Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Positano, Vincenzo; Brotini, L.; Aquaro, Giovanni Donato; Pingitore, Alessandro; Lombardi, Massimo; Landini, Luigi; Santarelli, Maria Filomena

doi:10.1109/isbi.2008.4541292

Cited by 2 publications

(2 citation statements)

References 9 publications

(8 reference statements)

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“…Disc herniation was evaluated with high precision of prediction (specificity 99%, sensitivity 93%) [33,34]. These studies were based on active shape modeling (2D), classifiers (2D) and histograms (2D/3D), which were also used as research tools for automated diagnosis of other diseases such as liver metastases [35] or myocardial fibrosis by shape recognition and histogram analysis [36]. …”

Section: Introductionmentioning

confidence: 99%

MRI signal distribution within the intervertebral disc as a biomarker of adolescent idiopathic scoliosis and spondylolisthesis

Gervais

Périé

Parent

et al. 2012

BMC Musculoskelet Disord

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BackgroundEarly stages of scoliosis and spondylolisthesis entail changes in the intervertebral disc (IVD) structure and biochemistry. The current clinical use of MR T2-weighted images is limited to visual inspection. Our hypothesis is that the distribution of the MRI signal intensity within the IVD in T2-weighted images depends on the spinal pathology and on its severity. Therefore, this study aims to develop the AMRSID (analysis of MR signal intensity distribution) method to analyze the 3D distribution of the MR signal intensity within the IVD and to evaluate their sensitivity to scoliosis and spondylolisthesis and their severities.MethodsThis study was realized on 79 adolescents who underwent a MRI acquisition (sagittal T2-weighted images) before their orthopedic or surgical treatment. Five groups were considered: low severity scoliosis (Cobb angle ≤50°), high severity scoliosis (Cobb angles >50°), low severity spondylolisthesis (Meyerding grades I and II), high severity spondylolisthesis (Meyerding grades III, IV and V) and control. The distribution of the MRI signal intensity within the IVD was analyzed using the descriptive statistics of histograms normalized by either cerebrospinal fluid or bone signal intensity, weighted centers and volume ratios. Differences between pathology and severity groups were assessed using one- and two-way ANOVAs.ResultsThere were significant (p < 0.05) variations of indices between scoliosis, spondylolithesis and control groups and between low and high severity groups. The cerebrospinal fluid normalization was able to detect differences between healthy and pathologic IVDs whereas the bone normalization, which reflects both bone and IVD health, detected more differences between the severities of these pathologies.ConclusionsThis study proves for the first time that changes in the intervertebral disc, non visible to the naked eye on sagittal T2-weighted MR images of the spine, can be detected from specific indices describing the distribution of the MR signal intensity. Moreover, these indices are able to discriminate between scoliosis and spondylolisthesis and their severities, and provide essential information on the composition and structure of the discs whatever the pathology considered. The AMRSID method may have the potential to complement the current diagnostic tools available in clinics to improve the diagnostic with earlier biomarkers, the prognosis of evolution and the treatment options of scoliosis and spondylolisthesis.

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Section: Introductionmentioning

confidence: 99%

MRI signal distribution within the intervertebral disc as a biomarker of adolescent idiopathic scoliosis and spondylolisthesis

Gervais

Périé

Parent

et al. 2012

BMC Musculoskelet Disord

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“…The 'gold' standard remains manual delineation by an expert and/or manual thresholding of only the myocardial areas of the MR images [34]. Typically, segmentation is accomplished by manually tracing the LV myocardial contours and applying a threshold of 2 or 3 standard deviations above the average intensity contained within the contours to highlight the pathological tissues [34,35].…”

Section: Detection Of Pathological Tissuesmentioning

confidence: 99%

Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology fromin vivoMRI*

Ringenberg

Deo

Devabhaktuni

et al. 2012

Meas. Sci. Technol.

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This paper presents an automated method to segment left ventricle (LV) tissues from functional and delayed-enhancement (DE) cardiac magnetic resonance imaging (MRI) scans using a sequential multi-step approach. First, a region of interest (ROI) is computed to create a subvolume around the LV using morphological operations and image arithmetic. From the subvolume, the myocardial contours are automatically delineated using difference of Gaussians (DoG) filters and GSV snakes. These contours are used as a mask to identify pathological tissues, such as fibrosis or scar, within the DE-MRI. The presented automated technique is able to accurately delineate the myocardium and identify the pathological tissue in patient sets. The results were validated by two expert cardiologists, and in one set the automated results are quantitatively and qualitatively compared with expert manual delineation. Furthermore, the method is patient-specific, performed on an entire patient MRI series. Thus, in addition to providing a quick analysis of individual MRI scans, the fully automated segmentation method is used for effectively tagging regions in order to reconstruct computerized patient-specific 3D cardiac models. These models can then be used in electrophysiological studies and surgical strategy planning.

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Automatic assessment of myocardial fibrosis by delayed enhanced magnetic resonance imaging

Cited by 2 publications

References 9 publications

MRI signal distribution within the intervertebral disc as a biomarker of adolescent idiopathic scoliosis and spondylolisthesis

MRI signal distribution within the intervertebral disc as a biomarker of adolescent idiopathic scoliosis and spondylolisthesis

Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology fromin vivoMRI*

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