Whole-body MRI: comprehensive evaluation on a 48-channel 3T MRI system in less than 40 minutes. Preliminary results

OBJECTIVE: To evaluate a comprehensive MRI protocol that investigates for cancer, vascular disease, and degenerative/inflammatory disease from the head to the pelvis in less than 40 minutes on a new generation 48-channel 3T system. MATERIALS AND METHODS: All MR studies were performed on a 48-channel 3T MR scanner. A 20-channel head/neck coil, two 18-channel body arrays, and a 32-channel spine array were employed. A total of 4 healthy individuals were studied. The designed protocol included a combination of single-shot T2-weighted sequences, T1-weighted 3D gradient-echo pre- and post-gadolinium. All images were retrospectively evaluated by two radiologists independently for overall image quality. RESULTS: The image quality for cancer was rated as excellent in the liver, pancreas, kidneys, lungs, pelvic organs, and brain, and rated as fair in the colon and breast. For vascular diseases ratings were excellent in the aorta, major branch vessel origins, inferior vena cava, portal and hepatic veins, rated as good in pulmonary arteries, and as poor in the coronary arteries. For degenerative/inflammatory diseases ratings were excellent in the brain, liver and pancreas. The inter-observer agreement was excellent. CONCLUSION: A comprehensive and time efficient screening for important categories of disease processes may be achieved with high quality imaging in a new generation 48-channel 3T system.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Near-Term Fetal Hypoxia-Ischemia in Rabbits MRI Can Predict Muscle Tone Abnormalities and Deep Brain Injury

Background and Purpose-The pattern of antenatal brain injury varies with gestational age at the time of insult. Deep brain nuclei are often injured at older gestational ages. Having previously shown postnatal hypertonia after preterm fetal rabbit hypoxia-ischemia, the objective of this study was to investigate the causal relationship between the dynamic regional pattern of brain injury on MRI and the evolution of muscle tone in the near-term rabbit fetus. Methods-Serial MRI was performed on New Zealand white rabbit fetuses to determine equipotency of fetal hypoxia-ischemia during uterine ischemia comparing 29 days gestation (E29, 92% gestation) with E22 and E25. E29 postnatal kits at 4, 24, and 72 hours after hypoxia-ischemia underwent T2- and diffusion-weighted imaging. Quantitative assessments of tone were made serially using a torque apparatus in addition to clinical assessments. Results-Based on the brain apparent diffusion coefficient, 32 minutes of uterine ischemia was selected for E29 fetuses. At E30, 58% of the survivors manifested hind limb hypotonia. By E32, 71% of the hypotonic kits developed dystonic hypertonia. Marked and persistent apparent diffusion coefficient reduction in the basal ganglia, thalamus, and brain stem was predictive of these motor deficits. Conclusions-MRI observation of deep brain injury 6 to 24 hours after near-term hypoxia-ischemia predicts dystonic hypertonia postnatally. Torque-displacement measurements indicate that motor deficits in rabbits progressed from initial hypotonia to hypertonia, similar to human cerebral palsy, but in a compressed timeframe. The presence of deep brain injury and quantitative shift from hypo-to hypertonia may identify patients at risk for developing cerebral palsy. (Stroke. 2012;43:2757-2763.)

Whole-body MRI: comprehensive evaluation on a 48-channel 3T MRI system in less than 40 minutes. Preliminary results

OBJECTIVE: To evaluate a comprehensive MRI protocol that investigates for cancer, vascular disease, and degenerative/inflammatory disease from the head to the pelvis in less than 40 minutes on a new generation 48-channel 3T system. MATERIALS AND METHODS: All MR studies were performed on a 48-channel 3T MR scanner. A 20-channel head/neck coil, two 18-channel body arrays, and a 32-channel spine array were employed. A total of 4 healthy individuals were studied. The designed protocol included a combination of single-shot T2-weighted sequences, T1-weighted 3D gradient-echo pre- and post-gadolinium. All images were retrospectively evaluated by two radiologists independently for overall image quality. RESULTS: The image quality for cancer was rated as excellent in the liver, pancreas, kidneys, lungs, pelvic organs, and brain, and rated as fair in the colon and breast. For vascular diseases ratings were excellent in the aorta, major branch vessel origins, inferior vena cava, portal and hepatic veins, rated as good in pulmonary arteries, and as poor in the coronary arteries. For degenerative/inflammatory diseases ratings were excellent in the brain, liver and pancreas. The inter-observer agreement was excellent. CONCLUSION: A comprehensive and time efficient screening for important categories of disease processes may be achieved with high quality imaging in a new generation 48-channel 3T system.

Evaluation of renal allograft function early after transplantation with diffusion-weighted MR imaging

To determine the inter-patient variability of apparent diffusion coefficients (ADC) and concurrent micro-circulation contributions from diffusion-weighted MR imaging (DW-MRI) in renal allografts early after transplantation, and to obtain initial information on whether these measures are altered in histologically proven acute allograft rejection (AR).

EPITHET: Positive Result After Reanalysis Using Baseline Diffusion-Weighted Imaging/Perfusion-Weighted Imaging Co-Registration

the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET) was a prospective, randomized, double-blinded, placebo-controlled, phase II trial of alteplase between 3 and 6 hours after stroke onset. The primary outcome of infarct growth attenuation on MRI with alteplase in mismatch patients was negative when mismatch volumes were assessed volumetrically, without coregistration, which underestimates mismatch volumes. We hypothesized that assessing the extent of mismatch by coregistration of perfusion and diffusion MRI maps may more accurately allow the effects of alteplase vs placebo to be evaluated.

DW-MRI of the urogenital tract: applications in oncology

Diffusion-weighted magnetic resonance imaging (DW-MRI) appears to hold promise as a non-invasive imaging modality in the detection of early microstructural and functional changes of different organs. DW-MRI is an imaging technique with a high sensitivity for the detection of a large variety of diseases in the urogenital tract. In kidneys, DW-MRI has shown promise for the characterization of solid lesions. Also in focal T1 hyperintense lesions DW-MRI was able to differentiate hemorrhagic cysts from tumours according to the lower apparent diffusion coefficient (ADC) values reported for renal cell carcinomas. Promising results were also published for the detection of prostate cancer. DW-MRI applied in addition to conventional T2-weighted imaging has been found to improve tumour detection. On a 3 T magnetic resonance unit ADC values were reported to be lower for tumours compared with the normal-appearing peripheral zone. The combined approach of T2-weighted imaging and DW-MRI also showed promising results for the detection of recurrent tumour in patients after radiation therapy. DW-MRI may improve the performance of conventional T2-weighted and contrast-enhanced MRI in the preoperative work-up of bladder cancer, as it may help in distinguishing superficial from muscle invasive bladder cancer, which is critical for patient management. Another challenging application of DW-MRI in the urogenital tract is the detection of pelvic lymph node metastases. As the ADC is generally reduced in malignant tumours and increased under inflammatory conditions, reduced ADC values were expected in patients with lymph node metastases.

Diffusion-weighted magnetic resonance imaging detects local recurrence after radical prostatectomy: initial experience

Current conventional cross-sectional imaging techniques, such as contrast-enhanced computed tomography and magnetic resonance imaging (MRI), are largely inaccurate in detecting local recurrence after radical prostatectomy. We report on five patients with biochemical recurrence after radical retropubic prostatectomy and pelvic lymph node dissection for whom local recurrence could only be detected with diffusion-weighted (DW) MRI. Prior to DW-MRI, all patients had negative digital rectal examinations, negative or equivocal conventional cross-sectional imaging, and negative bone scans. All suspicious lesions on DW-MRI imaging were histologically proved to be local recurrences of prostate cancer after either transrectal ultrasound-guided or transurethral biopsy. These results should encourage other centres to test our findings.

Gadolinium diethylenetriaminepentaacetate enhancement kinetics in the menisci of asymptomatic subjects: a first step towards a dedicated dGEMRIC (delayed gadolinium-enhanced MRI of cartilage)-like protocol for biochemical imaging of the menisci

It was our aim to investigate the gadolinium diethylenetriaminepentaacetate (Gd-DTPA(2-) ) enhancement kinetics in the menisci of the knee joint over a prolonged period of time. Six asymptomatic volunteers (four men and two women; mean age, 25 ± 2.4 years) were enrolled. Sagittal, T(1) -weighted, spin-echo MR sequences of the right knee joint were obtained at 3 T. Imaging was performed before (baseline), 1 h after and in half-hour intervals up to 9 h after the intravenous administration of 0.2 mmol/kg of Gd-DTPA(2-) . To measure the rates of contrast enhancement relative to the baseline, regions of interest that covered the anterior and posterior horns of the medial and lateral meniscus were defined on each of two adjacent sections, and enhancement curves were constructed. An enhancement peak between 2.5 and 4.5 h after Gd-DTPA(2-) administration was observed, and analysis of variance also revealed no significant difference (p=0.94), in terms of enhancement, within this time interval. Pair-wise, post hoc testing also revealed no significant differences between 2.5 and 3, 3 and 3.5, 3.5 and 4, and 4 and 4.5 h post Gd-DTPA(2-) application. Our preliminary data therefore suggest that the time window suitable for a dGEMRIC (delayed gadolinium-enhanced MRI of cartilage)-like T(1) mapping of the menisci is relatively short, and lies between 2.5 and 4.5 h after Gd-DTPA(2-) injection.

Potential and limitations of diffusion-weighted magnetic resonance imaging in kidney, prostate, and bladder cancer including pelvic lymph node staging: a critical analysis of the literature

Diagnosis, staging, and treatment monitoring are still suboptimal for most genitourinary tumours. Diffusion-weighted magnetic resonance imaging (DW-MRI) has already shown promise as a noninvasive imaging modality in the early detection of microstructural and functional changes in several pathologies of various organs.

Perfusion-diffusion mismatch in MRI to indicate endovascular treatment of cerebral vasospasm after subarachnoid haemorrhage

Endovascular treatments such as transluminal balloon angioplasty and intra-arterial nimodipine represent rescue therapy for cerebral vasospasm (CVS) after aneurysmal subarachnoid haemorrhage (SAH). Both indication and data regarding its efficacy in the prevention of cerebral infarct are, however, inconsistent. Therefore, an MR based perfusion weighted imaging/diffusion weighted imaging (PWI/DWI) mismatch was used to indicate this treatment and to characterise its effectiveness.