TY - GEN
T1 - Using PET to determine the ath-erosclerotic burden in the cardio-vascular system
AU - Piri, Reza
PY - 2022/5/31
Y1 - 2022/5/31
N2 - Introduction: Ischemic heart disease and stroke are the world’s number one and two killers. The underlying
cause is usually atherosclerosis, which may stay asymptomatic for years and is usually diagnosed late in the
course due to a complication or during a health check using common types of imaging such as ultrasound with
Doppler, computed tomography (CT) or magnetic resonance imaging, all of which can also determine if a
stenosis is present. Recent reports suggest that very early changes in the artery wall can be detected and measured by positron emission tomography (PET) imaging with tracers 18F-fluorodeoxyglucose (FDG) or 18F-sodium fluoride (NaF). In this PhD project, we initially wanted to study atherosclerosis in the carotids by PET
imaging, but we soon changed our focus to also include the heart and aorta and the use of AI to segment the
targeted structures in order to elucidate if this could make segmentation and quantification faster and more
reliable. Thus, the project ended comprising four studies published in four articles. Article I was a systematic
review on PET imaging of carotid atherosclerosis, emphasizing clinical usefulness and relations to conventional imaging modalities. Article II was a study of 2-year changes in carotid and aortic NaF uptake in healthy
individuals and angina patients using conventional manual segmentation. Article III was an attempt to establish
and test an automated AI-based method for fast segmentation of the heart in NaF-PET/CT scans, while Article
IV was an attempt to do the same in the aorta.Methods: In the systematic review, articles on carotid artery PET imaging with different radiotracers were
searched in several databases. Duplicates, editorials, case stories, studies regarding feasibility or reproducibility of PET imaging, and studies on patients with end-stage diseases or receiving immunosuppressive medication were omitted. All eligible articles were reviewed by one observer. In the cohort study using manual segmentation only, 29 healthy subjects and 20 angina pectoris patients underwent NaF-PET/CT twice two years
apart. The arch, thoracic, and abdominal aorta and the carotids were manually segmented. NaF uptake was
expressed as the maximum, mean and total standardized uptake values without and with partial volume correction (SUVmax, SUVmean, SUVtotal and cSUVmean, cSUVtotal). Subsequently, a convolutional neural
network (CNN) based method was developed to identify and segment the heart and the aorta in three mentioned parts. The CNN model was trained in NaF- PET/CT scans of other patients and tested in the same 49 subjects
as above by comparison with data obtained by manual segmentation. Bland-Altman limits of agreement were
used to compare derived parameters. Furthermore, the reproducibility of the manual method was examined by
repeated segmentation in 25 randomly selected scans.Results: In the systematic review, it was shown that patients with symptomatic carotid atherosclerosis have
higher FDG uptake than patients with asymptomatic carotid atherosclerosis. There was a strong correlation
between microcalcification and NaF uptake in symptomatic patients in histopathological assessment, but calcification had a negative correlation with uptake of FDG. In the manual cohort study, NaF uptake was insignificantly higher in the angina group at both time points, with less uptake in the healthy group and slightly
higher uptake in the angina group after two years. NaF uptake at baseline could not predict a change in CT calcification after 2 years. NaF uptake correlated positively with age in all parts of the aorta. CT scan did not
indicate any change in density of major arteries after 2 years of follow-up. In the final part of the project, CNN derived heart segmentation measures were 0% to 4% higher than by the manual method and 0% to 17% lower
than with manual aortic segmentation. However, with CNN-based and manual method the SUVmean values
in both heart and aorta were almost identical. Cardiac and aortic CNN-based segmentation method was much
faster than the manual approach, which had a maximal 0.5% and 6% variation at repeated segmentation of the
heart and the aorta, respectively, compared to a 100 % inborn CNN reproducibility.Conclusion: PET imaging is a newly introduced modality for imaging of atherosclerosis, which is a slow and
variable process in healthy individuals and patients with angina pectoris, albeit with a tendency of slightly
higher NaF uptake in angina patients. Although current technical difficulties such as time-taking image analysis exist, the AI-based models could present values for Volume, SUVmean, SUVmax, and SUVtotal similar
to the manually obtained ones. These AI-based models are observer-independent, highly reproducible and very
fast alternative alternatives for slow manual segmentation. With further training, the AI-based approach may
become the standard for assessing patients with suspected or known atherosclerosis.
AB - Introduction: Ischemic heart disease and stroke are the world’s number one and two killers. The underlying
cause is usually atherosclerosis, which may stay asymptomatic for years and is usually diagnosed late in the
course due to a complication or during a health check using common types of imaging such as ultrasound with
Doppler, computed tomography (CT) or magnetic resonance imaging, all of which can also determine if a
stenosis is present. Recent reports suggest that very early changes in the artery wall can be detected and measured by positron emission tomography (PET) imaging with tracers 18F-fluorodeoxyglucose (FDG) or 18F-sodium fluoride (NaF). In this PhD project, we initially wanted to study atherosclerosis in the carotids by PET
imaging, but we soon changed our focus to also include the heart and aorta and the use of AI to segment the
targeted structures in order to elucidate if this could make segmentation and quantification faster and more
reliable. Thus, the project ended comprising four studies published in four articles. Article I was a systematic
review on PET imaging of carotid atherosclerosis, emphasizing clinical usefulness and relations to conventional imaging modalities. Article II was a study of 2-year changes in carotid and aortic NaF uptake in healthy
individuals and angina patients using conventional manual segmentation. Article III was an attempt to establish
and test an automated AI-based method for fast segmentation of the heart in NaF-PET/CT scans, while Article
IV was an attempt to do the same in the aorta.Methods: In the systematic review, articles on carotid artery PET imaging with different radiotracers were
searched in several databases. Duplicates, editorials, case stories, studies regarding feasibility or reproducibility of PET imaging, and studies on patients with end-stage diseases or receiving immunosuppressive medication were omitted. All eligible articles were reviewed by one observer. In the cohort study using manual segmentation only, 29 healthy subjects and 20 angina pectoris patients underwent NaF-PET/CT twice two years
apart. The arch, thoracic, and abdominal aorta and the carotids were manually segmented. NaF uptake was
expressed as the maximum, mean and total standardized uptake values without and with partial volume correction (SUVmax, SUVmean, SUVtotal and cSUVmean, cSUVtotal). Subsequently, a convolutional neural
network (CNN) based method was developed to identify and segment the heart and the aorta in three mentioned parts. The CNN model was trained in NaF- PET/CT scans of other patients and tested in the same 49 subjects
as above by comparison with data obtained by manual segmentation. Bland-Altman limits of agreement were
used to compare derived parameters. Furthermore, the reproducibility of the manual method was examined by
repeated segmentation in 25 randomly selected scans.Results: In the systematic review, it was shown that patients with symptomatic carotid atherosclerosis have
higher FDG uptake than patients with asymptomatic carotid atherosclerosis. There was a strong correlation
between microcalcification and NaF uptake in symptomatic patients in histopathological assessment, but calcification had a negative correlation with uptake of FDG. In the manual cohort study, NaF uptake was insignificantly higher in the angina group at both time points, with less uptake in the healthy group and slightly
higher uptake in the angina group after two years. NaF uptake at baseline could not predict a change in CT calcification after 2 years. NaF uptake correlated positively with age in all parts of the aorta. CT scan did not
indicate any change in density of major arteries after 2 years of follow-up. In the final part of the project, CNN derived heart segmentation measures were 0% to 4% higher than by the manual method and 0% to 17% lower
than with manual aortic segmentation. However, with CNN-based and manual method the SUVmean values
in both heart and aorta were almost identical. Cardiac and aortic CNN-based segmentation method was much
faster than the manual approach, which had a maximal 0.5% and 6% variation at repeated segmentation of the
heart and the aorta, respectively, compared to a 100 % inborn CNN reproducibility.Conclusion: PET imaging is a newly introduced modality for imaging of atherosclerosis, which is a slow and
variable process in healthy individuals and patients with angina pectoris, albeit with a tendency of slightly
higher NaF uptake in angina patients. Although current technical difficulties such as time-taking image analysis exist, the AI-based models could present values for Volume, SUVmean, SUVmax, and SUVtotal similar
to the manually obtained ones. These AI-based models are observer-independent, highly reproducible and very
fast alternative alternatives for slow manual segmentation. With further training, the AI-based approach may
become the standard for assessing patients with suspected or known atherosclerosis.
U2 - 10.21996/h4m2-8s41
DO - 10.21996/h4m2-8s41
M3 - Ph.D. thesis
PB - Syddansk Universitet. Det Sundhedsvidenskabelige Fakultet
ER -