Zaid Bin Mahbub
@northsouth.edu
Associate Professor, Department of Mathematics & Physics, School of Engineering and Physical Sciences
North South University
EDUCATION
Doctor of Philosophy
School of Physics & Astronomy, University of Nottingham, UK, 2014
Master of Science in Physics (1st Class)
Department of Physics, University of Dhaka, Bangladesh, 2006
Bachelor of Science in Physics (1st Class)
Department of Physics, University of Dhaka, Bangladesh, 2004
Higher Secondary Certificate Science Group (1st Division)
Government Science College, Dhaka, 1996
Secondary School Certificate Science Group(1st Division)
Government Science College, Dhaka, 1994
RESEARCH INTERESTS
Biomedical Physics and Engineering; MRI, Quantitative MRI, pulse sequence; nerve conduction studies
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Kanchon Kanti Podder, Maymouna Ezeddin, Muhammad E. H. Chowdhury, Md. Shaheenur Islam Sumon, Anas M. Tahir, Mohamed Arselene Ayari, Proma Dutta, Amith Khandakar, Zaid Bin Mahbub, and Muhammad Abdul Kadir
MDPI AG
Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person. A sign language recognition system can help a signer to interpret the sign of a non-sign language user. This study presents a sign language recognition system that is capable of recognizing Arabic Sign Language from recorded RGB videos. To achieve this, two datasets were considered, such as (1) the raw dataset and (2) the face–hand region-based segmented dataset produced from the raw dataset. Moreover, operational layer-based multi-layer perceptron “SelfMLP” is proposed in this study to build CNN-LSTM-SelfMLP models for Arabic Sign Language recognition. MobileNetV2 and ResNet18-based CNN backbones and three SelfMLPs were used to construct six different models of CNN-LSTM-SelfMLP architecture for performance comparison of Arabic Sign Language recognition. This study examined the signer-independent mode to deal with real-time application circumstances. As a result, MobileNetV2-LSTM-SelfMLP on the segmented dataset achieved the best accuracy of 87.69% with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity. Overall, face–hand region-based segmentation and SelfMLP-infused MobileNetV2-LSTM-SelfMLP surpassed the previous findings on Arabic Sign Language recognition by 10.970% accuracy.
Arafat Rahman, Sakib Mahmud, Muhammad E.H. Chowdhury, Huseyin Cagatay Yalcin, Amith Khandakar, Onur Mutlu, Zaid Bin Mahbub, Reema Yousef Kamal, and Shona Pedersen
Elsevier BV
Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Zaid Bin Mahbub, Md Sakib Abrar Hossain, Abraham Alhatou, Eynas Abdalla, Sreekumar Muthiyal, Khandaker Farzana Islam, Saad Bin Abul Kashem,et al.
Springer Science and Business Media LLC
AbstractNowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March–June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.
Md Shafayet Hossain, Sakib Mahmud, Amith Khandakar, Nasser Al-Emadi, Farhana Ahmed Chowdhury, Zaid Bin Mahbub, Mamun Bin Ibne Reaz, and Muhammad E. H. Chowdhury
MDPI AG
Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models’ performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.
Md Nazmul Islam Shuzan, Muhammad E.H. Chowdhury, Mamun Bin Ibne Reaz, Amith Khandakar, Farhan Fuad Abir, Md. Ahasan Atick Faisal, Sawal Hamid Md Ali, Ahmad Ashrif A. Bakar, Moajjem Hossain Chowdhury, Zaid B. Mahbub,et al.
Elsevier BV
Sabrina Sharmin, Mohammad Abu Sayem Karal, Zaid Bin Mahbub, and Khondkar Siddique-e Rabbani
Frontiers Media SA
BackgroundBased on published experimental evidence, a recent publication revealed an anomalous phenomenon in nerve conduction: for myelinated nerves the nerve conduction velocity (NCV) increases with stretch, which should have been the opposite according to existing concepts and theories since the diameter decreases on stretching. To resolve the anomaly, a new conduction mechanism for myelinated nerves was proposed based on physiological changes in the nodal region, introducing a new electrical resistance at the node. The earlier experimental measurements of NCV were performed on the ulnar nerve at different angles of flexion, focusing at the elbow region, but left some uncertainty for not reporting the lengths of nerve segments involved so that the magnitudes of stretch could not be estimated.AimsThe aim of the present study was to relate NCV of myelinated nerves with different magnitudes of stretch through careful measurements.MethodEssentially, we duplicated the earlier published NCV measurements on ulnar nerves at different angles of flexion but recording appropriate distances between nerve stimulation points on the skin carefully and assuming that the lengths of the underlying nerve segment undergoes the same percentages of changes as that on the skin outside.ResultsWe found that the percentage of nerve stretch across the elbow is directly proportional to the angle of flexion and that the percentage increase in NCV is directly proportional to the percentage increase in nerve stretch. Page’s L Trend test also supported the above trends of changes through obtained p values.DiscussionOur experimental findings on myelinated nerves agree with those of some recent publications which measured changes in CV of single fibres, both myelinated and unmyelinated, on stretch. Analyzing all the observed results, we may infer that the new conduction mechanism based on the nodal resistance and proposed by the recent publication mentioned above is the most plausible one to explain the increase in CV with nerve stretch. Furthermore, interpreting the experimental results in the light of the new mechanism, we may suggest that the ulnar nerve at the forearm is always under a mild stretch, with slightly increased NCV of the myelinated nerves.
Md. Ahasan Atick Faisal, Muhammad E. H. Chowdhury, Zaid Bin Mahbub, Shona Pedersen, Mosabber Uddin Ahmed, Amith Khandakar, Mohammed Alhatou, Mohammad Nabil, Iffat Ara, Enamul Haque Bhuiyan,et al.
Springer Science and Business Media LLC
Amith Khandakar, Sakib Mahmud, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Serkan Kiranyaz, Zaid Bin Mahbub, Sawal Hamid Ali, Ahmad Ashrif A. Bakar, Mohamed Arselene Ayari, Mohammed Alhatou,et al.
MDPI AG
An intelligent insole system may monitor the individual’s foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals’ foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.
Amith Khandakar, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Tariq O. Abbas, Tanvir Alam, Mohamed Arselene Ayari, Zaid B. Mahbub, Rumana Habib, Tawsifur Rahman,et al.
MDPI AG
Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter—the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.
Kanchon Kanti Podder, Muhammad E. H. Chowdhury, Anas M. Tahir, Zaid Bin Mahbub, Amith Khandakar, Md Shafayet Hossain, and Muhammad Abdul Kadir
MDPI AG
A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.
Mohammad Abu Sayem Karal, Md. Kabir Ahamed, Marzuk Ahmed, and Zaid Bin Mahbub
Royal Society of Chemistry (RSC)
The kinetics of ruptures of giant vesicles under constant tension have been investigated under various conditions, which will help in the application of these techniques in the biomedical, bioengineering, and biotechnological fields.
Mohammad Abu Sayem Karal, Tawfika Nasrin, Marzuk Ahmed, Md. Kabir Ahamed, Shareef Ahammed, Salma Akter, Sharif Hasan, and Zaid Bin Mahbub
Public Library of Science (PLoS)
A new purification technique is developed for obtaining distribution of giant unilamellar vesicles (GUVs) within a specific range of sizes using dual filtration. The GUVs were prepared using well known natural swelling method. For filtration, different combinations of polycarbonate membranes were implemented in filter holders. In our experiment, the combinations of membranes were selected with corresponding pore sizes–(i) 12 and 10 μm, (ii) 12 and 8 μm, and (iii) 10 and 8 μm. By these filtration arrangements, obtained GUVs size distribution were in the ranges of 6−26 μm, 5–38 μm and 5–30 μm, respectively. In comparison, the size distribution range was much higher for single filtration technique, for example, 6−59 μm GUVs found for a membrane with 12 μm pores. Using this technique, the water-soluble fluorescent probe, calcein, can be removed from the suspension of GUVs successfully. The size distributions were analyzed with lognormal distribution. The skewness became smaller (narrow size distribution) when a dual filtration was used instead of single filtration. The mode of the size distribution obtained in dual filtration was also smaller to that of single filtration. By continuing this process of purification for a second time, the GUVs size distribution became even narrower. After using an extra filtration with dual filtration, two different size distributions of GUVs were obtained at a time. This experimental observation suggests that different size specific distributions of GUVs can be obtained easily, even if GUVs are prepared by different other methods.
Md. Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Md. Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Mohammad Monir Uddin, Amith Khandakar, Zaid Bin Mahbub, and Sawal Hamid Md. Ali
Institute of Electrical and Electronics Engineers (IEEE)
Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal.
Raiyan Chowdhury, Ehtesamul Azim, Mohammad Abu Sayem Karal, Asiful Islam, and Zaid Bin Mahbub
IEEE
Molecular Dynamics (MD) is extensively used for analyzing the physical movements of atoms and molecules. It gives the dynamic ’evolution’ of the system for a fixed period of time. In simulations, GUV (Giant Unilamellar Vesicle) is used as a model of biological cell. The dependence of saturation inside the GUV on size, pore width etc. has been investigated in this work. Fluorescent chemicals are used as probing agents to observe the diffusion in GUV. As a completely different approach, we introduced diffusion weighted (DW) magnetic resonance imaging (MRI) in MD. When a nanopore forms on the membrane of a GUV, molecular diffusion between the intra and extra cellular regions takes place. The type and nature of diffusion were approximated from diffusion coefficient measurement using model MRI sequences and compared with the recognized values. These investigations may contribute to direct estimation of structural and biophysical parameters within cellular domain and help to understand underlying physiology.
Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammad Abu Sayem Karal, M A Masud, and Zaid Bin Mahbub
IEEE
A new approach to non-invasively uncover the pattern of myelinated Aα motor nerve fiber conduction velocity distribution (CVD) from the corresponding compound muscle action potential (CMAP), also known as the inverse problem of motor nerve conduction study, has been explored in this work. The previous works to solve this type of problem were mostly in the discrete manner. We leveraged a continuous approach to exploit the gradient optimization technique to solve this problem. A diphasic sinusoidal function was taken to model the motor unit action potential (MUAP) signal and a 5th order polynomial function was taken to model the assumed continuous CVD curve. The continuous CVD instead of a discrete CVD helps us to perform more computation. The inverse results derived using the proposed methodology closely matched (almost 100%) the predicted CVD with the original CVD of different shapes from the corresponding simulated CMAP data using the forward solution of nerve conduction.
Mohammad Abu Sayem Karal, Shareef Ahammed, Victor Levadny, Marina Belaya, Md. Kabir Ahamed, Marzuk Ahmed, Zaid Bin Mahbub, and A.K.M. Atique Ullah
Elsevier BV
Mohammad Abu Sayem Karal, Md. Kabir Ahamed, Marzuk Ahmed, Shareef Ahamed, and Zaid Bin Mahbub
Springer Science and Business Media LLC
Moajjem Hossain Chowdhury, Md Nazmul Islam Shuzan, Muhammad E.H. Chowdhury, Zaid B. Mahbub, M. Monir Uddin, Amith Khandakar, and Mamun Bin Ibne Reaz
MDPI AG
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.
Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Muhammad A. Kadir, and Saad Kashem
MDPI AG
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Mamun Rabbani, Md Enamul Hoque, and Zaid Bin Mahbub
Elsevier
Tawsifur Rahman, Amith Khandakar, Muhammad Abdul Kadir, Khandaker Rejaul Islam, Khandakar F. Islam, Rashid Mazhar, Tahir Hamid, Mohammad Tariqul Islam, Saad Kashem, Zaid Bin Mahbub,et al.
Institute of Electrical and Electronics Engineers (IEEE)
Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.
Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi,et al.
Institute of Electrical and Electronics Engineers (IEEE)
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively. The high accuracy of this computer-aided diagnostic tool can significantly improve the speed and accuracy of COVID-19 diagnosis. This would be extremely useful in this pandemic where disease burden and need for preventive measures are at odds with available resources.
Mohammad Abu Sayem Karal, Md. Kamrul Islam, and Zaid Bin Mahbub
Springer Science and Business Media LLC
Arnold Julian Vinoj Benjamin, Pedro A. Gómez, Mohammad Golbabaee, Zaid Bin Mahbub, Tim Sprenger, Marion I. Menzel, Michael Davies, and Ian Marshall
Elsevier BV
Zaid B. Mahbub, Andrew M. Peters, and Penny A. Gowland
Wiley
This work describes the development of a method to measure the variation of apparent diffusion coefficient (ADC) with diffusion time (Δ) in the brachial plexus, as a potential method of probing microstructure.