Figure - uploaded by Ye Li
Content may be subject to copyright.
Source publication
Purpose
The objective of this study was to evaluate the image degrading factors in quantitative ¹⁷⁷Lu SPECT imaging when using both main gamma photopeak energies.
Methods
Phantom measurements with two different vials containing various calibrated activities in air or water were performed to derive a mean calibration factor (CF) for large and small...
Contexts in source publication
Context 1
... SAAM II software package (The Epsilon Group, Charlottesville, VA, USA) was used to obtain parameters values and their standard deviation by fitting Eq. 1 to the data [21]. The data were binned into 10-min increments for fitting (Table 1). ...Similar publications
Background
Microvascular dysfunction in patients with non-obstructive epicardial coronary may aggravate patient’s symptoms or lead to various clinical events.
Objective
To investigate the correlation between dynamic single photon emission computed tomography (D-SPECT) derived coronary flow reserve (CFR) and TIMI frame count (TFC) in patients with...
In myocardial gated SPECT imaging each cardiac cycle is divided into 8 or 16 temporal frames and the cause of the difference between 8 and 16 frames is not specified exactly. The aim of this study was to investigate the effect of myocardial detector counts and gender on the difference between 8 and 16 frames and also to compare the LVEF obtained by...
Introduction:
In lutetium-177 (Lu-177) single-photon emission computed tomography (SPECT) imaging, the accuracy of activity quantification is degraded by penetrated and scattered photons. We assessed the scattered photon fractions in order to determine the optimal situation and development of correction method. This study proposes to compare the i...
Purpose of the Study: Scattered gamma photons in single photon emission computed tomography (SPECT) is one of the main issues that degrade the image quality. There are several types of scatter correction methods. However, none of the technique is applied in routine clinical Tc-99m SPECT imaging. Therefore, Zinc (Zn) material filter was constructed...
Citations
... In the case of 177 Lutetium, the imaging protocol, the collimator, the imaged gamma-ray energy, and energy window settings have been thoroughly investigated using Monte Carlo (MC) simulations (127). The 208 keV photopeak should be used with a medium energy (ME) collimator and a 20% energy window setting (49, 56, 67, 118,127,[139][140][141][142][143][144]. Due to the low gamma-ray emission of 177 Lutetium, the effects of dead time are minimal and imaging can commence immediately after therapeutic activity values are administered (145). ...
Radiopharmaceutical therapy has been widely adopted owing primarily to the development of novel radiopharmaceuticals. To fully utilize the potential of these RPTs in the era of precision medicine, therapy must be optimized to the patient's tumor characteristics. The vastly disparate dosimetry methodologies need to be harmonized as the first step towards this. Multiple factors play a crucial role in the shift from empirical activity administration to patient-specific dosimetry-based administrations from RPT. Factors such as variable responses seen in patients with presumably similar clinical characteristics underscore the need to standardize and validate dosimetry calculations. These efforts combined with ongoing initiatives to streamline the dosimetry process facilitate the implementation of radiomolecular precision oncology. However, various challenges hinder the widespread adoption of personalized dosimetry-based activity administration, particularly when compared to the more convenient and resource-efficient approach of empiric activity administration. This review outlines the fundamental principles, procedures, and methodologies related to image activity quantification and dosimetry with a specific focus on ¹⁷⁷ Lutetium-based radiopharmaceuticals.
... 26 suggests the application of the TEW method with the use of LE collimators and a 113 keV photopeak or ME collimator for a 208 keV photopeak only or an ME collimator for both photopeaks [28]. However, later research [35][36][37] with Monte Carlo simulations revealed that the use of the TEW technique in 177 Lu therapy applications may lead to under-or over-estimates of activity and should be applied with caution. What is more, the TEW method is noisy because of the narrow window used; thus, noise reduction and low-pass filtering must be used. ...
The aim of this article is to review the single photon emission computed tomography (SPECT) segmentation methods used in patient-specific dosimetry of 177Lu molecular therapy. Notably, 177Lu-labelled radiopharmaceuticals are currently used in molecular therapy of metastatic neuroendocrine tumours (ligands for somatostatin receptors) and metastatic prostate adenocarcinomas (PSMA ligands). The proper segmentation of the organs at risk and tumours in targeted radionuclide therapy is an important part of the optimisation process of internal patient dosimetry in this kind of therapy. Because this is the first step in dosimetry assessments, on which further dose calculations are based, it is important to know the level of uncertainty that is associated with this part of the analysis. However, the robust quantification of SPECT images, which would ensure accurate dosimetry assessments, is very hard to achieve due to the intrinsic features of this device. In this article, papers on this topic were collected and reviewed to weigh up the advantages and disadvantages of the segmentation methods used in clinical practice. Degrading factors of SPECT images were also studied to assess their impact on the quantification of 177Lu therapy images. Our review of the recent literature gives an insight into this important topic. However, based on the PubMed and IEEE databases, only a few papers investigating segmentation methods in 177Lumolecular therapy were found. Although segmentation is an important step in internal dose calculations, this subject has been relatively lightly investigated for SPECT systems. This is mostly due to the inner features of SPECT. What is more, even when studies are conducted, they usually utilise the diagnostic radionuclide 99mTc and not a therapeutic one like 177Lu, which could be of concern regarding SPECT camera performance and its overall outcome on dosimetry.
... Predictions were processed using machine learning, namely MLR is a statistical algorithm that aims to determine the predictive results of the y variable. Y is the prediction result of each X input [49,50]. The general formula for the MLR algorithm is stated in formula 5. ...
Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.
... However, although singleenergy photon emitters have been investigated extensively, multi-energy photon emit- 111 ters, such as In-SRS, have not had their quantitative accuracy validated. In quantitative analysis, improving accuracy is important in order to correct for physical phenomena such as resolution recovery, attenuation and scatter [12][13]. In particular, scatter has a 111 large e ect when multi-energy photon emitters are used, such as In at 171 and 245keV. ...
Objective:
The aim of this study was to validate the optimal scatter correction method and scatter estimation window setting in terms of image quality and quantitative accuracy for quantitative indium-111 (111In)-pentetreotide SPECT imaging.
Materials and methods:
We used a positron emission tomography/computed tomography (PET/CT) phantom to validate image quality and quantitative accuracy, and the SPECT images were acquired by the multi-energy window (MEW) method. The scatter estimation was performed using four kinds of energy windows (MEW1, MEW2, MEW3, and MEW4). Scatter correction was also performed using a dual-energy window (DEW) for comparison with MEWs. Image quality was assessed using percent contrast (% contrast) and background variability, and quantitative accuracy was assessed using the mean standardized uptake value (SUVmean) with hot spheres.
Results:
In the quantification, all MEW settings approached the theoretical SUVmean (MEW1, 0.99±0.06; MEW2, 0.99±0.05; MEW3, 1.00±0.08; MEW4, 0.97±0.12) in contrast to DEW (0.88±0.05). The SUVmean value for scatter correction of both photopeaks for a 28 mm sphere showed the smallest difference from the theoretical value.
Conclusion:
The scatter correction method that gave optimal image quality and quantitative accuracy was MEW3 with two 20% energy windows (one over each photopeak) and four adjacent 3% scatter estimation windows (one on each side of the two photopeaks).
... Initially, R Po of the reconstructed SPECT images was computed using the entire volume of the phantom. However, particularly in the presence of a large volume of nonradioactive attenuating material, such as when using the CTDI phantoms, excess scattered counts may be ineffectively eliminated with DEW or TEW scatter correction techniques [20,26]. In an attempt to compensate for this, the following segmentation techniques were applied to compute R Po : ...
... Our comprehensive study with a variety of phantoms shows that CF and τ derived from tomographic or planar acquisitions tend to converge when segmentation is applied to tomographic data. Indeed, TEW or DEW scatter correction is less effective to get rid of the background counts in areas of dense, non-radioactive medium, leading to overestimated sensitivity when background noise is not excluded by mean of segmentation [17,20,26,31,32]. We have a preference for the threshold-based segmentation method because the SPECTderived CF tended to be more consistent among the various phantom geometries, as well as with the planar CF. ...
Background:
Personalization of 177Lu-based radionuclide therapy requires implementation of dosimetry methods that are both accurate and practical enough for routine clinical use. Quantitative single-photon emission computed tomography/computed tomography (QSPECT/CT) is the preferred scanning modality to achieve this and necessitates characterizing the response of the camera, and calibrating it, over the full range of therapeutic activities and system capacity. Various methods to determine the camera calibration factor (CF) and the deadtime constant (τ) were investigated, with the aim to design a simple and robust protocol for quantitative 177Lu imaging.
Methods:
The SPECT/CT camera was equipped with a medium energy collimator. Multiple phantoms were used to reproduce various attenuation conditions: rod sources in air or water-equivalent media, as well as a Jaszczak phantom with inserts. Planar and tomographic images of a wide range of activities were acquired, with multiple energy windows for scatter correction (double or triple energy window technique) as well as count rate monitoring over a large spectrum of energy. Dead time was modelled using the paralysable model. CF and τ were deduced by curve fitting either separately in two steps (CF determined first using a subset of low-activity acquisitions, then τ determined using the full range of activity) or at once (both CF and τ determined using the full range of activity). Total or segmented activity in the SPECT field of view was computed. Finally, these methods were compared in terms of accuracy to recover the known activity, in particular when planar-derived parameters were applied to the SPECT data.
Results:
The SPECT camera was shown to operate as expected on a finite count rate range (up to ~ 350 kcps over the entire energy spectrum). CF and τ from planar (sources in air) and SPECT segmented Jaszczak data yielded a very good agreement (CF < 1% and τ < 3%). Determining CF and τ from a single curve fit made dead-time-corrected images less prone to overestimating recovered activity. Using triple-energy window scatter correction while acquiring one or more additional energy window(s) to enable wide-spectrum count rate monitoring (i.e. ranging 55-250 or 18-680 keV) yielded the most consistent results across the various geometries. The final, planar-derived calibration parameters for our system were a CF of 9.36 ± 0.01 cps/MBq and a τ of 0.550 ± 0.003 μs. Using the latter, the activity in a Jaszczak phantom could be quantified by QSPECT with an accuracy of 0.02 ± 1.10%.
Conclusions:
Serial planar acquisitions of sources in air using an activity range covering the full operational capacity of the SPECT/CT system, with multiple energy windows for wide-spectrum count rate monitoring, and followed by simultaneous determination of CF and τ using a single equation derived from the paralysable model, constitutes a practical method to enable accurate dead-time-corrected QSPECT imaging in a post-177Lu radionuclide therapy setting.
Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a 100× shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">177</sup>
Lu SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing.
Implementation of dosimetry calculations in the daily practice of Nuclear Medicine Departments is, at this time, a controversial issue, partly due to the lack of a standardized methodology that is accepted by all interested parties (patients, nuclear medicine physicians and medical physicists). However, since the publication of RD 601/2019 there is a legal obligation to implement it, despite the fact that it is a complex and high resource consumption procedure. The aim of this article is to review the theoretical bases of in vivo dosimetry in treatments with ¹⁷⁷Lu-DOTATATE. The exposed methodology is the one proposed by the MIRD Committee (Medical Internal Radiation Dose) of the SNMMI (Society of Nuclear Medicine & Molecular Imaging). According to this method, the absorbed dose is obtained as the product of 2 factors: the time-integrated activity of the radiopharmaceutical present in a source region and a geometrical factor S. This approach, which a priori seems simple, in practice requires several SPECT/CT acquisitions, several measurements of the whole body activity and taking several blood samples, as well as hours of image processing and computation. The systematic implementation of these calculations, in all the patients we treat, will allow us to obtain homogeneous data to correlate the absorbed doses in the lesions with the biological effect of the treatment. The final purpose of the dosimetry calculations is to be able to maximize the therapeutic effect in the lesions, controlling the radiotoxicity in the organs at risk.
Objective
To investigate the value of integrin α vβ 3 targeted microPET/CT imaging with ⁶⁸Ga-NODAGA-RGD 2 as radiotracer for the detection of osteosarcoma and theranostics of osteosarcoma lung metastasis.
Methods
The ⁶⁸Ga-NODAGA-RGD 2 and ¹⁷⁷Lu-NODAGA-RGD 2 were prepared via one-step method and their stability and integrin α vβ 3 binding specificity were investigated in vitro. Forty-one nude mice were injected with human MG63 osteosarcoma to established the animal model bearing subcutaneous osteosarcoma ( n=21), osteosarcoma in tibia ( n=5), and osteosarcoma pulmonary metastatic ( n=15). The microPET-CT imaging was carried out in 3 animal models at 1 hour after tail vein injection of ⁶⁸Ga-NODAGA-RGD 2. Biodistribution study of ⁶⁸Ga-NODAGA-RGD 2 was performed in animal model bearing subcutaneous osteosarcoma at 10, 60, and 120 minutes. The animal model bearing pulmonary metastatic osteosarcoma was injected with ¹⁷⁷Lu-NODAGA-RGD 2 at 7 weeks after model establishment to observe the therapeutic effect of pulmonary metastatic osteosarcoma. Histological and immunohistochemistry examinations were also done to confirm the establishment of animal model and integrin β 3 expression in animal models bearing subcutaneous osteosarcoma and bearing pulmonary metastatic osteosarcoma.
Results
⁶⁸Ga-NODAGA-RGD 2 and ¹⁷⁷Lu-NODAGA-RGD 2 had good stability in vitro with the 50% inhibitory concentration value of (5.0±1.1) and (6.5±0.8) nmol/L, respectively. The radiochemical purity of ⁶⁸Ga-NODAGA-RGD 2 at 1, 4, and 8 hours was 98.5%±0.3%, 98.3%±0.5%, and 97.9%±0.4%; while the radiochemical purity of ¹⁷⁷Lu-NODAGA-RGD 2 at 1, 7, and 14 days was 99.3%±0.7%, 98.7%±1.2%, and 96.0%±2.8%. ⁶⁸Ga-NODAGA-RGD 2 microPET-CT showed that the accumulation of ⁶⁸Ga-NODAGA-RGD 2 in animal models bearing subcutaneous osteosarcoma and osteosarcoma in tibia and in lung metastasis as small as 1-2 mm in diameter of animal model bearing pulmonary metastatic osteosarcoma. Biodistribution study of ⁶⁸Ga-NODAGA-RGD 2 in animal model bearing subcutaneous osteosarcoma revealed rapid clearance from blood with tumor peak uptake of (3.85±0.84) %ID/g at 120 minutes. The distribution of ¹⁷⁷Lu-NODAGA-RGD 2 in lung metastasis was similar with ⁶⁸Ga-NODAGA-RGD 2. The number and size of osteosarcoma metastasis decreased at 2 weeks after ¹⁷⁷Lu-NODAGA-RGD 2 administration and integrin targeting specificity was confirmed by pathology examination.
Conclusion
⁶⁸Ga-NODAGA-RGD 2 was potential for positive imaging and early detection of osteosarcoma and metastasis. Targeted radiotherapy with ¹⁷⁷Lu-NODAGA-RGD 2 was one potential alternative for osteosarcoma lung metastasis.
