR Images and Terrain Model Caused by Measurement Deviations in Open-Pit Mines

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720[Formula: see text]m (@700[Formula: see text]m average monitoring range) when the average density of the terrain point cloud is 0.5[Formula: see text]m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications.

Abstract C016: Optical Techniques to Democratize Intravital Quantification of Metabolic Heterogeneity

Abstract Since metabolic and vascular adaptations are essential for tumor cell survival, a technique capable of tracking metabolic heterogeneity at both the bulk tumor and cellular scales over extended timelines is necessary. To address this technological need, we have developed a multi-scale optical microscope relying on exogenous fluorescent reporters for dynamic spatiotemporal imaging of major metabolic pathways in vivo. This microscope utilizes a low-cost CMOS detector and LEDs for excitation to enable fluorescence microscopy in resource-limited settings. Here, we implement our system to assess changes in bulk tumor and cellular metabolic heterogeneity as vasculature is perturbed. 4T1 breast tumor-bearing mice were treated with combretastatin A-1 (a vascular disruptor), implanted with window chambers, and temporally imaged (n=5/group). Fluorescence imaging and Gabor filter/Djikstra segmentation approaches enabled metabolic and vascular analyses, respectively. We demonstrated the system’s ability to acquire co-registered images of vasculature, morphology, and distinct metabolic endpoints reporting on glucose uptake, fatty acid uptake, and mitochondrial metabolism at both wide-field and high resolutions. At 12 hours post-treatment, the tumors’ metabolism shifted from mitochondrial respiration to glycolysis in avascular regions. Further, we report a higher average vascular density in untreated versus treated mice at 108 hours post-treatment (p&amp;lt;0.05, Kolmogorov-Smirnov test). On exploring the relationship between metabolic activity and vasculature, regions with high local tortuosity in the untreated group exhibited a higher reliance on fatty acid uptake than high tortuosity regions in the treatment group, indicating these cells’ reliance on fatty acids within a complex vascular network. Our multiplexed imaging strategy enables the quantification of tumor metabolism and vasculature over multiple length scales, which could be leveraged to study adaptations following clinical therapies. Citation Format: Enakshi Sunassee. Optical Techniques to Democratize Intravital Quantification of Metabolic Heterogeneity [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C016.