Composite metric R2 − R1ρ (1/T2 − 1/T1ρ) as a potential MR imaging biomarker associated with changes in pain after ACL reconstruction: A six‐month follow‐up

Russell, Colin M.; Pedoia, Valentina; Majumdar, Sharmila

doi:10.1002/jor.23400

Cited by 18 publications

(23 citation statements)

References 43 publications

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“…This technique allows for the investigation of local cartilage composition differences between two groups through voxel-based statistical parametric mapping (SPM). VBR and SPM were recently used in a multicenter study that aimed to explore how cartilage lesions at the time of ACL injury influence the longitudinal progression of cartilage degeneration (22), while a related study explored the usage of the composite R 2 -R 1ρ metric as a possible associative biomarker for patientreported outcomes (23).…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis-T1ρ voxel based relaxometry of the articular cartilage: a comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

Pedoia¹,

Russell²,

Randolph³

et al. 2016

Quant. Imaging Med. Surg.

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Background: Quantitative MR, including T 1ρ mapping, has been extensively used to probe early biochemical changes in knee articular cartilage of subjects with osteoarthritis (OA) and others at risk for cartilage degeneration, such as those with anterior cruciate ligament (ACL) injury and reconstruction.However, limited studies have been performed aimed to assess the spatial location and patterns of T 1ρ . In this study we used a novel voxel-based relaxometry (VBR) technique coupled with principal component analysis (PCA) to extract relevant features so as to describe regional patterns and to investigate their similarities and differences in T 1ρ maps in subjects with OA and subjects six months after ACL reconstruction (ACLR).Methods: T 1ρ quantitative MRI images were collected for 180 subjects from two separate cohorts. The OA cohort included 93 osteoarthritic patients and 25 age-matched controls. The ACLR-6M cohort included 52 patients with unilateral ACL tears who were imaged 6 months after ACL reconstruction, and 10 agematched controls. Non-rigid registration on a single template and local Z-score conversion were adopted for T 1ρ spatial and intensity normalization of all the images in the dataset. PCA was used as a data dimensionality reduction to obtain a description of all subjects in a 10-dimensional feature space. Logistic linear regression was used to identify distinctive features of OA and ACL subjects Conclusions: This study is the first example of T 1ρ local/regional pattern analysis and data-driven feature extraction in knees with cartilage degeneration. Our results revealed similarities and differences between OA and ACL relaxation patterns that could be potentially useful to better understand the pathogenesis of posttraumatic cartilage degeneration and the identification of imaging biomarkers for the early stratification of subjects at risk for developing post-traumatic OA.

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Section: Introductionmentioning

confidence: 99%

Principal component analysis-T1ρ voxel based relaxometry of the articular cartilage: a comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

Pedoia¹,

Russell²,

Randolph³

et al. 2016

Quant. Imaging Med. Surg.

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“…On the other hand, the T 1 ρ dispersion contrast produced a much greater difference in T 1 ρ between two SLFs, resulting in detection of more subtle differences in the extent of fibrosis, even within the border zone region [20]. For this reason, mFI is defined as the difference between T 1 ρ (0) and T 1 ρ (ω 1 ), rather than a composite metric R 2 − R 1 ρ (R 1 ρ = 1/T 1 ρ) [16] or the ratio of T 1 ρ (ω 1 ) and T 1 ρ (0) [34]. …”

Section: Discussionmentioning

confidence: 99%

“…In addition, loss of proteoglycan and glycosaminoglycan (GAG) in cartilage appeared to be associated with the elevation of T 1 ρ. Nevertheless, based on chemical exchange theory [16,18], an increased concentration of water protons bound to macromolecules leads to increases in both ΔR 1 ρ and mFI The magnitude of the increase is modulated by the chemical shifts and exchange rates of macromolecules. After myocardial infarction, production of structural proteins such as collagens is elevated to facilitate in farct healing.…”

Section: Discussionmentioning

confidence: 99%

“…at the border zone between infarcted and non-infarcted myocardium after MI, appeared relatively low in other patients assessed previously by these investigators [15]. Several studies have adopted a T 1 ρ dispersion contrast, rather than a T 1 ρ contrast, to improve the sensitivity for assessing early cartilage degeneration [16], to investigate aging difference in skeletal muscle [17], and to explore metabolite with chemical exchange sensitive MRI [18]The fundamental mechanism is likely to be the chemical exchange contribution, between free water protons and protons bound with macromolecules, on a time scale of the spin-locking field [19]. In this study, we hypothesized that a T 1 ρ dispersion contrast would provide another imaging approach with improved sensitivity for the assessment of diffuse myocardial fibrosis [20].…”

Section: Introductionmentioning

confidence: 99%

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A non-contrast CMR index for assessing myocardial fibrosis

Yin

Abendschein

Muccigrosso

et al. 2017

Magnetic Resonance Imaging

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Purpose: Safe, sensitive, and non-invasive imaging methods to assess the presence, extent, and turnover of myocardial fibrosis are needed for early stratification of risk in patients who might develop heart failure after myocardial infarction. We describe a non-contrast cardiac magnetic resonance (CMR) approach for sensitive detection of myocardial fibrosis using a canine model of myocardial infarction and reperfusion. Methods: Seven dogs had coronary thrombotic occlusion of the left anterior descending coronary arteries followed by fibrinolytic reperfusion. CMR studies were performed at 7 days after reperfusion. A CMR spin-locking T1ρ mapping sequence was used to acquire T1ρ dispersion data with spin-lock frequencies of 0 and 511 Hz. A fibrosis index map was derived on a pixel-by-pixel basis. CMR native T1 mapping, first-pass myocardial perfusion imaging, and post-contrast late gadolinium enhancement imaging were also performed for assessing myocardial ischemia and fibrosis. Hearts were dissected after CMR for histopathological staining and two myocardial tissue segments from the septal regions of adjacent left ventricular slices were qualitatively assessed to grade the extent of myocardial fibrosis. Results: Histopathology of 14 myocardial tissue segments from septal regions was graded as grade 1 (fibrosis area, <20% of a low power field, n = 9), grade 2 (fibrosis area, 20–50% of field, n = 4), or grade 3 (fibrosis area, >50% of field, n = 1). A dramatic difference in fibrosis index (183%, P < 0.001) was observed by CMR from grade 1 to 2, whereas differences were much smaller for T1ρ (9%, P = 0.14), native T1 (5.5%, P = 0.12), and perfusion (−21%, P = 0.05). Conclusion: A non-contrast CMR index based on T1ρ dispersion contrast was shown in preliminary studies to detect and correlate with the extent of myocardial fibrosis identified histopathologically. A non-contrast approach may have important implications for managing cardiac patients with heart failure, particularly in the presence of impaired renal function.

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“…An atlas‐based technique aligned all images onto a single template and was used for fully automatic articular cartilage segmentation . VBR has been used in a multicenter study with the aim to explore how cartilage lesions at the time of ACL injury influence the longitudinal progression of cartilage degeneration, to study differences between the ACL injured and contralateral knee in the composite R 2 – R 1ρ metric and to explored this metric as a possible associative biomarker for patient‐reported outcomes . VBR coupled with principal component analysis (PCA) has been used to compare biochemical patterns in ACL and OA subjects .…”

Section: Quantitative Mri To Extract Biochemical Compositional Featuresmentioning

confidence: 99%

Translation of morphological and functional musculoskeletal imaging

Pedoia

Majumdar

2018

Journal Orthopaedic Research

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In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring, and assessing the extent of whole joint degeneration, the past decade has been marked by a greatly increased role of noninvasive imaging. This coupled with recent advances in image processing and deep learning opens new possibilities for promising quantitative techniques. The clinical translation of quantitative imaging was previously hampered by tedious non‐scalable and subjective image analysis. Osteoarthritis (OA) diagnosis using X‐rays can be automated by the use of deep learning models and pilot studies showed feasibility of using similar techniques to reliably segment multiple musculoskeletal tissues and detect and stage the severity of morphological abnormalities in magnetic resonance imaging (MRI). Automation and more advanced feature extraction techniques have applications on larger more heterogeneous samples. Analyses based on voxel based relaxometry have shown local patterns in relaxation time elevations and local correlations with outcome variables. Bone cartilage interactions are also enhanced by the analysis of three‐dimensional bone morphology and the potential for the assessment of metabolic activity with simultaneous Positron Emission Tomography (PET)/MR systems. Novel techniques in image processing and deep learning are augmenting imaging to be a source of quantitative and reliable data and new multidimensional analytics allow us to exploit the interactions of data from various sources. In this review, we aim to summarize recent advances in quantitative imaging, the application of image processing and deep learning techniques to study knee and hip OA. ©2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res XX:XX–XX, 2018.

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Composite metric R₂ − R_1ρ (1/T₂ − 1/T_1ρ) as a potential MR imaging biomarker associated with changes in pain after ACL reconstruction: A six‐month follow‐up

Cited by 18 publications

References 43 publications

Principal component analysis-T1ρ voxel based relaxometry of the articular cartilage: a comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

Principal component analysis-T1ρ voxel based relaxometry of the articular cartilage: a comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

A non-contrast CMR index for assessing myocardial fibrosis

Translation of morphological and functional musculoskeletal imaging

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