Imperial College London

DrMassoudZolgharni

Faculty of MedicineNational Heart & Lung Institute

Honorary Research Associate
 
 
 
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Contact

 

m.zolgharni Website

 
 
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Location

 

ICTEM buildingHammersmith Campus

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Summary

 

Publications

Publication Type
Year
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50 results found

Lane ES, Azarmehr N, Jevsikov J, Howard JP, Shun-shin MJ, Cole GD, Francis DP, Zolgharni Met al., 2021, Multibeat echocardiographic phase detection using deep neural networks, COMPUTERS IN BIOLOGY AND MEDICINE, Vol: 133, ISSN: 0010-4825

Journal article

Howard J, Stowell C, Cole G, Ananthan K, Camelia D, Pearce K, Rajani R, Sehmi J, Vimalesvaran K, Kanaganayagam G, McPhail E, Ghosh A, Chambers J, Singh A, Zolgharni M, Rana B, Francis D, Shun-Shin Met al., 2021, Automated left ventricular dimension assessment using artificial intelligence developed and validated by a UK-wide collaborative, Circulation: Cardiovascular Imaging, Vol: 14, Pages: 405-415, ISSN: 1941-9651

Background: Echocardiography artificial intelligence (AI) requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardisation of such techniques. Methods: The training dataset were 2056individual frames drawn at random from 1265parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015-2016. Nine experts labelled these images using our online platform. From this, we trained a convolutional neural network to identify key points. Subsequently, 13 experts labelled a validation dataset of the end-systolic and end-diastolic frame from100new video-loops, twice each. The 26-opinionconsensus was used as the reference standard. The primary outcome was “precision SD”, the standard deviation of difference between AI measurement and expert consensus. Results: In the validation dataset, the AI’s precision SD for left ventricular internal dimension was 3.5mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4mm. Intraclass correlation coefficient (ICC) between AI and expert consensus was 0.926 (95% CI 0.904–0.944), compared with 0.817 (0.778–0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8mm for AI (ICC 0.809; 0.729–0.967), versus 2.0 for individuals (ICC 0.641; 0.568–0.716). For posterior wall thickness, precision SD was 1.4mm for AI (ICC 0.535; 95% CI 0.379–0.661), versus 2.2mm for individuals(0.366; 0.288 to 0.462).We present all images and annotations. This highlights challenging cases, including poor image quality, tapered ventricles, and indistinct boundaries. Conclusions: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiogr

Journal article

Azarmehr N, Ye X, Howard JP, Lane ES, Labs R, Shun-Shin MJ, Cole GD, Bidaut L, Francis DP, Zolgharni Met al., 2021, Neural architecture search of echocardiography view classifiers, JOURNAL OF MEDICAL IMAGING, Vol: 8, ISSN: 2329-4302

Journal article

Labs RB, Zolgharni M, Loo JP, 2021, Echocardiographic Image Quality Assessment Using Deep Neural Networks, Pages: 488-502, ISSN: 0302-9743

Echocardiography image quality assessment is not a trivial issue in transthoracic examination. As the in vivo examination of heart structures gained prominence in cardiac diagnosis, it has been affirmed that accurate diagnosis of the left ventricle functions is hugely dependent on the quality of echo images. Up till now, visual assessment of echo images is highly subjective and requires specific definition under clinical pathologies. While poor-quality images impair quantifications and diagnosis, the inherent variations in echocardiographic image quality standards indicates the complexity faced among different observers and provides apparent evidence for incoherent assessment under clinical trials, especially with less experienced cardiologists. In this research, our aim was to analyse and define specific quality attributes mostly discussed by experts and present a fully trained convolutional neural network model for assessing such quality features objectively. A total of 1,650 anonymized B-Mode images with dissimilar frame lengths were stratified from most popular ultrasound vendors equipment and clinical quality scores were provided for each echo cine by Cardiologists at England's Hammersmith Hospital which fed our multi-stream architecture model. The regression model assesses the quality features for depth-gain, chamber clarity, interventricular (on-Axis) orientation and foreshortening of the left ventricle. Four independent scores are thus displayed on each frame which compares against cardiologists' manually assigned scores to validate the degree of objective accuracy or its absolute errors. Absolute errors were found to be ±0.02 and ±0.12 for model and inter observer variability, respectively. We achieved a computation speed of 0.0095 ms per frame on GeForce 970, with feasibility for 2D/3D real-time deployment. The research outcome establishes the modality for the objective standardization of 2D echocardiographic image quality and provides a

Conference paper

Ghatwary N, Zolgharni M, Janan F, Ye Xet al., 2021, Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 131-142, ISSN: 2168-2194

Journal article

Azarmehr N, Ye X, Howes JD, Docking B, Howard JP, Francis DP, Zolgharni Met al., 2020, An optimisation-based iterative approach for speckle tracking echocardiography, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 58, Pages: 1309-1323, ISSN: 0140-0118

Journal article

Howard JP, Tan J, Shun-Shin MJ, Mahdi D, Nowbar AN, Arnold AD, Ahmad Y, McCartney P, Zolgharni M, Linton NWF, Sutaria N, Rana B, Mayet J, Rueckert D, Cole GD, Francis DPet al., 2020, Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography., J Med Artif Intell, Vol: 3

Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the 'view' (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accuracy may be due to these networks' ability to track the movement of specific structures such as heart valves throughout the cardiac cycle. Finally, we show the accuracies of these new state-of-the-art networks are approaching expert agreement (3.6% discordance), with a similar pattern of discordance between views.

Journal article

Azarmehr N, Ye X, Sacchi S, Howard JP, Francis DP, Zolgharni Met al., 2020, Segmentation of Left Ventricle in 2D Echocardiography Using Deep Learning, Pages: 497-504, ISBN: 9783030393427

The segmentation of Left Ventricle (LV) is currently carried out manually by the experts, and the automation of this process has proved challenging due to the presence of speckle noise and the inherently poor quality of the ultrasound images. This study aims to evaluate the performance of different state-of-the-art Convolutional Neural Network (CNN) segmentation models to segment the LV endocardium in echocardiography images automatically. Those adopted methods include U-Net, SegNet, and fully convolutional DenseNets (FC-DenseNet). The prediction outputs of the models are used to assess the performance of the CNN models by comparing the automated results against the expert annotations (as the gold standard). Results reveal that the U-Net model outperforms other models by achieving an average Dice coefficient of 0.93 ± 0.04, and Hausdorff distance of 4.52 ± 0.90.

Book chapter

Amer A, Ye X, Zolgharni M, Janan Fet al., 2020, ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images, 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 2019-2022, ISSN: 1557-170X

Conference paper

Ghatwary N, Zolgharni M, Ye X, 2019, Early esophageal adenocarcinoma detection using deep learning methods, INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, Vol: 14, Pages: 611-621, ISSN: 1861-6410

Journal article

Ghatwary N, Zolgharni M, Ye X, 2019, GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for Automatic Detection of Esophageal Abnormalities in Endoscopic Images, 10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 89-97, ISSN: 0302-9743

Conference paper

Ghatwary N, Ye X, Zolgharni M, 2019, Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features, IEEE ACCESS, Vol: 7, Pages: 84374-84385, ISSN: 2169-3536

Journal article

Sacchi S, Dhutia N, Shun-Shin MJ, Zolgharni M, Sutaria N, Francis DP, Cole GDet al., 2018, Doppler assessment of aortic stenosis: a 25-operator study demonstrating why reading the peak velocity is superior to velocity time integral, EHJ Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging, Vol: 19, Pages: 1380-1389, ISSN: 2047-2412

Aims Measurements with superior reproducibility are useful clinically and research purposes. Previous reproducibilitystudies of Doppler assessment of aortic stenosis (AS) have compared only a pair of observers and have notexplored the mechanism by which disagreement between operators occurs. Using custom-designed software whichstored operators’ traces, we investigated the reproducibility of peak and velocity time integral (VTI) measurementsacross a much larger group of operators and explored the mechanisms by which disagreement arose. ...................................................................................................................................................................................................Methodsand resultsTwenty-five observers reviewed continuous wave (CW) aortic valve (AV) and pulsed wave (PW) left ventricularoutflow tract (LVOT) Doppler traces from 20 sequential cases of AS in random order. Each operator unknowinglymeasured each peak velocity and VTI twice. VTI tracings were stored for comparison. Measuring the peak is muchmore reproducible than VTI for both PW (coefficient of variation 10.1 vs. 18.0%; P < 0.001) and CW traces (coeffi-cient of variation 4.0 vs. 10.2%; P < 0.001). VTI is inferior because the steep early and late parts of the envelope aredifficult to trace reproducibly. Dimensionless index improves reproducibility because operators tended to consistentlyover-read or under-read on LVOT and AV traces from the same patient (coefficient of variation 9.3 vs.17.1%; P < 0.001). ...................................................................................................................................................................................................Conclusion It is far more reproducible to measure the peak of a Doppler trace than the VTI, a strategy that reduces measurementvariance by approximately six-fold. Peak measurements are superior to VTI because tracing the steep slopesin th

Journal article

Shun-Shin M, Cole G, Dhutia N, Zolgharni M, Francis Det al., 2017, The development of automated methods for the reproducible assessment of aortic stenosis, Publisher: OXFORD UNIV PRESS, Pages: 489-489, ISSN: 0195-668X

Conference paper

Zolgharni M, Negoita M, Dhutia NM, Mielewczik M, Manoharan K, Sohaib SMA, Finegold JA, Sacchi S, Cole GD, Francis DPet al., 2017, Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography, ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES, Vol: 34, Pages: 956-967, ISSN: 0742-2822

Background:Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain.Methods and Results:We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%).Conclusion:An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame.

Journal article

Cole G, Sacchi S, Dhutia N, Shun-Shin M, Zolgharni M, Sutaria N, Francis Det al., 2017, DOPPLER ASSESSMENT OF AORTIC STENOSIS: READING THE PEAK VELOCITY IS SUPERIOR TO VELOCITY TIME INTEGRAL, Annual Conference of the British-Cardiovascular-Society (BCS), Publisher: BMJ PUBLISHING GROUP, Pages: A93-A93, ISSN: 1355-6037

Conference paper

Dhutia NM, Zolgharni M, Mielewczik M, Negoita M, Sacchi S, Manoharan K, Francis DP, Cole GDet al., 2017, Open-source, vendor-independent, automated multi-beat tissue Doppler echocardiography analysis, International Journal of Cardiovascular Imaging, Vol: 33, Pages: 1135-1148, ISSN: 1569-5794

Current guidelines for measuring cardiac function by tissue Doppler recommend using multiple beats, but this has a time cost for human operators. We present an open-source, vendor-independent, drag-and-drop software capable of automating the measurement process. A database of ~8000 tissue Doppler beats (48 patients) from the septal and lateral annuli were analyzed by three expert echocardiographers. We developed an intensity- and gradient-based automated algorithm to measure tissue Doppler velocities. We tested its performance against manual measurements from the expert human operators. Our algorithm showed strong agreement with expert human operators. Performance was indistinguishable from a human operator: for algorithm, mean difference and SDD from the mean of human operators’ estimates 0.48 ± 1.12 cm/s (R2 = 0.82); for the humans individually this was 0.43 ± 1.11 cm/s (R2 = 0.84), −0.88 ± 1.12 cm/s (R2 = 0.84) and 0.41 ± 1.30 cm/s (R2 = 0.78). Agreement between operators and the automated algorithm was preserved when measuring at either the edge or middle of the trace. The algorithm was 10-fold quicker than manual measurements (p < 0.001). This open-source, vendor-independent, drag-and-drop software can make peak velocity measurements from pulsed wave tissue Doppler traces as accurately as human experts. This automation permits rapid, bias-resistant multi-beat analysis from spectral tissue Doppler images.

Journal article

Negoita M, Zolgharni M, Dadkho E, Pernigo M, Mielewczik M, Cole GD, Dhutia NM, Francis DPet al., 2016, Frame rate required for speckle tracking echocardiography: A quantitative clinical study with open-source, vendor-independent software, International Journal of Cardiology, Vol: 218, Pages: 31-36, ISSN: 1874-1754

ObjectivesTo determine the optimal frame rate at which reliable heart walls velocities can be assessed by speckle tracking.BackgroundAssessing left ventricular function with speckle tracking is useful in patient diagnosis but requires a temporal resolution that can follow myocardial motion. In this study we investigated the effect of different frame rates on the accuracy of speckle tracking results, highlighting the temporal resolution where reliable results can be obtained.Material and methods27 patients were scanned at two different frame rates at their resting heart rate. From all acquired loops, lower temporal resolution image sequences were generated by dropping frames, decreasing the frame rate by up to 10-fold.ResultsTissue velocities were estimated by automated speckle tracking. Above 40 frames/s the peak velocity was reliably measured. When frame rate was lower, the inter-frame interval containing the instant of highest velocity also contained lower velocities, and therefore the average velocity in that interval was an underestimate of the clinically desired instantaneous maximum velocity.ConclusionsThe higher the frame rate, the more accurately maximum velocities are identified by speckle tracking, until the frame rate drops below 40 frames/s, beyond which there is little increase in peak velocity. We provide in an online supplement the vendor-independent software we used for automatic speckle-tracked velocity assessment to help others working in this field.

Journal article

Cole GD, Dhutia NM, Shun-Shin MJ, Willson K, Harrison J, Raphael CE, Zolgharni M, Mayet J, Francis DPet al., 2015, Defining the real-world reproducibility of visual grading and visual estimation of left ventricular ejection fraction: impact of image quality, experience and accreditation., International Journal of Cardiovascular Imaging, Vol: 31, Pages: 1303-1314, ISSN: 1569-5794

Left ventricular function can be evaluated by qualitative grading and by eyeball estimation of ejection fraction (EF). We sought to define the reproducibility of these techniques, and how they are affected by image quality, experience and accreditation. Twenty apical four-chamber echocardiographic cine loops (Online Resource 1–20) of varying image quality and left ventricular function were anonymized and presented to 35 operators. Operators were asked to provide (1) a one-phrase grading of global systolic function (2) an “eyeball” EF estimate and (3) an image quality rating on a 0–100 visual analogue scale. Each observer viewed every loop twice unknowingly, a total of 1400 viewings. When grading LV function into five categories, an operator’s chance of agreement with another operator was 50 % and with themself on blinded re-presentation was 68 %. Blinded eyeball LVEF re-estimates by the same operator had standard deviation (SD) of difference of 7.6 EF units, with the SD across operators averaging 8.3 EF units. Image quality, defined as the average of all operators’ assessments, correlated with EF estimate variability (r = −0.616, p < 0.01) and visual grading agreement (r = 0.58, p < 0.01). However, operators’ own single quality assessments were not a useful forewarning of their estimate being an outlier, partly because individual quality assessments had poor within-operator reproducibility (SD of difference 17.8). Reproducibility of visual grading of LV function and LVEF estimation is dependent on image quality, but individuals cannot themselves identify when poor image quality is disrupting their LV function estimate. Clinicians should not assume that patients changing in grade or in visually estimated EF have had a genuine clinical change.

Journal article

Saura Espin D, Caballero Jimenez L, Oliva Sandoval M, Gonzalez Carrillo J, Espinosa Garcia M, Garcia Navarro M, De La Morena G, Van Dyck M, Hulin J, De Kerchove L, Momeni M, Watremez C, Dreyfus J, Durand-Viel G, Cimadevilla C, Brochet E, Vahanian A, Messika-Zeitoun D, Nagy AI, Apor A, Kovacs A, Manouras A, Andrassy P, Merkely B, Adamyan K, Tumasyan L, Chilingaryan A, Tunyan L, Barutcu A, Bekler A, Gazi E, Kirilmaz B, Temiz A, Altun B, Cole GD, Dhutia N, Shun-Shin M, Willson K, Harrison J, Raphael C, Zolgharni M, Mayet J, Francis D, Kosior DA, Szulc M, Wozakowska-Kaplon B, Opolski Get al., 2014, Oral Abstract session: Demanding measurements: why bother? Thursday 4 December 2014, 16:30-18:00Location: Agora., Eur Heart J Cardiovasc Imaging, Vol: 15 Suppl 2, Pages: ii65-ii67

Journal article

Dhutia NM, Cole GD, Zolgharni M, Manisty CH, Willson K, Parker KH, Hughes AD, Francis DPet al., 2014, Automated speckle tracking algorithm to aid on-axis imaging in echocardiography, Journal of Medical Imaging, Vol: 1, ISSN: 2329-4310

Obtaining a "correct" view in echocardiography is a subjective process in which an operator attempts to obtain images conforming to consensus standard views. Real-time objective quantification of image alignment may assist less experienced operators, but no reliable index yet exists. We present a fully automated algorithm for detecting incorrect medial/lateral translation of an ultrasound probe by image analysis. The ability of the algorithm to distinguish optimal from sub-optimal four-chamber images was compared to that of specialists-the current "gold-standard." The orientation assessments produced by the automated algorithm correlated well with consensus visual assessments of the specialists ([Formula: see text]) and compared favourably with the correlation between individual specialists and the consensus, [Formula: see text]. Each individual specialist's assessments were within the consensus of other specialists, [Formula: see text] of the time, and the algorithm's assessments were within the consensus of specialists 85% of the time. The mean discrepancy in probe translation values between individual specialists and their consensus was [Formula: see text], and between the automated algorithm and specialists' consensus was [Formula: see text]. This technology could be incorporated into hardware to provide real-time guidance for image optimisation-a potentially valuable tool both for training and quality control.

Journal article

Dhutia NM, Zolgharni M, Willson K, Cole G, Nowbar AN, Dawson D, Zielke S, Whelan C, Newton J, Mayet J, Manisty CH, Francis DPet al., 2014, Guidance for accurate and consistent tissue Doppler velocity measurement: comparison of echocardiographic methods using a simple vendor-independent method for local validation, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 15, Pages: 817-827, ISSN: 2047-2404

Journal article

Zolgharni M, Dhutia NM, Cole GD, Bahmanyar MR, Jones S, Sohaib SMA, Tai SB, Willson K, Finegold JA, Francis DPet al., 2014, Automated Aortic Doppler Flow Tracing for Reproducible Research and Clinical Measurements, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 33, Pages: 1071-1082, ISSN: 0278-0062

Journal article

Zolgharni M, Dhutia NM, Cole GD, Willson K, Francis DPet al., 2014, Feasibility of using a reliable automated Doppler flow velocity measurements for research and clinical practices, Pages: 90401D-90401D

Conference paper

Dhutia NM, Zolgharni M, Willson K, Cole G, Nowbar AN, Manisty CH, Francis DPet al., 2014, Calibration of echocardiographic tissue doppler velocity, using simple, universally-applicable methods, Pages: 90400G-90400G

Conference paper

Zolgharni M, Griffiths H, Ledger PD, 2010, Frequency-difference MIT imaging of cerebral haemorrhage with a hemispherical coil array: numerical modelling, PHYSIOLOGICAL MEASUREMENT, Vol: 31, Pages: S111-S125, ISSN: 0967-3334

Journal article

Balachandran W, Azimi SM, Ahern J, Zolgharni M, Bahmanyar MR, Slijepcevic Pet al., 2010, MICROFLUIDIC DEVICE

Patent

Zolgharni M, 2010, Magnetic Induction Tomography for Imaging Cerebral Stroke

Thesis dissertation

Griffiths H, Zolgharni M, Ledger PD, Watson Set al., 2010, The cardiff Mk2b MIT head array: Optimising the coil configuration, Pages: 012046-012046

Conference paper

Zolgharni M, Ledger PD, Griffiths H, 2009, Forward modelling of magnetic induction tomography: a sensitivity study for detecting haemorrhagic cerebral stroke, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 47, Pages: 1301-1313, ISSN: 0140-0118

Journal article

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