Abstract
Background -Pulmonary embolism (PE) is a treatable potentially life threating condition caused by a thrombotic obstruction to the pulmonary arteries. Undiagnosed PE has high a morbidity and mortality rate worldwide. An accurate and timely diagnosis is essential to guide appropriate management in both acute and chronic settings. Conventionally PE is diagnosed by imaging which is reported in a binary manner stating the presence or absence of PE with a general description of its extent. There may be a role for quantification to help direct patient management in the acute setting and predict the development of long-term complications such as recurrence, impaired exercise tolerance and chronic thromboembolic pulmonary hypertension (CTEPH).Single photon emission computed tomography ventilation perfusion scintigraphy (V/Q SPECT) is the most sensitive method for diagnosing PE in acute and chronic settings that forms the basis of the 2019 EANM guidelines for the evaluation of PE104. V/Q SPECT images can be used to produce three-dimensional quotient and subtraction parametric maps. This project aims to explore V/Q SPECT volumetric quantification that may be utilised to quantify the percentage parenchymal %PE clot burden objectively. This may allow prognostication of outcome to assist in risk stratification of treatment and patient follow up strategies.
Methods Two V/Q SPECT phantoms were adapted to contain physical defects of known volume to represent PE and advanced V/Q SPECT imaging analysis with three-dimensional quotient imaging techniques was utilised to develop, optimise and validate a %PE quantification method(%PE), addressing particularly the processing parameters of the raw SPECT dataset, using the ventilated phantom volume as the denominator. The successful method was then applied to retrospective clinical dual isotope VQ SPECT datasets to establish which of two semiautomated techniques for %PE quantification compared most favourably aligned to a manually derived gold standard.
The optimal technique was then applied to retrospective clinical V/Q SPECT studies including 101 PE positive (41 acute PE and 60 chronic PE) cases and 20 age and sex matched PE negative control V/Q SPECT scans, all of whom had 2 years long term clinical follow up data available. V/Q SPECT datasets were quantified using the optimised %PE method and the %PE parenchymal burden for each patient was compared to their short and long term clinical details including acute PE symptoms and signs, biomarker test results, acute management details, recurrence, long term impaired exercise tolerance and diagnosis of CTEPH and analysed to determine the utility of %PE in patient outcome prediction.
Results – Accurate quantification and clinical validation of %PE were attainable with phantom optimised acquisition and reconstruction protocol and cutoff thresholds using V/Q quotient and ventilation SPECT images.
The %PE ranged from 0 (in the control group) to 58%, with 35 having <5%, 27 having 6-15%, 12 having 16-25%, 15 having 26-40% and 12 having >41%. There was a statistically significant correlation between %PE and right heart dysfunction. The %PE did not show any statistically significant correlation with any other acute clinical feature.
%PE showed a statistically significant correlation in the development of long term impaired exercise tolerance when parenchymal %PE was more than >15% and CTEPH when %PE was >30%; the latter is considered clinically relevant given that CTEPH can develop silently.
Conclusion Successful V/Q SPECT %PE quantification is possible, and a quantitative %PE tool may be a useful addition to standard diagnostic reports to help triage patients into different clinical management groups, particularly for those with a %PE burden of >30% which may result in the development of CTEPH. This study found no correlation between clinical symptoms, signs, or biochemical tests with %PE burden, reiterating the importance of imaging for making the diagnosis. The application of AI to %PE quantification is likely to contribute positively to this process in the future.
| Date of Award | 1 Nov 2024 |
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| Original language | English |
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| Supervisor | Lefteris Liveratos (Supervisor) & Charllotte Fowler (Supervisor) |