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Left atrial strain: a multi-modality, multi-vendor comparison study

Pathan F, Zainal Abidin HA, Vo QH, Zhou H, D'Angelo T, Elen E, et al.

Eur Heart J Cardiovasc Imaging, 2021 01 01;22(1):102-110.
PMID: 31848575 DOI: 10.1093/ehjci/jez303

AIMS: Left atrial (LA) strain is a prognostic biomarker with utility across a spectrum of acute and chronic cardiovascular pathologies. There are limited data on intervendor differences and no data on intermodality differences for LA strain. We sought to compare the intervendor and intermodality differences between transthoracic echocardiography (TTE) and cardiac magnetic resonance (CMR) derived LA strain. We hypothesized that various components of atrial strain would show good intervendor and intermodality correlation but that there would be systematic differences between vendors and modalities.
METHODS AND RESULTS: We evaluated 54 subjects (43 patients with a clinical indication for CMR and 11 healthy volunteers) in a study comparing TTE- and CMR-derived LA reservoir strain (ƐR), conduit strain (ƐCD), and contractile strain (ƐCT). The LA strain components were evaluated using four dedicated types of post-processing software. We evaluated the correlation and systematic bias between modalities and within each modality. Intervendor and intermodality correlation was: ƐR [intraclass correlation coefficient (ICC 0.64-0.90)], ƐCD (ICC 0.62-0.89), and ƐCT (ICC 0.58-0.77). There was evidence of systematic bias between vendors and modalities with mean differences ranging from (3.1-12.2%) for ƐR, ƐCD (1.6-8.6%), and ƐCT (0.3-3.6%). Reproducibility analysis revealed intraobserver coefficient of variance (COV) of 6.5-14.6% and interobserver COV of 9.9-18.7%.
CONCLUSION: Vendor derived ƐR, ƐCD, and ƐCT demonstrates modest to excellent intervendor and intermodality correlation depending on strain component examined. There are systematic differences in measurements depending on modality and vendor. These differences may be addressed by future studies, which, examine calibration of LA geometry/higher frame rate imaging, semi-quantitative approaches, and improvements in reproducibility.

Matched MeSH terms: Magnetic Resonance Imaging/instrumentation*
Referral of epileptic patients in North East Coast of West Malaysia an area with poor MRI coverage: an analysis

Mohamed Y, Alias NN, Shuaib IL, Tharakan J, Abdullah J, Munawir AH, et al.

Southeast Asian J. Trop. Med. Public Health, 2006 Nov;37(6):1199-208.
PMID: 17333778

Advances in neuroimaging techniques, particularly Magnetic Resonance Imaging (MRI), have proved invaluable in detecting structural brain lesions in patients with epilepsy in developed countries. In Malaysia, a few electroencephalography facilities available in rural district hospitals run by trained physician assistants have Internet connections to a government neurological center in Kuala Lumpur. These facilities are more commonly available than MRI machines, which require radiological expertise and helium replacement, which may problematic in Southeast Asian countries where radiologists are found in mainly big cities or towns. We conducted a cross-sectional study over a two year period begining January 2001 on rural patients, correlating EEG reports and MRI images with a clinical diagnosis of epilepsy to set guidelines for which rural patients need to be referred to a hospital with MRI facilities. The patients referred by different hospitals without neurological services were classified as having generalized, partial or unclassified seizures based on the International Classification of Epileptic Seizures proposed by the International League Against Epilepsy (ILAE). The clinical parameters studied were seizure type, seizure frequency, status epilepticus and duration of seizure. EEG reports were reviewed for localized and generalized abnormalities and epileptiform changes. Statistical analysis was performed using logistic regression and area under the curve. The association between clinical and radiological abnormalities was evaluated for sensitivity and specificity. Twenty-six males and 18 females were evaluated. The mean age was 20.7 +/- 13.3 years. Nineteen (43.2%) had generalized seizures, 22 (50.0%) had partial seizures and 3 (6.8%) presented with unclassified seizures. The EEG was abnormal in 30 patients (20 with generalized abnormalities and 10 localized abnormalities). The MRI was abnormal in 17 patients (38.6%); the abnormalities observed were cerebral atrophy (5), hippocampal sclerosis (4), infarct/gliosis (3), cortical dysgenesis (2) and tumors (2). One patient had an arachnoid cyst in the right occipital region. Of the 17 patients with an abnormal MRI, 14 had an abnormal EEG, this difference was not statistically significant. There was no significant associaton between epileptographic changes and MRI findings (p = 0.078). EEG findings were associated with MRI findings (p = 0.004). The association between an abnormal EEG and an abnormal MRI had a specificity of 82.4%, while epileptogenic changes had a specificity of 64.7% in relation to abnormal MRI findings. This meants that those patients in rural hospitals with abnormal EEGs should be referred to a neurology center for further workup and an MRI to detect causes with an epileptic focus.

Matched MeSH terms: Magnetic Resonance Imaging/instrumentation
Fulltext An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

Siddiqui MF, Reza AW, Kanesan J

PLoS One, 2015;10(8):e0135875.
PMID: 26280918 DOI: 10.1371/journal.pone.0135875

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Matched MeSH terms: Magnetic Resonance Imaging/instrumentation
Fulltext Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms

Ray KJ, Larkin JR, Tee YK, Khrapitchev AA, Karunanithy G, Barber M, et al.

NMR Biomed, 2016 11;29(11):1624-1633.
PMID: 27686882 DOI: 10.1002/nbm.3614

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated. Novel phantom models were produced, by adding perchloric acid extracts of either metastatic mouse breast carcinoma cells or healthy mouse brain to bovine serum albumin. The phantom model was validated using 1 H NMR spectroscopy, then utilized to determine the sensitivity of CEST MRI to changes in pH, labile proton concentration, T1 time and T2 time; six different CEST MRI analysis metrics (MTRasym , APT*, MTRRex , AREX and CESTR* with and without T1 /T2 compensation) were compared. The new phantom models were highly representative of the in vivo intracellular environment of both tumour and brain tissue. Of the analysis methods compared, CESTR* with T1 and T2 time compensation was optimally specific to changes in the CEST effect (i.e. minimal contamination from T1 or T2 variation). In phantoms with identical protein concentrations, pH differences between phantoms could be quantified with a mean accuracy of 0.6 pH units. We propose that CESTR* with T1 and T2 time compensation is the optimal analysis method for these phantoms. Analysis of CEST MRI data with T1 /T2 time compensated CESTR* is reproducible between phantoms, and its application in vivo may resolve the intracellular alkalosis associated with breast cancer brain metastases without the need for exogenous contrast agents.

Matched MeSH terms: Magnetic Resonance Imaging/instrumentation*