Artificial Intelligence in Vascular-PET

Paravastu, Sriram; Theng, Elizabeth; Morris, Michael; Grayson, Peter C.; Collins, Michael T.; Maass‐Moreno, Roberto; Piri, Reza; Gerke, Oke; Alavi, Abass; Høilund‐Carlsen, Poul Flemming; Edenbrandt, Lars; Saboury, Babak

doi:10.1016/j.cpet.2021.09.003

Cited by 7 publications

(6 citation statements)

References 51 publications

(12 reference statements)

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“…Opportunities for clinical use of AI in nuclear medicine practice were extensively reviewed recently, including brain imaging ( 45 ), head and neck imaging ( 46 ), lung imaging ( 47 ), cardiac imaging ( 48 , 49 ), vascular imaging ( 49 , 50 ), bone imaging ( 51 ), prostate imaging ( 52 ), and imaging of lymphoma ( 53 ). Neuroendocrine tumors, other cancers (including gastrointestinal, pancreatic, hepatobiliary, sarcoma, and hereditary), infection, and inflammation are some examples of additional areas requiring further consideration.…”

Section: Opportunitiesmentioning

confidence: 99%

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Saboury

Bradshaw

Boellaard³

et al. 2022

J Nucl Med

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Section: Opportunitiesmentioning

confidence: 99%

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Saboury

Bradshaw

Boellaard³

et al. 2022

J Nucl Med

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“…Multimodality imaging, such as positron emission tomography (PET) and computed tomography (CT) of these diseases, allows physicians to observe molecular information PET in an anatomical context CT. PET imaging allows for the imaging of functional alterations, which frequently arise before the structural modifications that can be detected via CT imaging. 2 Multiple studies have demonstrated potential advantages of hybrid PET/CT imaging in addition to conventional clinical inflammatory and infectious biomarkers. 2-Deoxy-2-[ 18 F]fluoro-D-glucose (2-[ 18 F]FDG) PET/CT is recommended for diagnosis of large vessel vasculitis and polymyalgia rheumatica and has higher diagnostic accuracy than conventional CT alone.…”

Section: Introductionmentioning

confidence: 99%

“…(Semi-)automated organ segmentation reached clinical maturity, but most of the vascular software tools currently focus on diagnostic (contrast-enhanced) CT or magnetic resonance angiography. 2 This is due to the high image quality of these techniques and typical use of intravascular contrast agents, contrary to low-dose CT (LDCT) as often utilized in PET/CT. Recently, various groups have published segmentation models for (PET/)CT including the aorta.…”

Section: Introductionmentioning

confidence: 99%

“…Non‐invasive imaging techniques play a central role in the identification, stratification, and follow‐up of infectious and inflammatory vascular disease. Multimodality imaging, such as positron emission tomography (PET) and computed tomography (CT) of these diseases, allows physicians to observe molecular information PET in an anatomical context CT. PET imaging allows for the imaging of functional alterations, which frequently arise before the structural modifications that can be detected via CT imaging 2 …”

Section: Introductionmentioning

confidence: 99%

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Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT–SEQUOIA

van Praagh,

Nienhuis,

Reijrink

et al. 2024

Medical Physics

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BackgroundCardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time‐consuming and labor‐intensive.PurposeTo investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low‐dose computed tomography (LDCT) as an anatomical reference for PET‐assessed vascular disease.MethodsA software pipeline was developed including automated segmentation using a 3D U‐Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U‐Net, 352 non‐contrast LDCTs from (2‐[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold‐out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values.ResultsFully automated segmentation of the aorta using a 3D U‐Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6–1.4] mm, similar to an open‐source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0–1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00–1.00] and 0.99 [0.93–1.00] for calcium and uptake values, respectively).ConclusionsWe present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG‐CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring.

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“…[ 29 - 31 ] Deep learning and CNNs have been preliminarily explored to detect large vessel abnormalities in PET of patients with LVV and may also help to distinguish the pathologies of LVV and atherosclerosis. [ 32 ]…”

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confidence: 99%

What does artificial intelligence mean in rheumatology?

Chandwar,

Prasanna Misra

2024

Arch Rheumatol

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Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician’s diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

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Artificial Intelligence in Vascular-PET

Cited by 7 publications

References 51 publications

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT–SEQUOIA

What does artificial intelligence mean in rheumatology?

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