Optimization of BMI-Based Images for Overweight and Obese Patients — Implications on Image Quality, Quantification, and Radiation Dose in Whole Body 18F-FDG PET/CT Imaging

Bouchareb, Yassine; Tag, Naima; Sulaiman, Hajir; Al-Riyami, Khulood; Jawa, Zabah Muhammad; Al-Dhuhli, Humoud

doi:10.1007/s13139-023-00795-5

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…[F-18]F-2-fluoro-2-deoxy-D-glucose (F-18 FDG) is the most commonly used PET radiotracer for routine clinical oncologic studies [2]. To improve image quality and quantitative accuracy, several studies have investigated the optimization of various image reconstruction methods and parameters in F-18 FDG PET imaging [3][4][5]. Despite the wide use and utility of F-18, many other isotopes are undergoing development and evaluation for potential clinical use in PET imaging; one notable instance of this is the growing interest in metallic isotopes [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Monsef,

Sheikhzadeh,

Steiner

et al. 2024

Biomed. Phys. Eng. Express

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Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameter β was varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration. Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, various β values (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 seconds. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV). Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasing β, peaking at β = 550. The CNR for all β, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images with β = 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration > 120 seconds, the noise level and CNR were not significantly different from the baseline 240-second list-mode data duration.Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 seconds when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

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Section: Introductionmentioning

confidence: 99%

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Monsef,

Sheikhzadeh,

Steiner

et al. 2024

Biomed. Phys. Eng. Express

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Evaluation of patients’ radiation doses and establishment of institutional diagnostic reference levels in nuclear medicine in Oman

Ebrahim,

Aljabri,

Alhaji

et al. 2024

Radiation Protection Dosimetry

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This study aimed to develop diagnostic reference levels (DRLs) in Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) and Positron Emission Tomography/Computed Tomography (PET/CT) imaging for the most frequent SPECT/CT and PET/CT examinations performed at our institution. A total of 1134 adult patients, who have undergone SPECT/CT and PET/CT scanning over a period of 4 years (2018–2021), were included. The scans consisted of 401 PET/CT and 733 SPECT/CT scans. The CT dosimetry data [CT-dose-index (CTDIvol), dose-length-product (DLP)] and administered activities were collected. The DRLs were calculated for CTDIvol, DLP and administrated activity. The estimated DRLs are given as [median CTDIvol (mGy):median DLP (mGy.cm):median administrated activity (MBq)]: whole body PET/CT: 1.88:175:259; brain PET/CT: 12.9:300:239; cardiac PET/CT: 1.34:32:368; bone SPECT/CT: 2.68:116:763; MPI SPECT/CT (stress-rest): 1.49:52:751-721; parathyroid SPECT/CT: 3.1:126:779; thyroid uptake SPECT: 3.52:147:195; thyroid post-ablation SPECT/CT: 3.85:160:NA. The derived DRLs have allowed careful monitoring of doses delivered to patients and could act as a trigger to investigate doses that systematically exceeds the derived DRLs.

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Detection of Aortic Dissection and Intramural Hematoma in Non-Contrast Chest Computed Tomography Using a You Only Look Once-Based Deep Learning Model

Kim,

Choi

et al. 2024

JCM

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Background/Objectives: Aortic dissection (AD) and aortic intramural hematoma (IMH) are fatal diseases with similar clinical characteristics. Immediate computed tomography (CT) with a contrast medium is required to confirm the presence of AD or IMH. This retrospective study aimed to use CT images to differentiate AD and IMH from normal aorta (NA) using a deep learning algorithm. Methods: A 6-year retrospective study of non-contrast chest CT images was conducted at a university hospital in Seoul, Republic of Korea, from January 2016 to July 2021. The position of the aorta was analyzed in each CT image and categorized as NA, AD, or IMH. The images were divided into training, validation, and test sets in an 8:1:1 ratio. A deep learning model that can differentiate between AD and IMH from NA using non-contrast CT images alone, called YOLO (You Only Look Once) v4, was developed. The YOLOv4 model was used to analyze 8881 non-contrast CT images from 121 patients. Results: The YOLOv4 model can distinguish AD, IMH, and NA from each other simultaneously with a probability of over 92% using non-contrast CT images. Conclusions: This model can help distinguish AD and IMH from NA when applying a contrast agent is challenging.

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Optimization of BMI-Based Images for Overweight and Obese Patients — Implications on Image Quality, Quantification, and Radiation Dose in Whole Body 18F-FDG PET/CT Imaging

Cited by 3 publications

References 27 publications

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Evaluation of patients’ radiation doses and establishment of institutional diagnostic reference levels in nuclear medicine in Oman

Detection of Aortic Dissection and Intramural Hematoma in Non-Contrast Chest Computed Tomography Using a You Only Look Once-Based Deep Learning Model

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