The metabolic profiling of tissue biopsies using high-resolution–magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy may be influenced by experimental factors such as the sampling method. Therefore, we compared the effects of two different sampling methods on the metabolome of brain tissue obtained from the brainstem and thalamus of healthy goats by 1H HR-MAS NMR spectroscopy—in vivo-harvested biopsy by a minimally invasive stereotactic approach compared with postmortem-harvested sample by dissection with a scalpel. Lactate and creatine were elevated, and choline-containing compounds were altered in the postmortem compared to the in vivo-harvested samples, demonstrating rapid changes most likely due to sample ischemia. In addition, in the brainstem samples acetate and inositols, and in the thalamus samples ƴ-aminobutyric acid, were relatively increased postmortem, demonstrating regional differences in tissue degradation. In conclusion, in vivo-harvested brain biopsies show different metabolic alterations compared to postmortem-harvested samples, reflecting less tissue degradation. Sampling method and brain region should be taken into account in the analysis of metabolic profiles. To be as close as possible to the actual situation in the living individual, it is desirable to use brain samples obtained by stereotactic biopsy whenever possible.

Purpose: Knee Osteoarthritis (OA) progression and monitoring are possible by evaluating changes in subchondral bone tissues from magnetic resonance images but highly dependent on the accurate segmentation and quantitative measurement techniques. Existing methods to segment bone from MR images are either insensitive or rely on supervised computational techniques that require training models. Thus, the aim of this work is to develop an automated and unsupervised bone segmentation technique that can be used in large-scale longitudinal/multicentre studies. Methods: In this work, an automatic and unsupervised bone segmentation approach is developed and tested on 8 MR Datasets (DESS MR Seq., No. of Slices ¼ 160, TR/TE ¼ 16.3/4.7ms, ST¼ 0.7 mm, Res ¼ 0.365 Â 0.456 mm, FOV ¼ 140 Â 140 mm, FA ¼ 25 , MS ¼ 384 Â 384, BW ¼ 185 Hz/px obtained from OA Initiative) consisting of 1280 Slices. Segmentation of bones including femur and tibia is achieved in the two-fold process i.e. 1) Pre-processing and Pre-segmentation and 2) boundary correction leading to accurate segmentation. The pre-processing steps involve enhancement of knee MR images using local Gray level S-curve transformation technique that improves gradient magnitude of the image and leading to provide sharp edges and high contrast between adjacent tissues. Furthermore, an unsupervised method called distance regularised level set evolution (DRLSE) is used for pre-segmentation that evolves curve in the region of interest. For DRLSE, level set function (LSF) evolution occurs according to the initialization of two level set functions with the very small dimension corresponding to femur and tibia bone by obtaining the location using multi-resolution 3D scale-space technique. In the first process, all the slices in a single dataset are pre-segmented that results in some leakages associated at boundaries of bone. In order to resolve this issue, a boundary displacement technique is applied on two consecutive pre-segmented slices and distance between individual points of the current (reference slice with perfect boundaries) and successive (adjacent slice with leakages) boundary contours are measured to classify leakages with a threshold of 5 pixels. Once classified as leakage point, this was replaced with the corresponding points of the reference slice and the rest boundary is kept unaltered that is leading to provide accurate boundary correction. If there are any edge gaps between the contours, a linear interpolation method is applied to fill those gaps. In the same way, all the slices in each dataset are segmented and the results obtained from the segmentation are compared with manually segmented bones by experts. For the comparison, sensitivity, specificity, accuracy and dice similarity coefficient (DSC) are measured with their coefficient of variations as the number of datasets are rather small.

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Purpose: MRI is the first-line modality to diagnose meniscal pathology. Possible alternative to MRI is an ultrasound scanning which is quick to perform and less costly. The aim of this study was to assess the accuracy of high resolution ultrasonography in the diagnosis of meniscal tears with arthroscopic examination as standard reference. Methods: Fifty pair of menisci were evaluated in 50 patients (23 females, 27 males, mean age ¼ 32.5 years, range ¼ 13-80 years). Knee examinations were performed with high resolution ultrasonography machine (Hi Vision Preirus, Hitachi, Japan) with 8-14 MHz linear transducer. Inclusion criteria for the study were 1) knees that needed to be treated or examined arthroscopically due to intra articular pathology, and 2) unilateral involvement. Exclusion criteria were a history of rheumatoid arthritis or other inflammatory disease, periarticular fracture, Paget's disease, joint infection, neuropathic arthropathy, acromegaly, gout, and pseudogout. Examiner was not informed preoperative diagnosis and laterality. After ultrasonographic examination, all patients underwent arthroscopic procedures within 1-3 days. After the final diagnosis about meniscal status was derived from surgical reports, ultrasonographic diagnosis was compared in terms of presence of tear as well as type of tear when tears were present. Results: The overall sensitivity, specificity, positive predictive value, and negative predictive value of ultrasonographic examination in the assessment of meniscal tears amounted to 90%, 70%, 92% and 64%, respectively. The statistical parameters were not statistically different in medial and lateral menisci. Sensitivity, specificity, positive predictive value and negative predictive value of ultrasonographic examination in the assessment of medial meniscal tears vs. lateral meniscal tears were 96% vs. 65%, 74% vs. 88%, 81% vs. 81%, and 94% vs. 76%, respectively. As for diagnostic ability in diagnosing the type of meniscal tear, the sensitivity and positive predictive value of horizontal tear, vertical tear, radial tear, flap tear, bucket handle tear, complex tear, discoid were 71% vs. 36%, 47% vs. 58%, 0% vs. 0%, 40% vs. 67%, 55% vs. 86%, 88% vs. 54%, 100% vs. 100%. Age, sex, body mass index (BMI), weight, did not have a statistically significant impact on the usefulness of ultrasonography. Conclusions: High resolution ultrasonography had high accuracy in the detecting the presence of tears both in medial and lateral menisci. But type of meniscal tear was difficult to diagnose.