Adipose segmentation in small animals at 7T: a preliminary study

<p>Abstract</p> <p>Background</p> <p>Small animal MRI at 7 Tesla (T) provides a useful tool for adiposity research. For adiposity researchers, separation of fat from surrounding tissues and its subsequent quantitative or semi- quantitative analysis is a key task. This is a relatively new field and a priori it cannot be known which specific biological questions related to fat deposition will be relevant in a specific study. Thus it is impossible to predict what accuracy and what spatial resolution will be required in all cases and even difficult what accuracy and resolution will be useful in most cases. However the pragmatic time constraints and the practical resolution ranges are known for small animal imaging at 7T. Thus we have used known practical constraints to develop a method for fat volume analysis based on an optimized image acquisition and image post processing pair.</p> <p>Methods</p> <p>We designed a fat segmentation method based on optimizing a variety of factors relevant to small animal imaging at 7T. In contrast to most previously described MRI methods based on signal intensity of T1 weighted image alone, we chose to use parametric images based on Multi-spin multi-echo (MSME) Bruker pulse sequence which has proven to be particularly robust in our laboratory over the last several years. The sequence was optimized on a T1 basis to emphasize the signal. T2 relaxation times can be calculated from the multi echo data and we have done so on a pixel by pixel basis for the initial step in the post processing methodology. The post processing consists of parallel paths. On one hand, the weighted image is precisely divided into different regions with optimized smoothing and segmentation methods; and on the other hand, a confidence image is deduced from the parametric image according to the distribution of relaxation time relationship of typical adipose. With the assistance of the confidence image, a useful software feature was implemented to which enhances the data and in the end results in a more reliable and flexible method for adipose evaluation.</p> <p>Results</p> <p>In this paper, we describe how we arrived at our recommended procedures and key aspects of the post-processing steps. The feasibility of the proposed method is tested on both simulated and real data in this preliminary research. A research tool was created to help researchers segment out fat even when the anatomical information is of low quality making it difficult to distinguish between fat and non-fat. In addition, tool is designed to allow the operator to make adjustments to many of the key steps for comparison purposes and to quantitatively assess the difference these changes make. Ultimately our flexible software lets the researcher define key aspects of the fat segmentation and quantification.</p> <p>Conclusions</p> <p>Combining the full T2 parametric information with the optimized first echo image information, the research tool enhances the reliability of the results while providing more flexible operations than previous methods. The innovation in the method is to pair an optimized and very specific image acquisition technique to a flexible but tuned image post processing method. The separation of the fat is aided by the confidence distribution of regions produced on a scale relevant to and dictated by practical aspects of MRI at 7T.</p

Atherosclerotic carotid plaque composition: a 3T and 7T MRI-histology correlation study

Background and Purpose Carotid artery atherosclerotic plaque composition may influence plaque stability and risk of thromboembolic events, and non-invasive plaque imaging may therefore permit risk stratification for clinical management. Plaque composition was compared using non-invasive in-vivo (3T) and ex-vivo (7T) MRI and histopathological examination. Methods Thirty three endarterectomy cross sections, from 13 patients, were studied. The datasets consisted of in-vivo 3T MRI, ex-vivo 7T MRI and histopathology. Semi-automated segmentation methods were used to measure areas of different plaque components. Bland- Altman plots and mean difference with 95% confidence interval were carried out. Results There was general quantitative agreement between areas derived from semi-automated segmentation of MRI data and histology measurements. The mean differences and 95% confidence bounds in the relative to total plaque area between 3T versus Histology were: fibrous tissue 4.99 % (-4.56 to 14.56), lipid-rich/necrotic core (LR/NC) with haemorrhage - 1.81% (-14.11 to 10.48), LR/NC without haemorrhage -2.43% (-13.04 to 8.17), and calcification -3.18% (-11.55 to 5.18). The mean differences and 95% confidence bounds in the relative to total plaque area between 7T and histology were: fibrous tissue 3.17 % (-3.17 to 9.52), LR/NC with haemorrhage -0.55% (-9.06 to 7.95), LR/NC without haemorrhage - 12.62% (-19.8 to -5.45), and calcification -2.43% (-9.97 to 4.73). Conclusions This study provides evidence that semi-automated segmentation of 3T/7T MRI techniques can help to determine atherosclerotic plaque composition. In particular, the high resolution of ex-vivo 7T data was able to highlight greater detail in the atherosclerotic plaque composition. High field MRI may therefore have advantages for in vivo carotid plaque MR imaging

An Automatic Technique for MRI Based Murine Abdominal Fat Measurement

Because of the well-known relationship between obesity and high incidence of diseases, fat related research using mice models is being widely investigated in preclinical experiments. In the present study, we developed a technique to automatically measure mice abdominal adipose volume and determine the depot locations using Magnetic Resonance Imaging (MRI). Our technique includes an innovative method to detect fat tissues from MR images which not only utilizes the T1 weighted intensity information, but also takes advantage of the transverse relaxation time(T2) calculated from the multiple echo data. The technique contains both a fat optimized MRI imaging acquisition protocol that works well at 7T and a newly designed post processing methodology that can automatically accomplish the fat extraction and depot recognition without user intervention in the segmentation procedure. The post processing methodology has been integrated into easy-to-use software that we have made available via free download. The method was validated by comparing automated results with two independent manual analyses in 26 mice exhibiting different fat ratios from the obesity research project. The comparison confirms a close agreement between the results in total adipose tissue size and voxel-by-voxel overlaps

Ultra-high-field (7 Tesla) MRI study of the articular cartilage in normal subjects: a work in progress

Abstract Objective: The purposes of this study are to optimize cartilage-dedicated sequences for in vivo articular cartilage imaging at 7 Tesla MRI and to investigate the diagnostic potential of UHF in detecting early changes of the articular cartilage related to the physiological aging, focusing on the feasibility and the results of in vivo T2 and T2* mapping. Materials and methods: Until now 12 healthy subjects of different age (50 yrs , n=5, 58.7 ± 5.14) were recruited for the study. Inclusion criteria were absence of clinical symptoms and no history of previous knee surgery or significant knee trauma. All the subjects underwent MR knee examination at 7 T whole-body system. Results: The mean T2 and T2* maps values were obtained for all 12 subjects. The average T2 maps values were 26,5 ms ± 1,5 and 29,5 ms ± 1,7 respectively for under 50 years and over 50 years aged subjects. The mean T2* maps values were 21,1 ms ± 2,2 and 25,6 ms ± 1,6 for under 50 years and over 50 years aged volunteers respectively. Our preliminary data revealed a significant correlation between both the increase in the T2 and T2* maps values and subjects age (p=0,0192* and p=0,082* respectively). Conclusion: The number of subjects imaged until now it is still limited but according to our project, totally 30 subjects will undergo MR examination. We cannot have precise data at the moment. However we obtained the mean T2 and T2* values for under 50 years and over 50 years aged subjects. Our preliminary data revealed the T2 and T2* values increase with the subjects age, in according to literature data. An expansion of the sample in study will be necessary to describe the extent of this correlation

MRI adipose tissue segmentation and quantification in R (RAdipoSeg)

Background: Excess adipose tissue is associated with increased cardiovascular and metabolic risk, but the volume of visceral and subcutaneous adipose tissue poses different metabolic risks. MRI with fat suppression can be used to accurately quantify adipose depots. We have developed a new semi-automatic method, RAdipoSeg, for MRI adipose tissue segmentation and quantification in the free and open source statistical software R. Methods: MRI images were obtained from wild-type mice on high- or low-fat diet, and from 20 human subjects without clinical signs of metabolic dysfunction. For each mouse and human subject, respectively, 10 images were segmented with RAdipoSeg and with the commercially available software SliceOmatic. Jaccard difference, relative volume difference and Spearman’s rank correlation coefficients were calculated for each group. Agreement between the two methods were analysed with Bland–Altman plots. Results: RAdipoSeg performed similarly to the commercial software. The mean Jaccard differences were 10–29% and the relative volume differences were below ( ±) 20%. Spearman’s rank correlation coefficient gave p-values below 0.05 for both mouse and human images. The Bland–Altman plots indicated some systematic and proporitional bias, which can be countered by the flexible nature of the method. Conclusion: RAdipoSeg is a reliable and low cost method for fat segmentation in studies of mice and humans.publishedVersio