Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data

Rowe, Steven P.; Solnes, Lilja B.; Yin, Yilong; Kitchen, Grant; Lodge, Martin A.; Karakatsanis, Nicolas; Rahmim, Arman; Pomper, Martin G.; Leal, Jeffrey P.

doi:10.1007/s10278-019-00255-7

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…Compared with other methods where the population‐based input function is used for the entire period of scans, 39 the hybrid approach in this study allows some degree of patient‐specific information in the input function. Based on the patient samples found in this study, the average value of rate constant for the exponential function in the models ( γ ) was found to be −1.38 × 10 −4 (s −1 ) with a standard deviation of 0.34 × 10 −4 (s −1 ).…”

Section: Discussionmentioning

confidence: 99%

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

et al. 2021

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Parametric imaging using the Patlak model has been shown to provide improved lesion detectability and specificity. The Patlak model requires both tissue time-activity curves (TACs) after equilibrium and knowledge of the input function from the start of injection. Therefore, the conventional dynamic scanning protocol typically starts from the radiotracer injection all the way to equilibrium. In this paper, we propose the use of hybrid population-based and model-based input function estimation and evaluate its use for whole-body Patlak analysis, in order to reduce the total scan time and simplify clinical Patlak parametric imaging protocols. Possible quantitative errors caused by the simplified scanning protocol were also analyzed both theoretically and with the use of clinical data. Materials and methods: Clinical data from 24 patients referred for tumor staging were included in this study. The patients underwent a whole-body dynamic PET study, 20 min after FDG injection (0.13 mCi/kg). The proposed whole-body scanning protocol includes 6 passes with 4-5 bed positions, depending on the size of the patient, with 2 min for each bed position. An input function from the literature was selected as the shape of the population-based input function. The descending aorta from the corresponding CT image was segmented and applied on the reconstructed dynamic PET images to acquire an image-based input function, which was later fitted using an exponential model. Due to the late scan time, only the later portion of the input function was available, which was used to scale the population-based input function. The hybrid input function was used to derive the wholebody Patlak images. Assuming a given error in the population-based input function, its influence on the final Patlak images were also derived theoretically and verified using the clinical data sets. Finally, the image quality of the reconstructed Patlak slope image was evaluated by an experienced radiologist in four different aspects: image artifacts, image noise, lesion sharpness, and lesion detectability. Results: It was found that errors in the population-based input function only affect the absolute scale of the Patlak slope image. The induced error is proportional to the percentage area-under-curve (AUC) error in the input function. These findings were also confirmed by numerical analysis. The predicted global scale was in good agreement with results from both image-based Patlak and direct Patlak approach. The fractions of the AUC from the early portion population-based input function were also found to be around 18% of the total AUC of the input function, further limiting the propagation of quantitation error from population-based input function to the final Patlak slope image. The reconstructed Patlak images were also found by the radiologist to provide excellent confidence in lesion detection tasks. Conclusions: We have proposed a simplified whole-body scanning protocol that utilizes both population-based input function and model-based input function. The error fr...

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Section: Discussionmentioning

confidence: 99%

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

et al. 2021

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“…For patient studies, VOI definition was performed using the Imager-4D software [24]. The IF was determined from a spherical VOI of 15 mm diameter centered in the aorta (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Short-duration dynamic FDG PET imaging: Optimization and clinical application

et al. 2020

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We aimed to investigate whether short dynamic PET imaging started at injection, complemented with routine clinical acquisition at 60-min post-injection (static), can achieve reliable kinetic analysis. Methods: Dynamic and static 18F-2-fluoro-2-deoxy-D-glucose (FDG) PET data were generated using realistic simulations to assess uncertainties due to statistical noise as well as bias. Following image reconstructions, kinetic parameters obtained from a 2-tissue-compartmental model (2TCM) were estimated, making use of the static image, and the time duration of dynamic PET data were incrementally shortened. We also investigated, in the first 2-min, different frame sampling rates, towards optimized dynamic PET imaging. Kinetic parameters from shortened dynamic datasets were additionally estimated for 9 patients (15 scans) with liver metastases of colorectal cancer, and were compared with those derived from full dynamic imaging using correlation and Passing-Bablok regression analyses. Results: The results showed that by reduction of dynamic scan times from 60-min to as short as 5-min, while using static data at 60-min post-injection, bias and variability stayed comparable in estimated kinetic parameters. Early frame samplings of 5, 24 and 30 s yielded highest biases compared to other schemes. An early frame sampling of 10 s generally kept both bias and variability to a minimum. In clinical studies, strong correlation (r ≥ 0.97, P < 0.0001) existed between all kinetic parameters in full vs. shortened scan protocols. Conclusions: Shortened 5-min dynamic scan, sampled as 12 × 10 + 6 × 30 s, followed by 3-min static image at 60min post-injection, enables accurate and robust estimation of 2TCM parameters, while enabling generation of SUV estimates.

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“…This pilot work has demonstrated the ability of AI-based workflow using DL-CNNs to specifically identify breast cancer tissue as determined by [ 18 F]FDG avidity in a PET/CT study. The high sensitivity and specificity of the network supports the idea that AI can be trained to recognize specific tissue signatures, both normal and disease, in molecular imaging studies using…”

Section: Discussionmentioning

confidence: 99%

“…For this study we used baseline [ 18 F]FDG PET/CT imaging exams acquired as part of two separate multi-institutional clinical trials of patients with breast cancer. The first was TBCRC 008 (NCT00616967) (14), which examined the rate of complete response when using chemotherapy vs chemotherapy + vorinostat in cases of HER2-negative breast cancer.…”

Section: Pet Imaging Studiesmentioning

confidence: 99%

“…Applications based on artificial intelligence (AI) and deep learning (DL) are rapidly being developed to assist in the detection and characterization of lesions on medical images. In this study, we developed and examined an image-processing workflow that incorporates both traditional image processing with AI technology and utilizes a standards-based approach for disease identification and quantitation to segment and classify tissue within a whole-body [ 18 F]FDG PET/CT study.…”

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Automated lesion detection of breast cancer in [18F] FDG PET/CT using a novel AI-Based workflow

et al. 2022

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Applications based on artificial intelligence (AI) and deep learning (DL) are rapidly being developed to assist in the detection and characterization of lesions on medical images. In this study, we developed and examined an image-processing workflow that incorporates both traditional image processing with AI technology and utilizes a standards-based approach for disease identification and quantitation to segment and classify tissue within a whole-body [18F]FDG PET/CT study.MethodsOne hundred thirty baseline PET/CT studies from two multi-institutional preoperative clinical trials in early-stage breast cancer were semi-automatically segmented using techniques based on PERCIST v1.0 thresholds and the individual segmentations classified as to tissue type by an experienced nuclear medicine physician. These classifications were then used to train a convolutional neural network (CNN) to automatically accomplish the same tasks.ResultsOur CNN-based workflow demonstrated Sensitivity at detecting disease (either primary lesion or lymphadenopathy) of 0.96 (95% CI [0.9, 1.0], 99% CI [0.87,1.00]), Specificity of 1.00 (95% CI [1.0,1.0], 99% CI [1.0,1.0]), DICE score of 0.94 (95% CI [0.89, 0.99], 99% CI [0.86, 1.00]), and Jaccard score of 0.89 (95% CI [0.80, 0.98], 99% CI [0.74, 1.00]).ConclusionThis pilot work has demonstrated the ability of AI-based workflow using DL-CNNs to specifically identify breast cancer tissue as determined by [18F]FDG avidity in a PET/CT study. The high sensitivity and specificity of the network supports the idea that AI can be trained to recognize specific tissue signatures, both normal and disease, in molecular imaging studies using radiopharmaceuticals. Future work will explore the applicability of these techniques to other disease types and alternative radiotracers, as well as explore the accuracy of fully automated and quantitative detection and response assessment.

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Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data

Cited by 5 publications

References 22 publications

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

Short-duration dynamic FDG PET imaging: Optimization and clinical application

Automated lesion detection of breast cancer in [18F] FDG PET/CT using a novel AI-Based workflow

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Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data

Cited by 5 publications

References 22 publications

Simplified protocol for whole‐body Patlak parametric imaging with 18F‐FDG PET/CT: Feasibility and error analysis

Simplified protocol for whole‐body Patlak parametric imaging with 18F‐FDG PET/CT: Feasibility and error analysis

Short-duration dynamic FDG PET imaging: Optimization and clinical application

Automated lesion detection of breast cancer in [18F] FDG PET/CT using a novel AI-Based workflow

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Product

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Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis