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Fulltext Achilles Tendon Rupture: Double the Trouble

Wafiuddin Ahmad, Ahmad Faizal Roslan, Faisal Amir, Khairul Nizam Siron

IIUM Medical Journal Malaysia, 2019;18(102):37-0.
MyJurnal

Achilles tendon is the strongest tendon in the body . Achilles tendon rupture is a debilitating ankle injury especially among the athletes. We would like to highlight an unusual case of acute dual-level injuries of the Achilles tendon. Case report: A 30-year-old footballer presented with left ankle injury during a football tournament. Examination revealed tenderness at posterior left heel, palpable gap at Achilles tendon region and positive Thompson test. Radiological assessment showed dual-level injuries of the Achilles tendon-proximally was a rupture at musculotendinous junction and distally was an avulsion calcaneal fracture. We performed a mini-open approach Achilles tendon reconstruction for this patient. First, we reconstructed the distal avulsion calcaneal fracture using double row anchor sutures technique. Next, we repaired the proximal ruptured Achilles tendon using percutaneous Achilles reconstruction system (PARS) from Arthrex. Postoperatively, patient was put on functional rehabilitation protocol. At present, patient recovers well and regains back the Achilles tendon function. In conclusion, segmental Achilles tendon injury is rare and its surgical treatment can be challenging and must be well-planned.
Fulltext Arthroscopic Treatment of Snapping Scapula Syndrome: A Case Report

Ahmad Faizal Roslan, Wafiuddin Ahmad, Faisal Amir, Khairul Nizam Siron

IIUM Medical Journal Malaysia, 2019;18(102):38-0.
MyJurnal

Snapping scapula syndrome is a condition with audible and palpable grating localized to the superomedial angle of the scapula associated with pain. The etiology is likely secondary to anomalous tissue between scapula and chest wall (e.g. bursitis, hooked superomedial angle scapula, Luschka tubercle, malunited rib/scapula fracture and osteochondroma). Case report: We present a case of a 17year-old gentleman, with chronic pain over bilateral upper scapula associated with grating sound upon shoulders movement. Examination revealed significant audible crepitus on bilateral scapula without restriction of shoulders motion. MRI showed no abnormal finding. An attempt for conservative approach including physical therapy and steroid/local anaesthesia injection has been unsuccessful. We subsequently performed an arthroscopic bursectomy and superomedial angle scapula decompression on the right scapula. There were inflammed bursa with fibrotic tissue and prominent superomedial angle of scapula observed during the surgery. Postoperatively, the symptom over right scapula completely resolved with good patient satisfaction. At present, patient is scheduled for the similar surgery on the remaining symptomatic left scapula. In conclusion, arthroscopic scapulothoracic bursectomy with superomedial angle scapula decompression is a reliable treatment for snapping scapula syndrome with predictably high rates of pain relief, patient satisfaction as well as improvement in functional outcomes.
CT-MRI Dual Information Registration for the Diagnosis of Liver Cancer: A Pilot Study Using Point-based Registration

Rahimi A, Khalil A, Faisal A, Lai KW

Curr Med Imaging, 2021;18(1):61-66.
PMID: 34433403 DOI: 10.2174/1573405617666210825155659

BACKGROUND: Early diagnosis of liver cancer may increase life expectancy. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information.
OBJECTIVE: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities.
METHODS: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively.
RESULTS: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution.
CONCLUSION: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.
Fulltext Multimodality registration of two-dimensional echocardiography and cardiac CT for mitral valve diagnosis and surgical planning

Khalil A, Faisal A, Ng SC, Liew YM, Lai KW

J Med Imaging (Bellingham), 2017 Jul;4(3):037001.
PMID: 28840172 DOI: 10.1117/1.JMI.4.3.037001

A registration method to fuse two-dimensional (2-D) echocardiography images with cardiac computed tomography (CT) volume is presented. The method consists of two major procedures: temporal and spatial registrations. In temporal registration, the echocardiography frames at similar cardiac phases as the CT volume were interpolated based on electrocardiogram signal information, and the noise of the echocardiography image was reduced using the speckle reducing anisotropic diffusion technique. For spatial registration, an intensity-based normalized mutual information method was applied with a pattern search optimization algorithm to produce an interpolated cardiac CT image. The proposed registration framework does not require optical tracking information. Dice coefficient and Hausdorff distance for the left atrium assessments were [Formula: see text] and [Formula: see text], respectively; for left ventricle, they were [Formula: see text] and [Formula: see text], respectively. There was no significant difference in the mitral valve annulus diameter measurement between the manually and automatically registered CT images. The transformation parameters showed small deviations ([Formula: see text] deviation in translation and [Formula: see text] for rotation) between manual and automatic registrations. The proposed method aids the physician in diagnosing mitral valve disease as well as provides surgical guidance during the treatment procedure.
2D to 3D fusion of echocardiography and cardiac CT for TAVR and TAVI image guidance

Khalil A, Faisal A, Lai KW, Ng SC, Liew YM

Med Biol Eng Comput, 2017 Aug;55(8):1317-1326.
PMID: 27830464 DOI: 10.1007/s11517-016-1594-6

This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis "Mercedes Benz" sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.
Knee cartilage segmentation and thickness computation from ultrasound images

Faisal A, Ng SC, Goh SL, Lai KW

Med Biol Eng Comput, 2018 Apr;56(4):657-669.
PMID: 28849317 DOI: 10.1007/s11517-017-1710-2

Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.
Fulltext Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Ashraf N, Asari A, Yousaf N, Ahmad M, Ahmed M, Faisal A, et al.

Front Chem, 2022;10:1003816.
PMID: 36405310 DOI: 10.3389/fchem.2022.1003816

Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.
Fulltext An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Serena Low WC, Chuah JH, Tee CATH, Anis S, Shoaib MA, Faisal A, et al.

Comput Math Methods Med, 2021;2021:5528144.
PMID: 34194535 DOI: 10.1155/2021/5528144

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.