Metabolic profiling of galectin-1 and galectin-3: a cross-sectional, multi-omics, association study

OBJECTIVES

Experimental studies indicate a role for galectin-1 and galectin-3 in metabolic disease, but clinical evidence from larger populations is limited.

METHODS

We measured circulating levels of galectin-1 and galectin-3 in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study, participants (n = 502, all aged 50 years) and characterized the individual association profiles with metabolic markers, including clinical measures, metabolomics, adipose tissue distribution (Imiomics) and proteomics.

RESULTS

Galectin-1 and galectin-3 were associated with fatty acids, lipoproteins and triglycerides including lipid measurements in the metabolomics analysis adjusted for body mass index (BMI). Galectin-1 was associated with several measurements of adiposity, insulin secretion and insulin sensitivity, while galectin-3 was associated with triglyceride-glucose index (TyG) and fasting insulin levels. Both galectins were associated with inflammatory pathways and fatty acid binding protein (FABP)4 and -5-regulated triglyceride metabolic pathways. Galectin-1 was also associated with several proteins related to adipose tissue differentiation.

CONCLUSIONS

The association profiles for galectin-1 and galectin-3 indicate overlapping metabolic effects in humans, while the distinctly different associations seen with fat mass, fat distribution, and adipose tissue differentiation markers may suggest a functional role of galectin-1 in obesity.

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.

Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies

BACKGROUND

Computed tomography (CT) is an imaging modality commonly used for studies of internal body structures and very useful for detailed studies of body composition. The aim of this study was to develop and evaluate a fully automatic image registration framework for inter-subject CT slice registration. The aim was also to use the results, in a set of proof-of-concept studies, for voxel-wise statistical body composition analysis (Imiomics) of correlations between imaging and non-imaging data.

METHODS

The current study utilized three single-slice CT images of the liver, abdomen, and thigh from two large cohort studies, SCAPIS and IGT. The image registration method developed and evaluated used both CT images together with image-derived tissue and organ segmentation masks. To evaluate the performance of the registration method, a set of baseline 3-single-slice CT images (from 2780 subjects including 8285 slices) from the SCAPIS and IGT cohorts were registered. Vector magnitude and intensity magnitude error indicating inverse consistency were used for evaluation. Image registration results were further used for voxel-wise analysis of associations between the CT images (as represented by tissue volume from Hounsfield unit and Jacobian determinant) and various explicit measurements of various tissues, fat depots, and organs collected in both cohort studies.

RESULTS

Our findings demonstrated that the key organs and anatomical structures were registered appropriately. The evaluation parameters of inverse consistency, such as vector magnitude and intensity magnitude error, were on average less than 3 mm and 50 Hounsfield units. The registration followed by Imiomics analysis enabled the examination of associations between various explicit measurements (liver, spleen, abdominal muscle, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), thigh SAT, intermuscular adipose tissue (IMAT), and thigh muscle) and the voxel-wise image information.

CONCLUSION

The developed and evaluated framework allows accurate image registrations of the collected three single-slice CT images and enables detailed voxel-wise studies of associations between body composition and associated diseases and risk factors.

Improved automated tumor segmentation in whole-body 3D scans using multi-directional 2D projection-based priors

Early cancer detection, guided by whole-body imaging, is important for the overall survival and well-being of the patients. While various computer-assisted systems have been developed to expedite and enhance cancer diagnostics and longitudinal monitoring, the detection and segmentation of tumors, especially from whole-body scans, remain challenging. To address this, we propose a novel end-to-end automated framework that first generates a tumor probability distribution map (TPDM), incorporating prior information about the tumor characteristics (e.g. size, shape, location). Subsequently, the TPDM is integrated with a state-of-the-art 3D segmentation network along with the original PET/CT or PET/MR images. This aims to produce more meaningful tumor segmentation masks compared to using the baseline 3D segmentation network alone. The proposed method was evaluated on three independent cohorts (autoPET, CAR-T, cHL) of images containing different cancer forms, obtained with different imaging modalities, and acquisition parameters and lesions annotated by different experts. The evaluation demonstrated the superiority of our proposed method over the baseline model by significant margins in terms of Dice coefficient, and lesion-wise sensitivity and precision. Many of the extremely small tumor lesions (i.e. the most difficult to segment) were missed by the baseline model but detected by the proposed model without additional false positives, resulting in clinically more relevant assessments. On average, an improvement of 0.0251 (autoPET), 0.144 (CAR-T), and 0.0528 (cHL) in overall Dice was observed. In conclusion, the proposed TPDM-based approach can be integrated with any state-of-the-art 3D UNET with potentially more accurate and robust segmentation results.

Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders

Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine-grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.

Automatic segmentation of large-scale CT image datasets for detailed body composition analysis

BACKGROUND

Body composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.

METHODS

The study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.

RESULTS

The Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909-0.996), UNET++ 0.981 (0.927-0.996), Ghost-UNET 0.961 (0.904-0.991), and Ghost-UNET++ 0.968 (0.910-0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.

CONCLUSION

Fully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.

A whole-body diffusion MRI normal atlas: development, evaluation and initial use

BACKGROUND

Statistical atlases can provide population-based descriptions of healthy volunteers and/or patients and can be used for region- and voxel-based analysis. This work aims to develop whole-body diffusion atlases of healthy volunteers scanned at 1.5T and 3T. Further aims include evaluating the atlases by establishing whole-body Apparent Diffusion Coefficient (ADC) values of healthy tissues and including healthy tissue deviations in an automated tumour segmentation task.

METHODS

Multi-station whole-body Diffusion Weighted Imaging (DWI) and water-fat Magnetic Resonance Imaging (MRI) of healthy volunteers (n = 45) were acquired at 1.5T (n = 38) and/or 3T (n = 29), with test-retest imaging for five subjects per scanner. Using deformable image registration, whole-body MRI data was registered and composed into normal atlases. Healthy tissue ADCmean was manually measured for ten tissues, with test-retest percentage Repeatability Coefficient (%RC), and effect of age, sex and scanner assessed. Voxel-wise whole-body analyses using the normal atlases were studied with ADC correlation analyses and an automated tumour segmentation task. For the latter, lymphoma patient MRI scans (n = 40) with and without information about healthy tissue deviations were entered into a 3D U-Net architecture.

RESULTS

Sex- and Body Mass Index (BMI)-stratified whole-body high b-value DWI and ADC normal atlases were created at 1.5T and 3T. %RC of healthy tissue ADCmean varied depending on tissue assessed (4-48% at 1.5T, 6-70% at 3T). Scanner differences in ADCmean were visualised in Bland-Altman analyses of dually scanned subjects. Sex differences were measurable for liver, muscle and bone at 1.5T, and muscle at 3T. Volume of Interest (VOI)-based multiple linear regression, and voxel-based correlations in normal atlas space, showed that age and ADC were negatively associated for liver and bone at 1.5T, and positively associated with brain tissue at 1.5T and 3T. Adding voxel-wise information about healthy tissue deviations in an automated tumour segmentation task gave numerical improvements in the segmentation metrics Dice score, sensitivity and precision.

CONCLUSIONS

Whole-body DWI and ADC normal atlases were created at 1.5T and 3T, and applied in whole-body voxel-wise analyses.

An image registration method for voxel-wise analysis of whole-body oncological PET-CT

Whole-body positron emission tomography-computed tomography (PET-CT) imaging in oncology provides comprehensive information of each patient's disease status. However, image interpretation of volumetric data is a complex and time-consuming task. In this work, an image registration method targeted towards computer-aided voxel-wise analysis of whole-body PET-CT data was developed. The method used both CT images and tissue segmentation masks in parallel to spatially align images step-by-step. To evaluate its performance, a set of baseline PET-CT images of 131 classical Hodgkin lymphoma (cHL) patients and longitudinal image series of 135 head and neck cancer (HNC) patients were registered between and within subjects according to the proposed method. Results showed that major organs and anatomical structures generally were registered correctly. Whole-body inverse consistency vector and intensity magnitude errors were on average less than 5 mm and 45 Hounsfield units respectively in both registration tasks. Image registration was feasible in time and the nearly automatic pipeline enabled efficient image processing. Metabolic tumor volumes of the cHL patients and registration-derived therapy-related tissue volume change of the HNC patients mapped to template spaces confirmed proof-of-concept. In conclusion, the method established a robust point-correspondence and enabled quantitative visualization of group-wise image features on voxel level.

Improved geometric accuracy of whole body diffusion-weighted imaging at 1.5T and 3T using reverse polarity gradients

Whole body diffusion-weighted imaging (WB-DWI) is increasingly used in oncological applications, but suffers from misalignments due to susceptibility-induced geometric distortion. As such, DWI and structural images acquired in the same scan session are not geometrically aligned, leading to difficulties in e.g. lesion detection and segmentation. In this work we assess the performance of the reverse polarity gradient (RPG) method for correction of WB-DWI geometric distortion. Multi-station DWI and structural magnetic resonance imaging (MRI) data of healthy controls were acquired at 1.5T (n = 20) and 3T (n = 20). DWI data was distortion corrected using the RPG method based on b = 0 s/mm² (b0) and b = 50 s/mm² (b50) DWI acquisitions. Mutual information  (MI) between low b-value DWI and structural data increased with distortion correction (P < 0.05), while improvements in region of interest (ROI) based similarity metrics, comparing the position of incidental findings on DWI and structural data, were location dependent. Small numerical differences between non-corrected and distortion corrected apparent diffusion coefficient (ADC) values were measured. Visually, the distortion correction improved spine alignment at station borders, but introduced registration-based artefacts mainly for the spleen and kidneys. Overall, the RPG distortion correction gave an improved geometric accuracy for WB-DWI data acquired at 1.5T and 3T. The b0- and b50-based distortion corrections had a very similar performance.

Erratum for: MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans

[This corrects the article DOI: 10.1148/ryai.210178.].

Serum levels of perfluoroalkyl substances (PFAS) and body composition - A cross-sectional study in a middle-aged population

BACKGROUND

It has been suggested that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors with a potential to influence fat mass.

OBJECTIVE

The primary hypothesis tested was that we would find positive relationships for PFAS vs measures of adiposity.

METHODS

In 321 subjects all aged 50 years in the POEM study, five PFAS (perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA)) were measured in serum together with a Dual-energy X-ray absorptiometry (DXA) scan for determination of fat and lean mass. Whole-body magnetic resonance imaging scan was performed and the body was divided into >1 million voxels. Voxel-wise statistical analysis was carried out by a novel method denoted Imiomics.

RESULTS

PFOS and PFHxS, did not show any consistent associations with body composition. However, PFOA, and especially PFNA and PFDA, levels were inversely related to most traditional measures reflecting the amount of fat in women, but not in men. In the Imiomics analysis of tissue volume, PFDA and PFNA levels were inversely related to the volume of subcutaneous fat, mainly in the arm, trunk and hip regions in women, while no such clear relationship was seen in men. Also, the visceral fat content of the liver, the pericardium, and the gluteus muscle were inversely related to PFDA and PFNA in women.

DISCUSSION

Contrary to our hypothesis, some PFAS showed inverse relationships vs measurements of adiposity.

CONCLUSION

PFOS and PFHxS levels in plasma did not show any consistent associations with body composition, but PFOA, and especially PFNA and PFDA were inversely related to multiple measures reflecting the amount of fat, but in women only.

MIMIR: Deep Regression for Automated Analysis of UK Biobank MRI Scans

UK Biobank (UKB) has recruited more than 500 000 volunteers from the United Kingdom, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100 000 participants with 70 000 follow-up sessions will yield up to 170 000 MRI scans, enabling image analysis of body composition, organs, and muscle. This study presents an experimental inference engine for automated analysis of UKB neck-to-knee body 1.5-T MRI scans. This retrospective cross-validation study includes data from 38 916 participants (52% female; mean age, 64 years) to capture baseline characteristics, such as age, height, weight, and sex, as well as measurements of body composition, organ volumes, and abstract properties, such as grip strength, pulse rate, and type 2 diabetes status. Prediction intervals for each end point were generated based on uncertainty quantification. On a subsequent release of UKB data, the proposed method predicted 12 body composition metrics with a 3% median error and yielded mostly well-calibrated individual prediction intervals. The processing of MRI scans from 1000 participants required 10 minutes. The underlying method used convolutional neural networks for image-based mean-variance regression on two-dimensional representations of the MRI data. An implementation was made publicly available for fast and fully automated estimation of 72 different measurements from future releases of UKB image data. Keywords: MRI, Adipose Tissue, Obesity, Metabolic Disorders, Volume Analysis, Whole-Body Imaging, Quantification, Supervised Learning, Convolutional Neural Network (CNN) © RSNA, 2022.

Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy

Background and purpose

Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.

Materials and methods

4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields.

Results

The registration algorithm produced accurate registration result, in general DICE overlap >0.95 across all time frames. Cumulative variance of the eigen values from the PCA showed that 50% or more of the motion is explained in the first component for all subjects. However, the size and direction for the components differed between subjects. Adding more than two components did not improve the accuracy significantly and the model was able to explain motion down to about 1 mm.

Conclusions

An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1–2 principal components.

Uncertainty-aware body composition analysis with deep regression ensembles on UK Biobank MRI

Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44-82, since 2014. Phenotypes derived from these images, such as measurements of body composition from MRI, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this work, six measurements of body composition and adipose tissues were automatically estimated by image-based, deep regression with ResNet50 neural networks from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no output segmentations that could indicate the reliability of individual measurements. The presented experiments therefore examine uncertainty quantification with mean-variance regression and ensembling to estimate individual measurement errors and thereby identify potential outliers, anomalies, and other failure cases automatically. In 10-fold cross-validation on data of about 8500 subjects, mean-variance regression and ensembling showed complementary benefits, reducing the mean absolute error across all predictions by 12%. Both improved the calibration of uncertainties and their ability to identify high prediction errors. With intra-class correlation coefficients (ICC) above 0.97, all targets except the liver fat content yielded relative measurement errors below 5%. Testing on another 1000 subjects showed consistent performance, and the method was finally deployed for inference to 30,000 subjects with missing reference values. The results indicate that deep regression ensembles could ultimately provide automated, uncertainty-aware measurements of body composition for more than 120,000 UK Biobank neck-to-knee body MRI that are to be acquired within the coming years.

Relationships between carotid artery intima-media thickness and echogenicity and body composition using a new magnetic resonance imaging voxel-based technique

BACKGROUND

We evaluated how carotid artery intima-media thickness (IMT) and the echogenicity of the intima-media (IM-GSM), measured by ultrasound, were related to body composition, evaluated by both traditional imaging techniques, as well as with a new voxel-based "Imiomics" technique.

METHODS

In 321 subjects all aged 50 years in the POEM study, IMT and IM-GSM were measured together with a DXA scan for determination of fat and lean mass. Also a whole-body MRI scan was performed and the body volume was divided into >1 million voxels in a standardized fashion. IMT and IM-GSM were related to each of these voxels to create a 3D-view of how these measurements were related to size of each part of the body.

RESULTS

IM-GSM was inversely related to almost all traditional measurements of body composition, like fat and lean mass, liver fat, visceral and subcutaneous fat, but this was not seen for IMT. Using Imiomics, IMT was positively related to the intraabdominal fat volume, as well of the leg skeletal muscle in women. In males, IMT was mainly positively related to the leg skeletal muscle volume. IM-GSM was inversely related to the volume of the SAT in the upper part of the body, leg skeletal muscle, the liver and intraabdominal fat in both men and women.

CONCLUSION

The voxel-based Imiomics technique provided a detailed view of how the echogenicity of the carotid artery wall was related to body composition, being inversely related to the volume of the major fat depots, as well as leg skeletal muscle.

Tissue-specific glucose partitioning and fat content in prediabetes and type 2 diabetes: whole-body PET/MRI during hyperinsulinemia

OBJECTIVE

To obtain direct quantifications of glucose turnover, volumes and fat content of several tissues in the development of type 2 diabetes (T2D) using a novel integrated approach for whole-body imaging.

DESIGN AND METHODS

Hyperinsulinemic-euglycemic clamps and simultaneous whole-body integrated [18F]FDG-PET/MRI with automated analyses were performed in control (n = 12), prediabetes (n = 16) and T2D (n = 13) subjects matched for age, sex and BMI.

RESULTS

Whole-body glucose uptake (Rd) was reduced by approximately 25% in T2D vs control subjects, and partitioning to brain was increased from 3.8% of total Rd in controls to 7.1% in T2D. In liver, subcutaneous AT, thigh muscle, total tissue glucose metabolic rates (MRglu) and their % of total Rd were reduced in T2D compared to control subjects. The prediabetes group had intermediate findings. Total MRglu in heart, visceral AT, gluteus and calf muscle was similar across groups. Whole-body insulin sensitivity assessed as glucose infusion rate correlated with liver MRglu but inversely with brain MRglu. Liver fat content correlated with MRglu in brain but inversely with MRglu in other tissues. Calf muscle fat was inversely associated with MRglu only in the same muscle group.

CONCLUSIONS

This integrated imaging approach provides detailed quantification of tissue-specific glucose metabolism. During T2D development, insulin-stimulated glucose disposal is impaired and increasingly shifted away from muscle, liver and fat toward the brain. Altered glucose handling in the brain and liver fat accumulation may aggravate insulin resistance in several organs.

Relationships between plasma levels and six proinflammatory interleukins and body composition using a new magnetic resonance imaging voxel-based technique

•

IL-1RA and IL-6 levels were related to traditional DXA and MRI measurements of adipose tissue.

•

Neither IL-6R nor IL-8 or IL-18 showed strong relationships vs the traditional measurements.

•

Weak relationships between IL-16 levels and trunk SAT volume was found by Imiomics.

•

On the contrary, IL-8 levels were related to a reduction of SAT volume.

Background

Obesity has previously been linked to inflammation. Here we investigated how plasma levels of six interleukins were related to body fat distribution.

Methods

In 321 subjects, all aged 50 years, in the population-based POEM study (mean BMI 26–27 kg/m²), six interleukins were measured together with a DXA scan for determination of fat and lean mass. Also a whole-body magnetic resonance imaging (MRI) scan, in which fat content measurements were acquired in > 1 million voxels was performed. Interleukin levels were related to each of these voxels by the voxel-based technique “Imiomics” to create a 3D-view of how these measurements were related to size of each part of the body.

Results

Levels of IL-1RA and IL-6 were related to traditional DXA and MRI measurements of adipose tissue at all locations. Neither IL-6R, nor IL-8 or IL-18, showed any consistent significant relationships vs the traditional measurements of body composition, while IL-16 showed relationships being of borderline significance. The Imiomics evaluation further strengthen the view that IL-1RA and IL-6 were related to subcutaneous adipose tissue (SAT), as well to ectopic fat distribution. In women, IL-16 levels were weakly related to expansion of SAT in the upper part of the body, while on the contrary, IL-8 levels were related to a reduction of SAT volume.

Conclusion

Of the six evaluated interleukins, plasma IL-1RA and IL-6 levels were related to the amount of adipose tissue in all parts of the body, while a diverse picture was seen for other interleukins, suggesting that different interleukins are related to fat distribution in different ways.

Cardiovascular-related proteins and the abdominal visceral to subcutaneous adipose tissue ratio

BACKGROUND AND AIMS

An increased amount of visceral adipose tissues has been related to atherosclerosis and future cardiovascular events. The present study aims to investigate how the abdominal fat distribution links to plasma levels of cardiovascular-related proteins.

METHOD AND RESULTS

In the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (n = 326, all aged 50 years), abdominal visceral (VAT) and subcutaneous (SAT) adipose tissue volumes were quantified by MRI. Eighty-six cardiovascular-related proteins were measured by the proximity extension assay (PEA). Similar investigations were carried out in the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (n = 400, all aged 75 years). In the discovery dataset (POEM), 10 proteins were related to the VAT/SAT-ratio using false discovery rate <.05. Of those, Cathepsin D (CTSD), Interleukin-1 receptor antagonist protein (IL-1RA) and Growth hormone (GH) (inversely) were related to the VAT/SAT-ratio in the validation in PIVUS following adjustment for sex, BMI, smoking, education level and exercise habits (p < 0.05). In a secondary analysis, a meta-analysis of the two samples suggested that 15 proteins could be linked to the VAT/SAT-ratio following adjustment as above and Bonferroni-correction of the p-value.

CONCLUSION

Three cardiovascular-related proteins, cathepsin D, IL-1RA and growth hormone, were being associated with the distribution of abdominal adipose tissue using a discovery/validation approach. A meta-analysis of the two samples suggested that also a number of other cardiovascular-related proteins could be associated with an unfavorable abdominal fat distribution.

Faster dense deformable image registration by utilizing both CPU and GPU

Purpose: Image registration is an important aspect of medical image analysis and a key component in many analysis concepts. Applications include fusion of multimodal images, multi-atlas segmentation, and whole-body analysis. Deformable image registration is often computationally expensive, and the need for efficient registration methods is highlighted by the emergence of large-scale image databases, e.g., the UK Biobank, providing imaging from 100,000 participants.

Approach: We present a heterogeneous computing approach, utilizing both the CPU and the graphics processing unit (GPU), to accelerate a previously proposed image registration method. The parallelizable task of computing the matching criterion is offloaded to the GPU, where it can be computed efficiently, while the more complex optimization task is performed on the CPU. To lessen the impact of data synchronization between the CPU and GPU, we propose a pipeline model, effectively overlapping computational tasks with data synchronization. The performance is evaluated on a brain labeling task and compared with a CPU implementation of the same method and the popular advanced normalization tools (ANTs) software.

Results: The proposed method presents a speed-up by factors of 4 and 8 against the CPU implementation and the ANTs software, respectively. A significant improvement in labeling quality was also observed, with measured mean Dice overlaps of 0.712 and 0.701 for our method and ANTs, respectively.

Conclusions: We showed that the proposed method compares favorably to the ANTs software yielding both a significant speed-up and an improvement in labeling quality. The registration method together with the proposed parallelization strategy is implemented as an open-source software package, deform.

Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification

Purpose: Convolutional neural network (CNN) methods have been proposed to quantify lesions in medical imaging. Commonly, more than one imaging examination is available for a patient, but the serial information in these images often remains unused. CNN-based methods have the potential to extract valuable information from previously acquired imaging to better quantify lesions on current imaging of the same patient.

Approach: A pretrained CNN can be updated with a patient’s previously acquired imaging: patient-specific fine-tuning (FT). In this work, we studied the improvement in performance of lesion quantification methods on magnetic resonance images after FT compared to a pretrained base CNN. We applied the method to two different approaches: the detection of liver metastases and the segmentation of brain white matter hyperintensities (WMH).

Results: The patient-specific fine-tuned CNN has a better performance than the base CNN. For the liver metastases, the median true positive rate increases from 0.67 to 0.85. For the WMH segmentation, the mean Dice similarity coefficient increases from 0.82 to 0.87.

Conclusions: We showed that patient-specific FT has the potential to improve the lesion quantification performance of general CNNs by exploiting a patient’s previously acquired imaging.

Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants

The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, which also covers the kidneys. In this work, a pipeline for automated segmentation of parenchymal kidney volume in UK Biobank neck-to-knee body MRI is proposed. The underlying neural network reaches a relative error of 3.8%, with Dice score 0.956 in validation on 64 subjects, close to the 2.6% and Dice score 0.962 for repeated segmentation by one human operator. The released MRI of about 40,000 subjects can be processed within one day, yielding volume measurements of left and right kidney. Algorithmic quality ratings enabled the exclusion of outliers and potential failure cases. The resulting measurements can be studied and shared for large-scale investigation of associations and longitudinal changes in parenchymal kidney volume.

Large-scale biometry with interpretable neural network regression on UK Biobank body MRI

In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2 > 0.97$$\end{document}2>0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.

Validation of automated whole-body analysis of metabolic and morphological parameters from an integrated FDG-PET/MRI acquisition

Automated quantification of tissue morphology and tracer uptake in PET/MR images could streamline the analysis compared to traditional manual methods. To validate a single atlas image segmentation approach for automated assessment of tissue volume, fat content (FF) and glucose uptake (GU) from whole-body [18F]FDG-PET/MR images. Twelve subjects underwent whole-body [18F]FDG-PET/MRI during hyperinsulinemic-euglycemic clamp. Automated analysis of tissue volumes, FF and GU were achieved using image registration to a single atlas image with reference segmentations of 18 volume of interests (VOIs). Manual segmentations by an experienced radiologist were used as reference. Quantification accuracy was assessed with Dice scores, group comparisons and correlations. VOI Dice scores ranged from 0.93 to 0.32. Muscles, brain, VAT and liver showed the highest scores. Pancreas, large and small intestines demonstrated lower segmentation accuracy and poor correlations. Estimated tissue volumes differed significantly in 8 cases. Tissue FFs were often slightly but significantly overestimated. Satisfactory agreements were observed in most tissue GUs. Automated tissue identification and characterization using a single atlas segmentation performs well compared to manual segmentation in most tissues and will be valuable in future studies. In certain tissues, alternative quantification methods or improvements to the current approach is needed.

Multiple comparison correction methods for whole-body magnetic resonance imaging

Purpose: Voxel-level hypothesis testing on images suffers from test multiplicity. Numerous correction methods exist, mainly applied and evaluated on neuroimaging and synthetic datasets. However, newly developed approaches like Imiomics, using different data and less common analysis types, also require multiplicity correction for more reliable inference. To handle the multiple comparisons in Imiomics, we aim to evaluate correction methods on whole-body MRI and correlation analyses, and to develop techniques specifically suited for the given analyses.

Approach: We evaluate the most common familywise error rate (FWER) limiting procedures on whole-body correlation analyses via standard (synthetic no-activation) nominal error rate estimation as well as smaller prior-knowledge based stringency analysis. Their performance is compared to our anatomy-based method extensions.

Results: Results show that nonparametric methods behave better for the given analyses. The proposed prior-knowledge based evaluation shows that the devised extensions including anatomical priors can achieve the same power while keeping the FWER closer to the desired rate.

Conclusions: Permutation-based approaches perform adequately and can be used within Imiomics. They can be improved by including information on image structure. We expect such method extensions to become even more relevant with new applications and larger datasets.

Voxel-wise Study of Cohort Associations in Whole-Body MRI: Application in Metabolic Syndrome and Its Components

Background The metabolic syndrome is related to obesity and ectopic fat distribution. Purpose To investigate whether an image analysis approach that uses image registration for whole-body voxel-wise analysis could provide additional information about the relationship between metabolic syndrome and body composition compared with traditional image analysis. Materials and Methods Whole-body quantitative water-fat MRI was performed in a population-based prospective study on obesity, energy, and metabolism between October 2010 and November 2016. Fat mass was measured with dual-energy x-ray absorptiometry (DXA). Whole-body voxel-wise analysis of tissue volume and fat content was applied in more than 2 million voxels from the whole-body examinations by automated interindividual deformable image registration of the water and fat MRI data. Metabolic syndrome was diagnosed by the harmonized National Cholesterol Education Program criteria. Two-tailed t tests were used and P values less than .05 were considered to indicate statistical significance. Results This study evaluated 167 women and 159 men (mean age, 50 years) by using voxel-wise analysis. Metabolic syndrome (13.5%; 44 of 326) was related to traditional measurements of fat distribution, such as total fat mass at DXA, visceral and subcutaneous adipose tissue, and liver and pancreatic fat at MRI. Voxel-wise analysis found metabolic syndrome related to liver, heart, and perirenal fat volume; fat content in subcutaneous fat in the hip region in both sexes; fatty infiltration of leg muscles in men, especially in gluteus maximus; and pericardial and aortic perivascular fat mainly in women. Sex differences in associations with subcutaneous adipose tissue were identified. In women, metabolic syndrome diagnosis was linked to regional differences in associations to adipose tissue volumes in upper versus lower body, and dorsal versus ventral abdominal depots. In men similar gradients were only seen in individual components. Conclusion In addition to showing the relationships between metabolic syndrome and body composition in a detailed and intuitive fashion in the whole body, the voxel-wise analysis provided additional information compared with traditional image analysis. © RSNA, 2020 Online supplemental material is available for this article.

Average volume reference space for large scale registration of whole-body magnetic resonance images

BACKGROUND AND OBJECTIVES

The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is neutral with respect to local tissue volume is valuable in correlation studies. The purpose of this work is to generate a reference space for whole-body imaging that has zero voxel-wise average volume change when mapped to a cohort.

METHODS

This work proposes an approach to register multiple whole-body images to a common template using volume changes to generate a synthetic reference space, starting with an initial reference and refining it by warping it with a deformation that brings the voxel-wise average volume change associated to the mappings of all the images in the cohort to zero.

RESULTS

Experiments on fat/water separated whole-body MR images show how the method effectively generates a reference space neutral with respect to volume changes, without reducing the quality of the registration nor introducing artefacts in the anatomy, while providing better alignment when compared to an implicit reference groupwise approach.

CONCLUSIONS

The proposed method allows to quickly generate a reference space neutral with respect to local volume changes, that retains the registration quality of a sharp template, and that can be used for statistical analysis of voxel-wise correlations in large datasets of whole-body image data.

Relationship between endothelium-dependent vasodilation and fat distribution using the new "imiomics" image analysis technique

BACKGROUND AND AIMS

We investigated how vasoreactivity in the brachial artery and the forearm resistance vessels were related to fat distribution and tissue volume, using both traditional imaging analysis and a new technique, called "Imiomics", whereby vasoreactivity was related to each of the >2M 3D image elements included in the whole-body magnetic resonance imaging (MRI).

METHODS AND RESULTS

In 326 subjects in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (all aged 50 years), endothelium-dependent vasodilation was measured by acetylcholine infusion in the brachial artery (EDV) and flow-mediated vasodilation (FMD). Fat distribution was evaluated by dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI). EDV, but not FMD, was significantly related to total fat mass, liver fat, subcutaneous (SAT) and visceral (VAT) adipose tissue in a negative fashion in women, but not in men. Using Imiomics, an inverse relationship was seen between EDV and a local tissue volume of SAT in both the upper part of the body, as well as the gluteo-femoral part and the medial parts of the legs in women. Also the size of the liver, heart and VAT was inversely related to EDV. In men, less pronounced relationships were seen. FMD was also significantly related to local tissue volume of upper-body SAT and liver fat in women, but less so in men.

CONCLUSION

EDV, and to a lesser degree also FMD, were related to liver fat, SAT and VAT in women, but less so in men. Imiomics both confirmed findings from traditional methods and resulted in new, more detailed results.

Brown adipose tissue estimated with the magnetic resonance imaging fat fraction is associated with glucose metabolism in adolescents

BACKGROUND

Despite therapeutic potential against obesity and diabetes, the associations of brown adipose tissue (BAT) with glucose metabolism in young humans are relatively unexplored.

OBJECTIVES

To investigate possible associations between magnetic resonance imaging (MRI) estimates of BAT and glucose metabolism, whilst considering sex, age, and adiposity, in adolescents with normal and overweight/obese phenotypes.

METHODS

In 143 subjects (10-20 years), MRI estimates of BAT were assessed as cervical-supraclavicular adipose tissue (sBAT) fat fraction (FF) and T2* from water-fat MRI. FF and T2* of neighbouring subcutaneous adipose tissue (SAT) were also assessed. Adiposity was estimated with a standardized body mass index, the waist-to-height ratio, and abdominal visceral and subcutaneous adipose tissue volumes. Glucose metabolism was represented by the 2h plasma glucose concentration, the Matsuda index, the homeostatic model assessment of insulin resistance, and the oral disposition index; obtained from oral glucose tolerance tests.

RESULTS

sBAT FF and T2* correlated positively with adiposity before and after adjustment for sex and age. sBAT FF, but not T2* , correlated with 2h glucose and Matsuda index, also after adjustment for sex, age, and adiposity. The association with 2h glucose persisted after additional adjustment for SAT FF.

CONCLUSIONS

The association between sBAT FF and 2h glucose, observed independently of sex, age, adiposity, and SAT FF, indicates a role for BAT in glucose metabolism, which potentially could influence the risk of developing diabetes. The lacking association with sBAT T2* might be due to FF being a superior biomarker for BAT and/or to methodological limitations in the T2* quantification.

A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities

Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales.

Proof of principle study of a detailed whole-body image analysis technique, "Imiomics", regarding adipose and lean tissue distribution

This "proof-of-principle" study evaluates if the recently presented "Imiomics" technique could visualize how fat and lean tissue mass are associated with local tissue volume and fat content at high/unprecedented resolution. A whole-body quantitative water-fat MRI scan was performed in 159 men and 167 women aged 50 in the population-based POEM study. Total fat and lean mass were measured by DXA. Fat content was measured by the water-fat MRI. Fat mass and distribution measures were associated to the detailed differences in tissue volume and fat concentration throughout the body using Imiomics. Fat mass was positively correlated (r > 0.50, p < 0.05) with tissue volume in all subcutaneous areas of the body, as well as volumes of the liver, intraperitoneal fat, retroperitoneal fat and perirenal fat, but negatively to lung volume. Fat mass correlated positively with volumes of paravertebral muscles, and muscles in the ventral part of the thigh and lower limb. Fat mass was distinctly correlated with the fat content in subcutaneous adipose tissue at the trunk. Lean mass was positively related to the large skeletal muscles and the skeleton. The present study indicates the Imiomics technique to be suitable for studies of fat and lean tissue distribution, and feasible for large scale studies.

A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis

Quantitative multiparametric imaging is a potential key application for Positron Emission Tomography/Magnetic Resonance (PET/MR) hybrid imaging. To enable objective and automatic voxel-based multiparametric analysis in whole-body applications, the purpose of this study was to develop a multimodality whole-body atlas of functional 18F-fluorodeoxyglucose (FDG) PET and anatomical fat-water MR data of adults. Image registration was used to transform PET/MR images of healthy control subjects into male and female reference spaces, producing a fat-water MR, local tissue volume and FDG PET whole-body normal atlas consisting of 12 male (66.6 ± 6.3 years) and 15 female (69.5 ± 3.6 years) subjects. Manual segmentations of tissues and organs in the male and female reference spaces confirmed that the atlas contained adequate physiological and anatomical values. The atlas was applied in two anomaly detection tasks as proof of concept. The first task automatically detected anomalies in two subjects with suspected malignant disease using FDG data. The second task successfully detected abnormal liver fat infiltration in one subject using fat fraction data.

Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous

Cells are neither flat nor smooth, which has serious implications for prevailing plasma membrane models and cellular processes like cell signalling, adhesion and molecular clustering. Using probability distributions from diffusion simulations, we demonstrate that 2D and 3D Euclidean distance measurements substantially underestimate diffusion on non-flat surfaces. Intuitively, the shortest within surface distance (SWSD), the geodesic distance, should reduce this problem. The SWSD is accurate for foldable surfaces but, although it outperforms 2D and 3D Euclidean measurements, it still underestimates movement on deformed surfaces. We demonstrate that the reason behind the underestimation is that topographical features themselves can produce both super- and subdiffusion, i.e. the appearance of anomalous diffusion. Differentiating between topography-induced and genuine anomalous diffusion requires characterising the surface by simulating Brownian motion on high-resolution cell surface images and a comparison with the experimental data.

Region-by-region analysis of PET, MRI, and histology in en bloc-resected oligodendrogliomas reveals intra-tumoral heterogeneity

Purpose

Oligodendrogliomas are heterogeneous tumors in terms of imaging appearance, and a deeper understanding of the histopathological tumor characteristics in correlation to imaging parameters is needed. We used PET-to-MRI-to-histology co-registration with the aim of studying intra-tumoral ¹¹C-methionine (MET) uptake in relation to tumor perfusion and the protein expression of histological cell markers in corresponding areas.

Methods

Consecutive histological sections of four tumors covering the entire en bloc-removed tumor were immunostained with antibodies against IDH1-mutated protein (tumor cells), Ki67 (proliferating cells), and CD34 (blood vessels). Software was developed for anatomical landmarks-based co-registration of subsequent histological images, which were overlaid on corresponding MET PET scans and MRI perfusion maps. Regions of interest (ROIs) on PET were selected throughout the entire tumor volume, covering hot spot areas, areas adjacent to hot spots, and tumor borders with infiltrating zone. Tumor-to-normal tissue (T/N) ratios of MET uptake and mean relative cerebral blood volume (rCBV) were measured in the ROIs and protein expression of histological cell markers was quantified in corresponding regions. Statistical correlations were calculated between MET uptake, rCBV, and quantified protein expression.

Results

A total of 84 ROIs were selected in four oligodendrogliomas. A significant correlation (p < 0.05) between MET uptake and tumor cell density was demonstrated in all tumors separately. In two tumors, MET correlated with the density of proliferating cells and vessel cell density. There were no significant correlations between MET uptake and rCBV, and between rCBV and histological cell markers.

Conclusions

The MET uptake in hot spots, outside hotspots, and in infiltrating tumor edges unanimously reflects tumor cell density. The correlation between MET uptake and vessel density and density of proliferating cells is less stringent in infiltrating tumor edges and is probably more susceptible to artifacts caused by larger blood vessels surrounding the tumor. Although based on a limited number of samples, this study provides histological proof for MET as an indicator of tumor cell density and for the lack of statistically significant correlations between rCBV and histological cell markers in oligodendrogliomas.

Electronic supplementary material

The online version of this article (10.1007/s00259-018-4107-z) contains supplementary material, which is available to authorized users.

Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies

Computed Tomography (CT) allows detailed studies of body composition and its association with metabolic and cardiovascular disease. The purpose of this work was to develop and validate automated and manual image processing techniques for detailed and efficient analysis of body composition from CT data. The study comprised 107 subjects examined in the Swedish CArdioPulmonary BioImage Study (SCAPIS) using a 3-slice CT protocol covering liver, abdomen, and thighs. Algorithms were developed for automated assessment of liver attenuation, visceral (VAT) and subcutaneous (SAT) abdominal adipose tissue, thigh muscles, subcutaneous, subfascial (SFAT) and intermuscular adipose tissue. These were validated using manual reference measurements. SFAT was studied in selected subjects were the fascia lata could be visually identified (approx. 5%). In addition, precision of manual measurements of intra- (IPAT) and retroperitoneal adipose tissue (RPAT) and deep- and superficial SAT was evaluated using repeated measurements. Automated measurements correlated strongly to manual reference measurements. The SFAT depot showed the weakest correlation (r = 0.744). Automated VAT and SAT measurements were slightly, but significantly overestimated (≤4.6%, p ≤ 0.001). Manual segmentation of abdominal sub-depots showed high repeatability (CV ≤ 8.1%, r ≥ 0.930). We conclude that the low dose CT-scanning and automated analysis makes the setup suitable for large-scale studies.

The response of a boreal deep-sea sponge holobiont to acute thermal stress

Effects of elevated seawater temperatures on deep-water benthos has been poorly studied, despite reports of increased seawater temperature (up to 4 °C over 24 hrs) coinciding with mass mortality events of the sponge Geodia barretti at Tisler Reef, Norway. While the mechanisms driving these mortality events are unclear, manipulative laboratory experiments were conducted to quantify the effects of elevated temperature (up to 5 °C, above ambient levels) on the ecophysiology (respiration rate, nutrient uptake, cellular integrity and sponge microbiome) of G. barretti. No visible signs of stress (tissue necrosis or discolouration) were evident across experimental treatments; however, significant interactive effects of time and treatment on respiration, nutrient production and cellular stress were detected. Respiration rates and nitrogen effluxes doubled in responses to elevated temperatures (11 °C & 12 °C) compared to control temperatures (7 °C). Cellular stress, as measured through lysosomal destabilisation, was 2-5 times higher at elevated temperatures than for control temperatures. However, the microbiome of G. barretti remained stable throughout the experiment, irrespective of temperature treatment. Mortality was not evident and respiration rates returned to pre-experimental levels during recovery. These results suggest other environmental processes, either alone or in combination with elevated temperature, contributed to the mortality of G. barretti at Tisler reef.

Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars

Articular osteochondrosis (OC) often develops in typical locations within joints, and the characterization of OC distribution in the pig tarsus is incomplete. Prevalence of OC is high in domestic pigs but is presumed to be low in wild boars. Postmortem and computed tomography (CT) examinations of the talus and distal tibia from 40 domestic pigs and 39 wild boars were evaluated for the locations and frequencies of OC, synovial fossae, and other articular indentations, and frequency distribution maps were made. All domestic pigs but only 5 wild boars (13%) had OC on the talus. In domestic pigs, OC consistently affected the axial aspect of the medial trochlea tali in 11 (28%) joints and the distomedial talus in 26 (65%) joints. In wild boars, all OC lesions consistently affected the distomedial talus. On the articular surface of the distal tibia, all domestic pigs and 34 wild boars (87%) had synovial fossae and 7 domestic pigs (18%) had superficial cartilage fibrillation opposite an OC lesion (kissing lesion). Other articular indentations occurred in the intertrochlear groove of the talus in all domestic pigs and 13 wild boars (33%) and were less common on the trochlea tali. The prevalence of tarsal OC in wild boars is low. In domestic pigs and wild boars, OC is typically localized to the distomedial talus and in domestic pigs also to the medial trochlea tali. Further investigations into the reasons for the low OC prevalence in wild boars may help in developing strategies to reduce OC incidence in domestic pigs.

A new method for reconstructing brain morphology: applying the brain-neurocranial spatial relationship in an extant lungfish to a fossil endocast

Lungfish first appeared in the geological record over 410 million years ago and are the closest living group of fish to the tetrapods. Palaeoneurological investigations into the group show that unlike numerous other fishes-but more similar to those in tetrapods-lungfish appear to have had a close fit between the brain and the cranial cavity that housed it. As such, researchers can use the endocast of fossil taxa (an internal cast of the cranial cavity) both as a source of morphological data but also to aid in developing functional and phylogenetic implications about the group. Using fossil endocast data from a three-dimensional-preserved Late Devonian lungfish from the Gogo Formation, Rhinodipterus, and the brain-neurocranial relationship in the extant Australian lungfish, Neoceratodus, we herein present the first virtually reconstructed brain of a fossil lungfish. Computed tomographic data and a newly developed 'brain-warping' method are used in conjunction with our own distance map software tool to both analyse and present the data. The brain reconstruction is adequate, but we envisage that its accuracy and wider application in other taxonomic groups will grow with increasing availability of tomographic datasets.

Digital Topology and Geometry in Medical Imaging: A Survey

Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmentation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties, skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.

Magnetic resonance imaging cooling-reheating protocol indicates decreased fat fraction via lipid consumption in suspected brown adipose tissue

Objectives

To evaluate whether a water-fat magnetic resonance imaging (MRI) cooling-reheating protocol could be used to detect changes in lipid content and perfusion in the main human brown adipose tissue (BAT) depot after a three-hour long mild cold exposure.

Materials and Methods

Nine volunteers were investigated with chemical-shift-encoded water-fat MRI at baseline, after a three-hour long cold exposure and after subsequent short reheating. Changes in fat fraction (FF) and R₂*, related to ambient temperature, were quantified within cervical-supraclavicular adipose tissue (considered as suspected BAT, denoted sBAT) after semi-automatic segmentation. In addition, FF and R₂* were quantified fully automatically in subcutaneous adipose tissue (not considered as suspected BAT, denoted SAT) for comparison. By assuming different time scales for the regulation of lipid turnover and perfusion in BAT, the changes were determined as resulting from either altered absolute fat content (lipid-related) or altered absolute water content (perfusion-related).

Results

sBAT-FF decreased after cold exposure (mean change in percentage points = -1.94 pp, P = 0.021) whereas no change was observed in SAT-FF (mean = 0.23 pp, P = 0.314). sBAT-R₂* tended to increase (mean = 0.65 s^-1, P = 0.051) and SAT-R₂* increased (mean = 0.40 s^-1, P = 0.038) after cold exposure. sBAT-FF remained decreased after reheating (mean = -1.92 pp, P = 0.008, compared to baseline) whereas SAT-FF decreased (mean = -0.79 pp, P = 0.008, compared to after cold exposure).

Conclusions

The sustained low sBAT-FF after reheating suggests lipid consumption, rather than altered perfusion, as the main cause to the decreased sBAT-FF. The results obtained demonstrate the use of the cooling-reheating protocol for detecting changes in the cervical-supraclavicular fat depot, being the main human brown adipose tissue depot, in terms of lipid content and perfusion.

Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.

Automated classification of immunostaining patterns in breast tissue from the human protein atlas

BACKGROUND

The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

MATERIALS AND METHODS

The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

RESULTS

We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

CONCLUSIONS

Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

Impact of early salmon louse, Lepeophtheirus salmonis, infestation and differences in survival and marine growth of sea-ranched Atlantic salmon, Salmo salar L., smolts 1997-2009

The impact of salmon lice on the survival of migrating Atlantic salmon smolts was studied by comparing the adult returns of sea-ranched smolts treated for sea lice using emamectin benzoate or substance EX with untreated control groups in the River Dale in western Norway. A total of 143 500 smolts were released in 35 release groups in freshwater from 1997 to 2009 and in the fjord system from 2007 to 2009. The adult recaptures declined gradually with release year and reached minimum levels in 2007. This development corresponded with poor marine growth and increased age at maturity of ranched salmon and in three monitored salmon populations and indicated unfavourable conditions in the Norwegian Sea. The recapture rate of treated smolts was significantly higher than the controls in three of the releases performed: the only release in 1997, one of three in 2002 and the only group released in sea water in 2007. The effect of treating the smolts against salmon lice was smaller than the variability in return rates between release groups, and much smaller that variability between release years, but its overall contribution was still significant (P < 0.05) and equivalent to an odds ratio of the probability of being recaptured of 1.17 in favour of the treated smolts. Control fish also tended to be smaller as grilse (P = 0.057), possibly due to a sublethal effect of salmon lice.

Can anti-erosion dentifrices also provide effective plaque control?

OBJECTIVE

While gingivitis and caries continue to be prevalent issues, there is growing concern about dental erosion induced by dietary acids. An oral hygiene product that protects against all these conditions would be beneficial. This study investigated the potential of two anti-erosion dentifrices to inhibit plaque.

METHODS

This was a randomized, three-period, two-treatment, double-blind, crossover study evaluating a stannous chloride/sodium fluoride dentifrice (SnCl(2)/NaF, blend-a-med(®) Pro Expert) and a popular anti-erosion dentifrice (NaF, Sensodyne(®) ProNamel(™)). During Period 3, subjects were randomized to repeat one treatment to evaluate any product carryover effects. Each treatment period was 17 days. Test dentifrices were used with a standard manual toothbrush. Digital plaque image analysis (DPIA) was employed at the end of each period to evaluate plaque levels (i) overnight (am prebrush); (ii) post-brushing with the test product (am post-brush); and (iii) mid-afternoon (pm). Analysis was conducted via an objective computer algorithm, which calculated total area of visible plaque.

RESULTS

Twenty-seven subjects completed the study. At all time points, subjects had statistically significantly (P ≤ 0.0001) lower plaque levels after using the SnCl(2)/NaF dentifrice than the NaF dentifrice. The antiplaque benefit for the SnCl(2)/NaF dentifrice versus the NaF dentifrice was: am prebrush = 26.0%; am post-brushing = 27.9%; pm = 25.7%.

CONCLUSIONS

The SnCl(2)/NaF dentifrice provided significantly greater daytime and overnight plaque inhibition than the NaF toothpaste. When recommending dentifrice to patients susceptible to dental erosion, clinicians can consider one that also inhibits plaque.