In vivo intervertebral disc mechanical deformation following a treadmill walking "stress test" is inversely related to T1rho relaxation time.

OBJECTIVE: To assess the in vivo relationship between the mechanical response of intervertebral discs (IVDs) to dynamic activity and IVD biochemical composition assessed via T1rho relaxation imaging. DESIGN: Eighteen asymptomatic participants with no history of low back pain (LBP), injury, or surgery underwent magnetic resonance (MR) imaging of their lumbar spine prior to and immediately following a treadmill walking "stress test." Anatomic (SPACE, FLASH) MR images were obtained pre- and post-exercise and utilized to measure IVD mechanical deformation. Quantitative (T1rho) imaging was performed pre-exercise to reflect IVD composition. Pre-exercise anatomic images were also utilized to assess IVD degenerative status based on the modified Pfirrmann scale. To quantify mechanical response, 3D surface models of the L1-L2-L5-S1 IVDs were created from manual segmentations of pre- and post-exercise anatomic images and utilized to assess changes in IVD height. IVD strain (%) was defined as change in IVD height normalized to pre-activity height. Linear mixed models were used to assess the relationships between IVD mechanical deformation (strain), composition (T1rho relaxation time), and degenerative status (Pfirrmann grade). RESULTS: Increased compressive IVD strain was associated with lower T1rho relaxation times in the nucleus pulposus (NP) of the disc (ßT1rho=5.07,CI:[1.52,7.77],Rmarg2=0.52,p=0.005). Thus, an inverse relationship between IVD strain and NP T1rho relaxation time was observed. CONCLUSION: The in vivo mechanical response of the IVD to the "stress test" was sensitive to differences in NP composition. The results of this study suggest that quantification of in vivo IVD mechanical function and composition may provide insight into IVD health.

Osteoarthritis year in review 2018: mechanics.

OBJECTIVE: To review recent biomechanics literature focused on the interactions between biomechanics and articular cartilage health, particularly focused on macro-scale and human studies. DESIGN: A literature search was conducted in PubMed using the search terms (biomechanics AND osteoarthritis) OR (biomechanics AND cartilage) OR (mechanics AND osteoarthritis) OR (mechanics AND cartilage) for publications from April 2017 to April 2018. RESULTS: Abstracts from the 559 articles generated from the literature search were reviewed. Due to the wide range of topics, 62 full texts with a focus on in vivo biomechanical studies were included for further discussion. Several overarching themes in the recent literature were identified and are summarized, including 1) new methods to detect early osteoarthritis (OA) development, 2) studies describing healthy and OA cartilage and biomechanics, 3) ACL injury and OA development, 4) meniscus injury and OA development, and 5) OA prevention, treatment, and management. CONCLUSIONS: Mechanical loading is a critical factor in the maintenance of joint health. Abnormal mechanical loading can lead to the onset and progression of OA. Thus, recent studies have utilized various biomechanical models to better describe the etiology of OA development and the subsequent effects of OA on the mechanics of joint tissues and whole body biomechanics.

Local Glioma Cells Are Associated with Vascular Dysregulation.

BACKGROUND AND PURPOSE: Malignant glioma is a highly infiltrative malignancy that causes variable disruptions to the structure and function of the cerebrovasculature. While many of these structural disruptions have known correlative histopathologic alterations, the mechanisms underlying vascular dysfunction identified by resting-state blood oxygen level-dependent imaging are not yet known. The purpose of this study was to characterize the alterations that correlate with a blood oxygen level-dependent biomarker of vascular dysregulation. MATERIALS AND METHODS: Thirty-two stereotactically localized biopsies were obtained from contrast-enhancing (n = 16) and nonenhancing (n = 16) regions during open surgical resection of malignant glioma in 17 patients. Preoperative resting-state blood oxygen level-dependent fMRI was used to evaluate the relationships between radiographic and histopathologic characteristics. Signal intensity for a blood oxygen level-dependent biomarker was compared with scores of tumor infiltration and microvascular proliferation as well as total cell and neuronal density. RESULTS: Biopsies corresponded to a range of blood oxygen level-dependent signals, ranging from relatively normal (z = -4.79) to markedly abnormal (z = 8.84). Total cell density was directly related to blood oxygen level-dependent signal abnormality (P = .013, R2 = 0.19), while the neuronal labeling index was inversely related to blood oxygen level-dependent signal abnormality (P = .016, R2 = 0.21). The blood oxygen level-dependent signal abnormality was also related to tumor infiltration (P = .014) and microvascular proliferation (P = .045). CONCLUSIONS: The relationship between local, neoplastic characteristics and a blood oxygen level-dependent biomarker of vascular function suggests that local effects of glioma cell infiltration contribute to vascular dysregulation.

A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies.

BACKGROUND AND PURPOSE: The complex MR imaging appearance of glioblastoma is a function of underlying histopathologic heterogeneity. A better understanding of these correlations, particularly the influence of infiltrating glioma cells and vasogenic edema on T2 and diffusivity signal in nonenhancing areas, has important implications in the management of these patients. With localized biopsies, the objective of this study was to generate a model capable of predicting cellularity at each voxel within an entire tumor volume as a function of signal intensity, thus providing a means of quantifying tumor infiltration into surrounding brain tissue. MATERIALS AND METHODS: Ninety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. Signal intensity was plotted against cell density for each MR image. RESULTS: T2-FLAIR (r = -0.61) and ADC (r = -0.63) sequences were inversely correlated with cell density. T1-postcontrast (r = 0.69) subtraction was directly correlated with cell density. Combining these relationships yielded a multiparametric model with improved correlation (r = 0.74), suggesting that each sequence offers different and complementary information. CONCLUSIONS: Using localized biopsies, we have generated a model that illustrates a quantitative and significant relationship between MR signal and cell density. Projecting this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity in both the contrast-enhancing tumor core and nonenhancing margins of glioblastoma and may be used to guide extended surgical resection, localized biopsies, and radiation field mapping.