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Characterization of metabolites in different kiwifruit varieties by NMR and fluorescence spectroscopy

Abdul Hamid NA, Mediani A, Maulidiani M, Abas F, Park YS, Leontowicz H, et al.

J Pharm Biomed Anal, 2017 May 10;138:80-91.
PMID: 28189049 DOI: 10.1016/j.jpba.2017.01.046

It is known from our previous studies that kiwifruits, which are used in common human diet, have preventive properties of coronary artery disease. This study describes a combination of (1)H NMR spectroscopy, multivariate data analyses and fluorescence measurements in differentiating of some kiwifruit varieties, their quenching and antioxidant properties. A total of 41 metabolites were identified by comparing with literature data Chenomx database and 2D NMR. The binding properties of the extracted polyphenols against HSA showed higher reactivity of studied two cultivars in comparison with the common Hayward. The results showed that the fluorescence of HSA was quenched by Bidan as much as twice than by other fruits. The correlation between the binding properties of polyphenols in the investigated fruits, their relative quantification and suggested metabolic pathway was established. These results can provide possible application of fruit extracts in pharmaceutical industry.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Preoperative high resolution CT and MR imaging in cochlear implantation

Abdullah A, Mahmud MR, Maimunah A, Zulfiqar MA, Saim L, Mazlan R

Ann Acad Med Singap, 2003 Jul;32(4):442-5.
PMID: 12968546

INTRODUCTION: Accurate preoperative imaging of the temporal bone in patients receiving cochlear implants is important. High resolution computed tomography (HRCT) and magnetic resonance (MR) imaging are the 2 preoperative imaging modalities that provide critical information on abnormalities of the otic capsule, pneumatisation of the mastoid, middle ear abnormalities, cochlear ducts patency and presence of cochlear nerve.
MATERIALS AND METHODS: The HRCT and MR imaging in 46 cochlear implant patients in our department were reviewed.
RESULTS: Majority of our patients [34 patients (73.9%)] showed normal HRCT of the temporal bone; 5 (10.9%) patients had labyrinthitis ossificans, 2 (4.3%) had Mondini's abnormality and 2 (4.3%) had middle ear effusion. One patient each had high jugular bulb, hypoplasia of the internal auditory canal and single cochlear cavity, respectively.
CONCLUSION: The above findings contribute significantly to our surgical decisions regarding candidacy for surgery, side selection and surgical technique in cochlear implantation.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Fulltext Fetal MRI of thoraco-omphalopagus conjoined twins

Abdullah H, Abdul Wahab N, Abu Bakar K

BMJ Case Rep, 2017 Jun 13;2017.
PMID: 28611167 DOI: 10.1136/bcr-2017-219793
Matched MeSH terms: Magnetic Resonance Imaging/methods*
Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques

Acharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MKM, Tanik UJ, et al.

J Med Syst, 2019 Aug 09;43(9):302.
PMID: 31396722 DOI: 10.1007/s10916-019-1428-9

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Multimodal 3-D reconstruction of human anatomical structures using SurLens Visualization System

Adeshina AM, Hashim R, Khalid NE, Abidin SZ

Interdiscip Sci, 2013 Mar;5(1):23-36.
PMID: 23605637 DOI: 10.1007/s12539-013-0155-z

In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain's blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom's macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens' functionality as a 3-D Multimodal Visualization System.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Simultaneous EEG-fMRI for working memory of the human brain

Ahmad RF, Malik AS, Kamel N, Reza F, Abdullah JM

Australas Phys Eng Sci Med, 2016 Jun;39(2):363-78.
PMID: 27043850 DOI: 10.1007/s13246-016-0438-x

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach

Ahmad RF, Malik AS, Kamel N, Reza F, Amin HU, Hussain M

Technol Health Care, 2017;25(3):471-485.
PMID: 27935575 DOI: 10.3233/THC-161286

BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful.
METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Fulltext Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG)

Al-Faris AQ, Ngah UK, Isa NA, Shuaib IL

J Digit Imaging, 2014 Feb;27(1):133-44.
PMID: 24100762 DOI: 10.1007/s10278-013-9640-5

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Advanced MRI applications and findings of malignant phyllodes tumour: review of two cases

Alhabshi SM, Rahmat K, Abu Hassan H, Westerhout CJ, Chandran PA

Jpn J Radiol, 2013 May;31(5):342-8.
PMID: 23385379 DOI: 10.1007/s11604-013-0183-y

Phyllodes tumour or cystosarcoma phyllodes is a rare stromal breast tumour that is usually benign but on rare occasions can turn malignant. Non-specificity of the imaging features on sonography and mammography makes it difficult to distinguish malignant from benign counterparts solely based on imaging. The final diagnosis is still highly dependent on histopathological assessment. Herein, we describe two cases of malignant phyllodes tumour with emphasis on magnetic resonance (MR) imaging features using advanced MR applications.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Assessment of global and regional left ventricular function using 64-slice multislice computed tomography and 2D echocardiography: a comparison with cardiac magnetic resonance

Annuar BR, Liew CK, Chin SP, Ong TK, Seyfarth MT, Chan WL, et al.

Eur J Radiol, 2008 Jan;65(1):112-9.
PMID: 17466480

To compare the assessment of global and regional left ventricular (LV) function using 64-slice multislice computed tomography (MSCT), 2D echocardiography (2DE) and cardiac magnetic resonance (CMR).

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Fulltext Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan MJ, Mohd Rahim MS, Salim N, Rehman A, Nobanee H

J Healthc Eng, 2022;2022:2550120.
PMID: 35444781 DOI: 10.1155/2022/2550120

In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Contrast media induced transient cortical blindness

Balasingam S, Azman RR, Nazri M

QJM, 2016 Feb;109(2):121-2.
PMID: 26101228 DOI: 10.1093/qjmed/hcv121
Matched MeSH terms: Magnetic Resonance Imaging/methods
Fulltext Reduction of Motion Artifacts in the Recovery of Undersampled DCE MR Images Using Data Binning and L+S Decomposition

Bilal M, Anis H, Khan N, Qureshi I, Shah J, Kadir KA

Biomed Res Int, 2019;2019:6139785.
PMID: 31119178 DOI: 10.1155/2019/6139785

Background: Motion is a major source of blurring and ghosting in recovered MR images. It is more challenging in Dynamic Contrast Enhancement (DCE) MRI because motion effects and rapid intensity changes in contrast agent are difficult to distinguish from each other.
Material and Methods: In this study, we have introduced a new technique to reduce the motion artifacts, based on data binning and low rank plus sparse (L+S) reconstruction method for DCE MRI. For Data binning, radial k-space data is acquired continuously using the golden-angle radial sampling pattern and grouped into various motion states or bins. The respiratory signal for binning is extracted directly from radially acquired k-space data. A compressed sensing- (CS-) based L+S matrix decomposition model is then used to reconstruct motion sorted DCE MR images. Undersampled free breathing 3D liver and abdominal DCE MR data sets are used to validate the proposed technique.
Results: The performance of the technique is compared with conventional L+S decomposition qualitatively along with the image sharpness and structural similarity index. Recovered images are visually sharper and have better similarity with reference images.
Conclusion: L+S decomposition provides improved MR images with data binning as preprocessing step in free breathing scenario. Data binning resolves the respiratory motion by dividing different respiratory positions in multiple bins. It also differentiates the respiratory motion and contrast agent (CA) variations. MR images recovered for each bin are better as compared to the method without data binning.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Endoscopic transpterygoidal repair of a large cranial defect with cerebrospinal fluid leak in a patient with extensive osteoradionecrosis of the skull base: case report and technical note

Brand Y, Lim E, Waran V, Prepageran N

J Laryngol Otol, 2015 Dec;129(12):1243-7.
PMID: 26412297 DOI: 10.1017/S0022215115002601

Endoscopic endonasal techniques have recently become the method of choice in dealing with cerebrospinal fluid leak involving the anterior cranial fossa. However, most surgeons prefer an intracranial approach when leaks involve the middle cranial fossa. This case report illustrates the possibilities of using endoscopic techniques for cerebrospinal fluid leaks involving the middle fossa.

Matched MeSH terms: Magnetic Resonance Imaging/methods
A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects

Cacha LA, Parida S, Dehuri S, Cho SB, Poznanski RR

J Integr Neurosci, 2016 Dec;15(4):593-606.
PMID: 28093025 DOI: 10.1142/S0219635216500345

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

Matched MeSH terms: Magnetic Resonance Imaging/methods*
Pictorial essay on MRI local staging of rectal carcinoma: An easy approach

Cheah WH

Asia Pac J Clin Oncol, 2023 Apr;19(2):e80-e88.
PMID: 35437926 DOI: 10.1111/ajco.13782

Rectal cancer is common and accounts for more than one-third of colorectal tumors. It is associated with significant morbidity and mortality. Previously computed tomography scan is the key imaging modality in preoperative assessment to detect local invasion and distant metastasis. However, the advent of magnetic resonance imaging (MRI) has aided in local staging and prognosticates the outcome of rectal tumor. Here, the author briefly explains why rectal MRI has a comprehensive role and provides a simple and easy way in reporting an MRI rectal carcinoma, even for a non-radiologist.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction

Cheng J, Wang H, Wei S, Mei J, Liu F, Zhang G

Comput Biol Med, 2024 Mar;170:108000.
PMID: 38232453 DOI: 10.1016/j.compbiomed.2024.108000

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Fulltext Frontal mucocoele secondary to nasal polyposis: an unusual complication

Chew YK, Noorizan Y, Khir A, Brito-Mutunayagam S, Prepageran N

Singapore Med J, 2009 Nov;50(11):e374-5.
PMID: 19960147

The incidence of mucocoeles associated with a non-surgically treated nasal polyposis is rare. We report a rare case of nasal polyposis with asymptomatic frontal mucocoeles in a 28-year-old Malay man who presented with bilateral nasal obstruction with anosmia. Physical examination revealed bilateral grade III nasal polyps causing obstruction. Computed tomography revealed paranasal polyposis with a large polyp extending and expanding the posterior table of the frontal sinus causing erosion and thinning of its wall. Marsupialisation of the mucocoele and nasal polypectomy were done. Endoscopic sinus surgery and marsupialisation should be the treatment of choice for asymptomatic frontal mucocoele.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Fulltext Cardiac imaging in valvular heart disease

Choo WS, Steeds RP

Br J Radiol, 2011 Dec;84 Spec No 3:S245-57.
PMID: 22723532 DOI: 10.1259/bjr/54030257

The aim of this article is to provide a perspective on the relative importance and contribution of different imaging modalities in patients with valvular heart disease. Valvular heart disease is increasing in prevalence across Europe, at a time when the clinical ability of physicians to diagnose and assess severity is declining. Increasing reliance is placed on echocardiography, which is the mainstay of cardiac imaging in valvular heart disease. This article outlines the techniques used in this context and their limitations, identifying areas in which dynamic imaging with cardiovascular magnetic resonance and multislice CT are expanding.

Matched MeSH terms: Magnetic Resonance Imaging/methods
Improving diagnostic quality of MR images through controlled lossy compression using SPIHT

Choong MK, Logeswaran R, Bister M

J Med Syst, 2006 Jun;30(3):139-43.
PMID: 16848126

This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR's, it becomes virtually impossible to distinguish between the original and compressed-decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.

Matched MeSH terms: Magnetic Resonance Imaging/methods*

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