Quantitative Analysis Of MR Images Research Articles

Noninvasive Assessment of Kidney Injury by Combining Structure and Function Using Artificial Intelligence-Based Manganese-Enhanced Magnetic Resonance Imaging.

Contrast-enhanced magnetic resonance imaging (MRI) is seriously limited in kidney injury detection due to the nephrotoxicity of clinically used gadolinium-based contrast agents. Herein, we propose a noninvasive method for the assessment of kidney injury by combining structure and function information based on manganese (Mn)-enhanced MRI for the first time. As a proof of concept, the Mn-melanin nanoprobe with good biocompatibility and excellent T1 relaxivity is applied in MRI of a unilateral ureteral obstruction mice model. The abundant renal structure and function information is obtained through qualitative and quantitative analysis of MR images, and a brand new comprehensive assessment framework is proposed to precisely identify the degree of kidney injury successfully. Our study demonstrates that Mn-enhanced MRI is a promising approach for the highly sensitive and biosafe assessment of kidney injury in vivo.

A semi-automatic framework based upon quantitative analysis of MR-images for classification of femur cartilage into asymptomatic, early OA, and advanced-OA groups.

To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2-mapping, 2D-WearMap generation, and subregion analysis were performed semi-automatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computer-aided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging.

Magnetic resonance imaging is widely available and accepted as the imaging method of choice for many pediatric body imaging applications. Traditionally, it has been used in a qualitative way, where the images are reported non-numerically by radiologists. But now MRI machines have built-in post-processing software connected to the scanner and the database of MR images. This setting enables and encourages simple quantitative analysis of MR images. In this paper, the author reviews the fundamentals of MRI and discusses the most common quantitative MRI techniques for body imaging: T1, T2, T2*, T1rho and diffusion-weighted imaging (DWI). For each quantitative imaging method, this article reviews the technique, its measurement mechanism, and selected clinical applications to body imaging.

Holmium-166 Radioembolization in Hepatocellular Carcinoma: Feasibility and Safety of a New Treatment Option in Clinical Practice

To investigate clinical feasibility, technical success and toxicity of 166Ho-radioembolization (166Ho-RE) as new approach for treatment of hepatocellular carcinomas (HCC) and to assess postinterventional calculation of exact dosimetry through quantitative analysis of MR images. From March 2017 to April 2018, nine patients suffering from HCC were treated with 166Ho-RE. To calculate mean doses on healthy liver/tumor tissue, MR was performed within the first day after treatment. For evaluation of hepatotoxicity and to rule out radioembolization-induced liver disease (REILD), the Model for End-Stage Liver Disease (MELD) Score, the Common Terminology Criteria for Adverse Events and specific laboratory parameters were used 1-day pre- and posttreatment and after 60days. After 6months, MR/CT follow-up was performed. In five patients the right liver lobe, in one patient the left liver lobe and in three patients both liver lobes were treated. Median administered activity was 3.7GBq (range 1.7-5.9GBq). Median dose on healthy liver tissue was 41Gy (21-55Gy) and on tumor tissue 112Gy (61-172Gy). Four patients suffered from mild postradioembolization syndrome. No significant differences in median MELD-Score were observed pre-, posttherapeutic and 60days after 166Ho-RE. No deterioration of liver function and no indicators of REILD were observed. One patient showed a complete response, four a partial response, three a stable disease and one a progressive disease at the 6months follow-up. 166Ho-RE seems to be a feasible and safe treatment option with no significant hepatotoxicity for treatment of HCC.

Quantitative MR Image Analysis for Brian Tumor.

This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) texture, and stochastic texture such as multifractional Brownian motion (mBm) and GmBm features for robust tumor and other abnormal tissue segmentation in brain MRI. We evaluate these texture and associated intensity features to effectively delineate multiple abnormal tissues within and around the tumor core, and stroke lesions using large scale public and private datasets.

Segmentation of Cerebrospinal Fluid and Internal Brain Nuclei in Brain Magnetic Resonance Images

Brain tissue segmentation on structural Magnetic Resonance Imaging (MRI) has received considerable attention. Quantitative analysis of MR images of the brain is of interest in order to study the aging brain in epidemiological studies, to better understand how diseases affect the brain and to support diagnosis in clinical practice. Manual quantitative analysis of brain imaging data is a tedious and time-consuming procedure, prone to observer variability. Therefore, there is a large interest in automatic analysis of MR brain imaging data, especially segmentation of Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM). This paper presents a fully automated method for the segmentation of cerebrospinal fluid and internal brain nuclei from T1-weighted MRI head scans. The proposed methodology performs intensity based thresholding to get the boundaries between gray matter, white matter, cerebrospinal fluid and others. Combined with preprocessing techniques and incorporating mathematical morphology, we first perform the extraction of brain cortex. Subsequently, the cerebrospinal fluid is segmented by using orthogonal polynomial transform. Finally, the gray matter and the white matter regions in the MRI are segmented based on the intensity values. Experimental results show that the proposed method achieves reasonably good segmentation. The comparative analysis depicts that the proposed methodology shows better segmentation results with some other existing techniques like FAST, SPM5, k-nearest neighbor (k-NN) classifier, and a conventional k-NN.

Change in cartilage thickness in the femoropatellatar joint after acute anterior cruciate ligament tear – long term follow-up

Purpose: Patellofemoral joint (PFJ) osteoarthritis (OA) is a prevalent disease affecting young and elderly individuals and is recognized as a potent source of knee symptoms. Using quantitative MR image analysis it was reported that the femoral trochlea undergoes cartilage thinning within the first two years after an ACL tear and that such thinning wasmore frequent in older individuals. It is however unknown whether these changes continue (and if yes, at which magnitude) over longer follow-up periods. Thus, the purpose of the present work was to study the rate of change in cartilage thickness in the PFJ during the period of 2-5years after ACL tear and to explore differences related to age, gender and BMI. Methods: 121 young active adults (32 women, 26.0 4.9 years) with an acute ACL tear were included in a randomized control trial (the KANON-trial). The study compared rehabilitation plus early ACL reconstruction (n1⁄462), with rehabilitation plus the option of having delayed ACL reconstruction if needed (n1⁄459). Mean cartilage thickness was assessed by manual segmentation in the patella and femoral trochlea with blinding to time points and treatment groups. Crude and adjusted (age, sex & BMI) statistical testing was performed to explore the magnitude of cartilage loss between baseline (BL, within 4 weeks from ACL tear)-2years and 2-5years in the patella and the femoral trochlea. Results: 107 of 121 participants had a complete set of MR images at BL, 2 and 5 year follow up. Mean change of cartilage thickness in patella and trochlea was -8.9mm/[-26.4, 8.5] and -44.9mm/[-67.0, -22.8] (mean/[95% CI]) over the first 2 years after ACL tear and -3.8mm/[-22.1, 14.6] and -1.0mm/[-15.6, 13.5] over the period between 2-5 years, respectively. Older patients (above median age, 25.63years) lost significantly more cartilage thickness than younger individuals (below median age) in both the patella (p1⁄40.022) and in the trochlea (p1⁄40.009) over the first 2 years. Similar differences were seen in the patella (p1⁄40.004), but not in the trochlea (p1⁄40.17), between 2-5years (Table 1). Compared to individuals with a lower BMI (belowmedian, 23.66kg/m), individuals with a higher BMI (above median) had a significant decrease in cartilage thickness of the trochlea over the first 2 years (p1⁄40.045) but not between 2-5years. No corresponding differences related to BMI or gender were observed in the patella (Table 1). In a multi-variate model including age, BMI and gender, older age at ACL tear significantly increased the odds of losing cartilage thickness in the femoral trochlea (OR 1.06, 95% CI 1.01, 1.10) over the first 2 years after tear and in the patella (OR 1.04, 1.00, 1.08) in the period 2-5 years. Conclusions: These results show that cartilage loss in the femoral trochlea may be an early and temporary event occurring over the first 2 years after ACL tear. In contrast to that, cartilage loss in the patellar cartilage seems to occur later than 2 years after the ACL tear. Older age seems to be a risk factor for these changes. Our results indicate that different mechanisms may drive loss of cartilage in the femoropatellar joint after ACL tear.

Quantitative MR image analysis in subjects with defects in the PAX6 gene

Cerebral development is governed by genetic and environmental factors. Animal models provide much of our knowledge about genetic influences in the brain but it is not clear how similar or relevant these are to the human brain. The investigation of human subjects with mutations in genes known to be expressed in the developing brain is an alternative approach to improving our understanding of the role of genetic factors in brain development. We investigated 24 subjects with known mutations in the PAX6 gene (including representatives of all the known mutations of the gene) which are associated with characteristic ocular abnormalities in humans and animals. We have quantified MRI data using several techniques to assess qualities of cerebral structure which are difficult to interpret visually. Abnormalities were identified using voxel-based morphometry, statistical morphometrics, and measurement of corpus callosum cross-sectional area when comparing data from subjects with PAX6 abnormality and 72 age-and sex-matched control subjects. These abnormalities include grey matter changes in the cerebellum and occipital poles and white matter loss in the corpus callosum, alteration of sulcal orientation in the occipital lobe, and alteration to overall neuronal connectivity. These abnormalities complement and exceed the changes seen in the mouse models, and those seen on visual inspection alone of the human MRI data. Structural differences were also identified between the two largest genotype mutation subgroups.

Automated segmentation of MS lesions in MR

Objective Quantitative analysis of MR images is becoming increasingly important in assessing the progression of multiple sclerosis (MS) and in monitoring the effect of a drug therapy. In clinical trials, manual analysis of the MR images by human experts is prohibitively time-consuming because of the large amounts of data typically involved. The interand mtra-observer variability associated with manual delineations. and the unclear decision making process when multi-spectral MR data are presented to a human expert. further complicate matters. The goal of our work is to develop a fast, fully-automated method for MS lesion segmentation that analysis multi-spectral MR data in an objective and reproducible way.

MR imaging of the parotid gland in Sjögren's syndrome: a proposal for new diagnostic criteria.

Because classic diagnostic techniques for sjögren's syndrome (SS) are invasive and require radiation exposure, a noninvasive diagnostic method might benefit patients with SS. The purpose of this study was to determine whether quantitative analysis of MR images of the parotid gland can differentiate patients with SS from normal subjects. We performed quantitative analysis of MR images of the parotid gland for 40 patients with definite and probable SS, for 30 normal subjects matched by age and sex to the SS patients, and for 10 patients with parotid inflammation. MR images of teh parotid gland were assessed by calculating standard deviations (SD) of signal intensity. SD of signal intensity exclusively (p < .0001) separated MR images of the parotid glands of patients with definite SS from those of normal subjects. SD of signal intensity for the parotid glands of patients with probable SS also were significantly higher (p < .001) than those of normal subjects. Changes in signal intensity were specific for SS and did not occur with parotid inflammation. Tentative categorization on the basis of signal intensity patterns on MR images of the parotid glands of SS patients showed a high correlation with the results of labial gland biopsy (r = .834) and sialography (r = .936). Taken together, these results conclusively indicate that quantitative analysis of MR images for SD of signal intensity is useful method for diagnosing SS and can replace classic methods, which are invasive.

Correction of intensity variations in MR images for computer-aided tissue classification

A number of supervised and unsupervised pattern recognition techniques have been proposed in recent years for the segmentation and the quantitative analysis of MR images. However, the efficacy of these techniques is affected by acquisition artifacts such as inter-slice, intra-slice, and inter-patient intensity variations. Here a new approach to the correction of intra-slice intensity variations is presented. Results demonstrate that the correction process enhances the performance of backpropagation neural network classifiers designed for the segmentation of the images. Two slightly different versions of the method are presented. The first version fits an intensity correction surface directly to reference points selected by the user in the images. The second version fits the surface to reference points obtained by an intermediate classification operation. Qualitative and quantitative evaluation of both methods reveals that the first one leads to a better correction of the images than the second but that it is more sensitive to operator errors.

Fatty Liver

In vitro animal and human models were used to evaluate the potential of chemical shift magnetic resonance imaging (MRI) for assessing fatty liver. Phantoms of varying fat content were created from mayonnaise-agar preparations. Fatty liver was induced in eight rats by feeding them ethanol for three to six weeks (36% of total calories), whereas eight control rats were fed a normal diet. T1-weighted in-phase and opposed-phase MR images were obtained of the phantoms animals, and 28 human subjects. Additional images obtained in animals included long TR images with in-phase and opposed-phase technique, and hybrid chemical shift water and fat suppression. The rats were killed and histologic status was graded blindly by a hepatopathologist as normal, mild, moderate, or severe fatty change, for correlation with MR grading. Quantitative analysis of MR images included fat signal fraction for animals, and relative signal decrease between in-phase and opposed-phase images for phantom and human data. Phantom in-phase signal increased linearly with respect to fat content, whereas opposed-phase signal decreased linearly. MRI and histologic grading of rat livers were highly correlated, especially when based on water suppression images (r = 0.91, P = .0001). Opposed-phase images were also highly correlated, while fat suppression images were less effective. There was no overlap between MR-derived fat fractions for control (2.6%-5.7%) versus ethanol-fed rats (7.7%-17.9%, P = .0002). Human liver considered to be fatty by visual inspection (n = 8) had higher relative signal decrease than nonfatty liver (n = 22) (P less than .001). Phantom, animal, and human data demonstrate that comparison of T1-weighted in-phase and opposed-phase images is both practical and sensitive in the detection and grading of fatty liver.