Publications
107 results found
Ali N, Arnold AD, Miyazawa AA, et al., 2023, Comparison of methods for delivering cardiac resynchronization therapy: an acute electrical and haemodynamic within-patient comparison of left bundle branch area, His bundle, and biventricular pacing, EP Europace, Pages: 1-8, ISSN: 1099-5129
AimsLeft bundle branch area pacing (LBBAP) is a promising method for delivering cardiac resynchronization therapy (CRT), but its relative physiological effectiveness compared with His bundle pacing (HBP) is unknown. We conducted a within-patient comparison of HBP, LBBAP, and biventricular pacing (BVP).Methods and resultsPatients referred for CRT were recruited. We assessed electrical response using non-invasive mapping, and acute haemodynamic response using a high-precision haemodynamic protocol. Nineteen patients were recruited: 14 male, mean LVEF of 30%. Twelve had time for BVP measurements. All three modalities reduced total ventricular activation time (TVAT), (ΔTVATHBP -43 ± 14 ms and ΔTVATLBBAP −35 ± 20 ms vs. ΔTVATBVP −19 ± 30 ms, P = 0.03 and P = 0.1, respectively). HBP produced a significantly greater reduction in TVAT compared with LBBAP in all 19 patients (−46 ± 15 ms, −36 ± 17 ms, P = 0.03). His bundle pacing and LBBAP reduced left ventricular activation time (LVAT) more than BVP (ΔLVATHBP −43 ± 16 ms, P < 0.01 vs. BVP, ΔLVATLBBAP −45 ± 17 ms, P < 0.01 vs. BVP, ΔLVATBVP −13 ± 36 ms), with no difference between HBP and LBBAP (P = 0.65). Acute systolic blood pressure was increased by all three modalities. In the 12 with BVP, greater improvement was seen with HBP and LBBAP (6.4 ± 3.8 mmHg BVP, 8.1 ± 3.8 mmHg HBP, P = 0.02 vs. BVP and 8.4 ± 8.2 mmHg for LBBAP, P = 0.3 vs. BVP), with no difference between HBP and LBBAP (P = 0.8).ConclusionHBP delivered better ventricular resynchronization than LBBAP because right ventricular activation was slower during LBBAP. But LBBAP was not inferior to HBP with respect to LV electrical resynchronization and acute haemodynamic response.
Kelshiker M, Seligman H, Howard JAMES, et al., 2022, Coronary flow reserve and cardiovascular outcomes: a systematic review and meta-analysis, European Heart Journal, Vol: 43, Pages: 1582-1593, ISSN: 0195-668X
Aims: This meta-analysis aims to quantify the association of reduced coronary flow with all3 cause mortality and major adverse cardiovascular events (MACE) across a broad range of patient groups and pathologies. Methods and Results: We systematically identified all studies between 1st January 2000 and1st August 2020, where coronary flow was measured and clinical outcomes were reported. The endpoints were all-cause mortality and MACE. Estimates of effect were calculated from published hazard ratios using a random-effects model. 79 studies, including 59,740 subjects were included. Abnormal coronary flow reserve (CFR) was associated with a higher incidence of all-cause mortality (HR 3.78, 95% CI 2.39-5.97) and a higher incidence of MACE (HR 3.42, 95% CI 2.92-3.99). Each 0.1-unit reduction in CFR was associated with a proportional increase in mortality (per 0.1 CFR unit HR 1.16, 95% CI 1.04-1.29) and MACE (per 0.1 CFR unit HR 1.08, 95% CI 1.04-1.11)). In patients with isolated coronary microvascular dysfunction, an abnormal CFR was associated with a higher incidence of mortality (HR 5.44, 95% CI 3.78-7.83) and MACE (HR 3.56, 95% CI 2.14-5.90). Abnormal CFR was also associated with a higher incidence of MACE in patients with acute coronary syndromes (HR 3.76, 95% CI 2.35-6.00), heart failure (HR 6.38, 95% CI 1.95-20.90), heart transplant (HR 3.32, 95% CI 2.34-4.71) and diabetes mellitus (HR 7.47, 95% CI 3.37-16.55). Conclusions: Reduced coronary flow is strongly associated with increased risk of all-cause mortality and MACE across a wide range of pathological processes. This finding supports recent recommendations that coronary flow should be measured more routinely in clinical practice to target aggressive vascular risk modification for individuals at higher risk
Seraphim A, Dowsing B, Rathod KS, et al., 2022, Quantitative Myocardial Perfusion Predicts Outcomes in Patients With Prior Surgical Revascularization, JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, Vol: 79, Pages: 1141-1151, ISSN: 0735-1097
- Author Web Link
- Cite
- Citations: 5
Ahmad Y, Kane C, Arnold AD, et al., 2022, Randomized blinded placebo-controlled trials of renal sympathetic denervation for hypertension: a meta-analysis, Cardiovascular Revascularization Medicine, Vol: 34, Pages: 112-118, ISSN: 1553-8389
BackgroundThe efficacy of renal denervation has been controversial, but the procedure has now undergone several placebo-controlled trials. New placebo-controlled trial data has recently emerged, with longer follow-up of one trial and the full report of another trial (which constitutes 27% of the total placebo-controlled trial data). We therefore sought to evaluate the effect of renal denervation on ambulatory and office blood pressures in patients with hypertension.MethodsWe systematically identified all blinded placebo-controlled randomized trials of catheter-based renal denervation for hypertension. The primary efficacy outcome was ambulatory systolic blood pressure change relative to placebo. A random-effects meta-analysis was performed.Results6 studies randomizing 1232 patients were eligible. 713 patients were randomized to renal denervation and 519 to placebo. Renal denervation significantly reduced ambulatory systolic blood pressure (−3.52 mmHg; 95% CI −4.94 to −2.09; p < 0.0001), ambulatory diastolic blood pressure (−1.93 mmHg; 95% CI −3.04 to −0.83, p = 0.0006), office systolic blood pressure size (−5.10 mmHg; 95% CI −7.31 to −2.90, p < 0.0001) and office diastolic pressure (effect size −3.11 mmHg; 95% CI −4.43 to −1.78, p < 0.0001). Adverse events were rare and not more common with denervation.ConclusionsThe totality of blinded, randomized placebo-controlled data shows that renal denervation is safe and provides genuine reduction in blood pressure for at least 6 months post-procedure. If this effect continues in the long term, renal denervation might provide a life-long 10% relative risk reduction in major adverse cardiac events and 7.5% relative risk reduction in all-cause mortality.
Zaman S, Petri C, Vimalesvaran K, et al., 2022, Automatic diagnosis labeling of cardiovascular MRI by using semisupervised natural language processing of text reports, Radiology: Artificial Intelligence, Vol: 4, ISSN: 2638-6100
A semisupervised natural language processing (NLP) algorithm, based on bidirectional transformers, accurately categorized diagnoses from cardiac MRI text of radiology reports for the labeling of MR images; the model had a higher accuracy than traditional NLP models and performed faster labeling than clinicians.
Li Z, Petri C, Howard J, et al., 2022, PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 13593 LNCS, Pages: 359-368, ISSN: 0302-9743
Background: Increased pericardial adipose tissue (PAT) is associated with many types of cardiovascular disease (CVD). Although cardiac magnetic resonance images (CMRI) are often acquired in patients with CVD, there are currently no tools to automatically identify and quantify PAT from CMRI. The aim of this study was to create a neural network to segment PAT from T2-weighted CMRI and explore the correlations between PAT volumes (PATV) and CVD outcomes and mortality. Methods: We trained and tested a deep learning model, PAT-CNN, to segment PAT on T2-weighted cardiac MR images. Using the segmentations from PAT-CNN, we automatically calculated PATV on images from 391 patients. We analysed correlations between PATV and CVD diagnosis and 1-year mortality post-imaging. Results: PAT-CNN was able to accurately segment PAT with Dice score/ Hausdorff distances of 0.74 ± 0.03/27.1 ± 10.9 mm, similar to the values obtained when comparing the segmentations of two independent human observers (0.76 ± 0.06/21.2 ± 10.3 mm). Regression models showed that, independently of sex and body-mass index, PATV is significantly positively correlated with a diagnosis of CVD and with 1-year all cause mortality (p-value < 0.01). Conclusions: PAT-CNN can segment PAT from T2-weighted CMR images automatically and accurately. Increased PATV as measured automatically from CMRI is significantly associated with the presence of CVD and can independently predict 1-year mortality.
Vimalesvaran K, Uslu F, Zaman S, et al., 2022, Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View, Editors: Wang, Dou, Fletcher, Speidel, Li, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 571-580, ISBN: 978-3-031-16430-9
Thornton GD, Shetye A, Knight DS, et al., 2021, Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction, FRONTIERS IN CARDIOVASCULAR MEDICINE, Vol: 8, ISSN: 2297-055X
- Author Web Link
- Cite
- Citations: 5
Kellman P, Xue H, Chow K, et al., 2021, Bright-blood and dark-blood phase sensitive inversion recovery late gadolinium enhancement and T1 and T2 maps in a single free-breathing scan: an all-in-one approach, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 23, ISSN: 1097-6647
- Author Web Link
- Cite
- Citations: 1
Howard J, Zaman S, Francis D, et al., 2021, INTELLIGENT LOCALISERS: AN INTEGRATED TIME-SAVING DEEP LEARNING SOLUTION FOR THE PLANNING OF CINE IMAGING AND IDENTIFICATION OF UNEXPECTED FINDINGS FROM A SINGLE TRANSAXIAL STACK, Annual Meeting of the British-Society-of-Cardiovascular-Magnetic-Resonance (BSCMR), Publisher: BMJ PUBLISHING GROUP, Pages: A5-A6, ISSN: 1355-6037
Stowell C, Howard J, Demetrescu C, et al., 2021, Fully automated global longitudinal strain assessment using artificial intelligence developed and validated by a UK-wide echocardiography expert collaborative, Publisher: OXFORD UNIV PRESS, Pages: 2-2, ISSN: 0195-668X
Bachtiger P, Park S, Letchford E, et al., 2021, Triage-HF plus: 12-month study of remote monitoring pathway for triage of heart failure risk initiated during the Covid-19 pandemic, Publisher: OXFORD UNIV PRESS, Pages: 3082-3082, ISSN: 0195-668X
Stowell C, Howard J, Cole G, et al., 2021, Automated left ventricular dimension assessment using artificial intelligence, Publisher: OXFORD UNIV PRESS, Pages: 1-1, ISSN: 0195-668X
Banerjee M, Chiew D, Patel K, et al., 2021, The impact of artificial intelligence on clinical education: Perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers., BMC Medical Education, Vol: 21, Pages: 1-10, ISSN: 1472-6920
BackgroundArtificial intelligence (AI) technologies are increasingly used in clinical practice. Although there is robust evidence that AI innovations can improve patient care, reduce clinicians’ workload and increase efficiency, their impact on medical training and education remains unclear.MethodsA survey of trainee doctors’ perceived impact of AI technologies on clinical training and education was conducted at UK NHS postgraduate centers in London between October and December 2020. Impact assessment mirrored domains in training curricula such as ‘clinical judgement’, ‘practical skills’ and ‘research and quality improvement skills’. Significance between Likert-type data was analysed using Fisher’s exact test. Response variations between clinical specialities were analysed using k-modes clustering. Free-text responses were analysed by thematic analysis.Results210 doctors responded to the survey (response rate 72%). The majority (58%) perceived an overall positive impact of AI technologies on their training and education. Respondents agreed that AI would reduce clinical workload (62%) and improve research and audit training (68%). Trainees were skeptical that it would improve clinical judgement (46% agree, p=0.12) and practical skills training (32% agree, p<0.01). The majority reported insufficient AI training in their current curricula (92%), and supported having more formal AI training (81%).ConclusionsTrainee doctors have an overall positive perception of AI technologies’ impact on clinical training. There is optimism that it will improve ‘research and quality improvement’ skills and facilitate ‘curriculum mapping’. There is skepticism that it may reduce educational opportunities to develop ‘clinical judgement’ and ‘practical skills’. Medical educators should be mindful that these domains are protected as AI develops. We recommend that ‘Applied AI&r
Kotecha T, Knight DS, Moon JC, et al., 2021, The evolution of cardiovascular COVID-19 research, EUROPEAN HEART JOURNAL, Vol: 42, Pages: 2953-2954, ISSN: 0195-668X
Rajkumar C, Shun-Shin M, Seligman H, et al., 2021, Placebo-controlled efficacy of percutaneous coronary intervention for focal and diffuse patterns of stable coronary artery disease, Circulation: Cardiovascular Interventions, Vol: 14, Pages: 809-818, ISSN: 1941-7640
Background Physiological assessment with pressure wire pullback can characterize coronary artery disease (CAD) with a focal or diffuse pattern. However, the clinical relevance of this distinction is unknown. We use data from ORBITA to test if the pattern of CAD predicts the placebo-controlled efficacy of percutaneous coronary intervention (PCI) on stress echocardiography ischemia and symptom endpoints.Methods164 patients in ORBITA underwent blinded instantaneous wave-free ratio (iFR) pullback assessment prior to randomization. Focal disease was defined as 0.03 iFR unit drop within 15mm, rather than over a longer distance. Analyses were performed using regression modelling. ResultsIn the PCI arm (n=85), 48 were focal and 37 were diffuse. In the placebo arm (n=79), 35 were focal and 44 were diffuse. Focal stenoses were associated with significantly lower fractional flow reserve (FFR) and iFR values than diffusely diseased vessels (focal mean FFR and iFR 0.600.15 and 0.650.24, diffuse 0.780.10 and 0.880.08 respectively, p<0.0001). With adjustment for this difference, PCI for focal stenoses resulted in significantly greater reduction in stress echo ischemia than PCI for diffuse disease (p<0.05). The effect of PCI on between-arm pre-randomization-adjusted exercise time was 9.32 seconds (95% CI, -17.1 to 35.7s; p=0.487). When stratified for pattern of disease, there was no detectable difference between focal and diffuse CAD (Pinteraction=0.700). PCI improved Seattle Angina Questionnaire angina frequency score and freedom from angina more than placebo (p=0.034; p=0.0035). However, there was no evidence of interaction between the physiological pattern of CAD and these effects (Pinteraction=0.436; Pinteraction=0.908).ConclusionPCI achieved significantly greater reduction of stress echocardiography ischemia in focal compared to diffuse CAD. However, for symptom endpoints, no such difference was observed.
Rosmini S, Seraphim A, Knott K, et al., 2021, Non-invasive characterization of pleural and pericardial effusions using T1 mapping by magnetic resonance imaging, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 23, Pages: 1117-1126, ISSN: 2047-2404
- Author Web Link
- Cite
- Citations: 3
Gorecka M, McCann GP, Berry C, et al., 2021, Demographic, multi-morbidity and genetic impact on myocardial involvement and its recovery from COVID-19: protocol design of COVID-HEART-a UK, multicentre, observational study, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 23, ISSN: 1097-6647
- Author Web Link
- Cite
- Citations: 7
Howard J, Stowell C, Cole G, et 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
Kotecha T, Knight DS, Razvi Y, et al., 2021, Patterns of myocardial injury in recovered troponin-positive COVID-19 patients assessed by cardiovascular magnetic resonance, EUROPEAN HEART JOURNAL, Vol: 42, Pages: 1866-1878, ISSN: 0195-668X
- Author Web Link
- Cite
- Citations: 1
Zaman S, Seligman H, Lloyd FH, et al., 2021, Aerosolised fluorescein can quantify FFP mask faceseal leakage: a cost-effective adaptation to the existing point of care fit test, CLINICAL MEDICINE, Vol: 21, Pages: E263-E268, ISSN: 1470-2118
- Author Web Link
- Cite
- Citations: 1
Azarmehr N, Ye X, Howard JP, et al., 2021, Neural architecture search of echocardiography view classifiers, JOURNAL OF MEDICAL IMAGING, Vol: 8, ISSN: 2329-4302
- Author Web Link
- Cite
- Citations: 2
Lane ES, Azarmehr N, Jevsikov J, et al., 2021, Multibeat echocardiographic phase detection using deep neural networks, COMPUTERS IN BIOLOGY AND MEDICINE, Vol: 133, ISSN: 0010-4825
- Author Web Link
- Cite
- Citations: 4
Levy S, Cole G, Pabari P, et al., 2021, New horizons in cardiogeriatrics: geriatricians and heart failure care-the custard in the tart, not the icing on the cake, AGE AND AGEING, Vol: 50, Pages: 1064-1068, ISSN: 0002-0729
Mikhail G, Khawaja SA, Mohan P, et al., 2021, COVID-19 and its impact on the cardiovascular system, Open Heart, Vol: 8, Pages: 1-9, ISSN: 2053-3624
Objectives: The clinical impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has varied across countries with varying cardiovascular manifestations. We review the cardiac presentations, in-hospital outcomes and development of cardiovascular complications in the initial cohort of SARS-CoV-2 positive patients at Imperial College Healthcare NHS Trust, United Kingdom.Methods: We retrospectively analysed 498 COVID-19 positive adult admissions to our institute from 7th March to 7th April 2020. Patient data was collected for baseline demographics, co-morbidities and in-hospital outcomes, especially relating to cardiovascular intervention.Results:Mean age was 67.4±16.1 years and 62.2%(n=310) were male. 64.1%(n=319) of our cohort had underlying cardiovascular disease (CVD) with 53.4%(n=266) having hypertension. 43.2%(n=215) developed acute myocardial injury. Mortality was significantly increased in those patients with myocardial injury (47.4% vs 18.4%,p<0.001). Only 4 COVID-19 patients had invasive coronary angiography,2 underwent percutaneous coronary intervention and 1 required a permanent pacemaker implantation. 7.0%(n=35) of patients had an inpatient echocardiogram. Acute myocardial injury (OR 2.39,1.31-4.40,p=0.005) and history of hypertension (OR 1.88 ,1.01-3.55,p=0.049) approximately doubled the odds of in-hospital mortality in patients admitted with COVID-19 after other variables had been controlled for.Conclusion:Hypertension, pre-existing CVD and acute myocardial injury were associated with increased in-hospital mortality in our cohort of COVID-19 patients. However, only a low number of patients required invasive cardiac intervention.
Sweeney M, Cole GD, Pabari P, et al., 2021, Urinary drug metabolite testing in chronic heart failure patients indicates high levels of adherence with life-prolonging therapies, ESC HEART FAILURE, Vol: 8, Pages: 2334-2337, ISSN: 2055-5822
- Author Web Link
- Cite
- Citations: 1
Cox-Smith A, Cooper T, Punjabi P, et al., 2021, LACK OF EVIDENCE FOR REDUCED EFFICACY OF MEDICAL THERAPY FOR HEART FAILURE IN OLDER ADULTS, Publisher: OXFORD UNIV PRESS, ISSN: 0002-0729
Sivalokanathan S, Foley M, Cole G, et al., 2021, Gastroenteritis and cardiogenic shock in a healthcare worker: a case report of COVID-19 myocarditis confirmed with serology, European Heart Journal: Case Reports, Vol: 5, Pages: 1-5, ISSN: 2514-2119
BackgroundCoronavirus disease 2019 (COVID-19) myocarditis is emerging as a component of the hyperactive inflammatory response secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Isolated gastrointestinal symptoms are uncommon presenting features in adults with COVID-19 myocarditis. The availability of antibody testing is a valuable addition to the confirmation of COVID-19, when repeated reverse transcriptase–polymerase chain reaction of nasopharyngeal swabs are negative.Case summaryA young healthcare worker presented with dizziness and pre-syncope, 4 weeks after his original symptoms that included fever, lethargy, and diarrhoea. Despite 2 weeks of isolation, followed by a quiescent spell, his symptoms had returned. Shortly after, he presented in cardiogenic shock (left ventricular ejection fraction 25%), that required vasopressor support, at the height of the COVID-19 pandemic. Cardiac magnetic resonance imaging suggested florid myocarditis. Three nasopharyngeal swabs (Days 1, 3, and 5) were negative for SARS-CoV-2, but subsequent serology (Day 13) confirmed the presence of SARS-CoV-2 IgG. Treatment with intravenous immunoglobulin and glucocorticoids led to full recovery.DiscussionOur case study highlights the significance of the use of the available serological assays for diagnosis of patients presenting late with SARS-CoV-2. Importantly, it supports further research in the use of immunomodulatory drugs for the hyperinflammatory microenvironment induced by COVID-19.
Naderi H, Robinson S, Swaans MJ, et al., 2021, Adapting the role of handheld echocardiography during the COVID-19 pandemic: A practical guide, PERFUSION-UK, Vol: 36, Pages: 547-558, ISSN: 0267-6591
Howard JP, Zaman S, Ragavan A, et al., 2020, Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition, International Journal of Cardiovascular Imaging, Vol: 37, Pages: 1033-1042, ISSN: 1569-5794
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.