Effective dose Estimation in Whole Body 18F-FDG PET/CT Imaging

Karimipourfard, M; Sina, Sedigheh; Shobeiry, M; Alavi, Mehrosadat; Faghihi, Reza

doi:10.1093/rpd/ncac176

Cited by 2 publications

(6 citation statements)

References 14 publications

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“…Nevertheless, the TAC method, based on an imaging approach, has been performed using static PET with several time-series scans or dynamic PET data acquisition. Providing these conditions has limited the clinical application of TAC based on the imaging approach owing to temporal and economic aspects, and has resulted in less clinical mention 11 . In this context, image-to-image translation is considered a new frontier in the field of deep-learning applications in medical imaging analysis 12 .In the field of medical imaging, a wide spectrum of methods is used to vary and translate the resolved information of organs and tissues between two different modalities or different acquisitions within one modality.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Karimipourfard

Sina²,

Shoshtari

et al. 2023

Nuklearmedizin

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The cumulative activity map estimation are essential tools for patient specific dosimetry with high accuracy, which is estimated using biokinetic models instead of patient dynamic data or the number of static PET scans, owing to economical and time-consuming points of view. In the era of deep learning applications in medicine, the pix-to-pix (p2 p) GAN neural networks play a significant role in image translation between imaging modalities. In this pilot study, we extended the p2 p GAN networks to generate PET images of patients at different times according to a 60 min scan time after the injection of F-18 FDG. In this regard, the study was conducted in two sections: phantom and patient studies. In the phantom study section, the SSIM, PSNR, and MSE metric results of the generated images varied from 0.98–0.99, 31–34 and 1–2 respectively and the fine-tuned Resnet-50 network classified the different timing images with high performance. In the patient study, these values varied from 0.88–0.93, 36–41 and 1.7–2.2, respectively and the classification network classified the generated images in the true group with high accuracy. The results of phantom studies showed high values of evaluation metrics owing to ideal image quality conditions. However, in the patient study, promising results were achieved which showed that the image quality and training data number affected the network performance. This study aims to assess the feasibility of p2 p GAN network application for different timing image generation.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Karimipourfard

Sina²,

Shoshtari

et al. 2023

Nuklearmedizin

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show abstract

“…The bladder in each slice was overestimated owing to the aggregation of the 18F‐FDG radiotracer after injection. The reasons are 1 considerable washout of radionuclide into balder at 60‐min post‐injection, 2 filling the bladder with urine. Therefore, segmentation of the bladder using DL is challenging and inaccurate.…”

Section: Resultsmentioning

confidence: 99%

“…Afterward, the low‐dose CT images were resampled to match the PET images. Owing to the resolution limitations of PET images, three preprocessing procedures were applied 1 : denoising using a median filter with neighborhood size of 1 × 1 × 1 (thresholding at the 20‐percentile level to boost SNR) and the smoothing process using Gaussian kernel with 2 mm sigma for each frame, 19,2 normalization into [0 1] scale, and 3 resampling to a 256 × 256 grid. The regions of interest (ROIs) were delineated on the axial low‐dose CT images for seven organs and two regions: bladder, kidney, stomach, liver, lung, spleen, heart wall, body contour, and bone skeleton regions, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In the era of precision medicine, positron emission tomography (PET) imaging is a vital technique for the acquisition of molecular data, while suffering from inaccurate anatomical information. 1,2 The lack of highresolution structural data and detailed organ localization are challenging in PET imaging, particularly 18F-FDG scans. 3 The challenges have been properly approached by introducing multimodality imaging such as PET/computed tomography (CT) and PET/magnetic resonance (MR) imaging.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of deep learning‐based multiorgan segmentation methods on patient‐specific internal dosimetry in PET/CT imaging: A comparative study

Karimipourfard,

Sina,

Mahani

et al. 2024

J Applied Clin Med Phys

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PurposeAccurate and fast multiorgan segmentation is essential in image‐based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time‐consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks.MethodsThe pre‐trained p2p‐U‐Net‐GAN and HighRes3D architectures were fine‐tuned with PET‐only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL‐assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)‐derived S‐values to be used for internal dosimetry.ResultsA fair comparison with manual low‐dose CT‐aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89–0.93, 0.98–0.99, and 0.87–0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S‐values varied between 0.1% and 6% with a maximum attributed to the stomach.ConclusionThe findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL‐based methods provide automated and fast segmentation of whole‐body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F‐FDG‐PET image segmentation.

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Effective dose Estimation in Whole Body 18F-FDG PET/CT Imaging

Cited by 2 publications

References 14 publications

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Synthesis of Prospective Multiple Time Points F-18 FDG PET Images from a Single Scan Using a Supervised Generative Adversarial Network

Impact of deep learning‐based multiorgan segmentation methods on patient‐specific internal dosimetry in PET/CT imaging: A comparative study

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