Many spectral CT applications require accurate material decomposition. Existing material decomposition algorithms are often susceptible to significant noise magnification or, in the case of one-step model-based approaches, hampered by slow convergence rates and large computational requirements. In this work, we proposed a novel framework - spectral diffusion posterior sampling (spectral DPS) - for one-step reconstruction and multi-material decomposition, which combines sophisticated prior information captured by one-time unsupervised learning and an arbitrary analytic physical system model. Spectral DPS is built upon a general DPS framework for nonlinear inverse problems. Several strategies developed in previous work, including jumpstart sampling, Jacobian approximation, and multi-step likelihood updates are applied facilitate stable and accurate decompositions. The effectiveness of spectral DPS was evaluated on a simulated dual-layer and a kV-switching spectral system as well as on a physical cone-beam CT (CBCT) test bench. In simulation studies, spectral DPS improved PSNR by 27.49% to 71.93% over baseline DPS and by 26.53% to 57.30% over MBMD, depending on the the region of interest. In physical phantom study, spectral DPS achieved a <1% error in estimating the mean density in a homogeneous region. Compared with baseline DPS, spectral DPS effectively avoided generating false structures in the homogeneous phantom and reduced the variability around edges. Both simulation and physical phantom studies demonstrated the superior performance of spectral DPS for stable and accurate material decomposition.
{"title":"Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling","authors":"Xiao Jiang, Grace J. Gang, J. Webster Stayman","doi":"arxiv-2408.01519","DOIUrl":"https://doi.org/arxiv-2408.01519","url":null,"abstract":"Many spectral CT applications require accurate material decomposition.\u0000Existing material decomposition algorithms are often susceptible to significant\u0000noise magnification or, in the case of one-step model-based approaches,\u0000hampered by slow convergence rates and large computational requirements. In\u0000this work, we proposed a novel framework - spectral diffusion posterior\u0000sampling (spectral DPS) - for one-step reconstruction and multi-material\u0000decomposition, which combines sophisticated prior information captured by\u0000one-time unsupervised learning and an arbitrary analytic physical system model.\u0000Spectral DPS is built upon a general DPS framework for nonlinear inverse\u0000problems. Several strategies developed in previous work, including jumpstart\u0000sampling, Jacobian approximation, and multi-step likelihood updates are applied\u0000facilitate stable and accurate decompositions. The effectiveness of spectral\u0000DPS was evaluated on a simulated dual-layer and a kV-switching spectral system\u0000as well as on a physical cone-beam CT (CBCT) test bench. In simulation studies,\u0000spectral DPS improved PSNR by 27.49% to 71.93% over baseline DPS and by 26.53%\u0000to 57.30% over MBMD, depending on the the region of interest. In physical\u0000phantom study, spectral DPS achieved a <1% error in estimating the mean density\u0000in a homogeneous region. Compared with baseline DPS, spectral DPS effectively\u0000avoided generating false structures in the homogeneous phantom and reduced the\u0000variability around edges. Both simulation and physical phantom studies\u0000demonstrated the superior performance of spectral DPS for stable and accurate\u0000material decomposition.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Dadgar, S. Parzych, F. Tayefi Ardebili, J. Baran, N. Chug, C. Curceanu, E. Czerwinski, K. Dulski, K. Eliyan, A. Gajos, B. C. Hiesmayr, K. Kacprzak, L. Kaplon, K. Klimaszewski, P. Konieczka, G. Korcyl, T. Kozik, W. Krzemien, D. Kumar, S. Niedzwiecki, D. Panek, E. Perez del Rio, L. Raczynski, S. Sharma, Shivani, R. Y. Shopa, M. Skurzok, K. Tayefi Ardebili, S. Vandenberghe, W. Wislicki, E. Stepien, P. Moskal
The growing interest in human-grade total body positron emission tomography (PET) systems has also application in small animal research. Due to the existing limitations in human-based studies involving drug development and novel treatment monitoring, animal-based research became a necessary step for testing and protocol preparation. In this simulation-based study two unconventional, cost-effective small animal total body PET scanners (for mouse and rat studies) have been investigated in order to inspect their feasibility for preclinical research. They were designed with the novel technology explored by the Jagiellonian-PET (J-PET) Collaboration. Two main PET characteristics: sensitivity and spatial resolution were mainly inspected to evaluate their performance. Moreover, the impact of the scintillator dimension and time-of-flight on the latter parameter was examined in order to design the most efficient tomographs. The presented results show that for mouse TB J-PET the achievable system sensitivity is equal to 2.35% and volumetric spatial resolution to 9.46 +- 0.54 mm3, while for rat TB J-PET they are equal to 2.6% and 14.11 +- 0.80 mm3, respectively. Furthermore, it was shown that the designed tomographs are almost parallax-free systems, hence, they resolve the problem of the acceptance criterion tradeoff between enhancing spatial resolution and reducing sensitivity.
{"title":"Investigation of Novel Preclinical Total Body PET Designed With J-PET Technology:A Simulation Study","authors":"M. Dadgar, S. Parzych, F. Tayefi Ardebili, J. Baran, N. Chug, C. Curceanu, E. Czerwinski, K. Dulski, K. Eliyan, A. Gajos, B. C. Hiesmayr, K. Kacprzak, L. Kaplon, K. Klimaszewski, P. Konieczka, G. Korcyl, T. Kozik, W. Krzemien, D. Kumar, S. Niedzwiecki, D. Panek, E. Perez del Rio, L. Raczynski, S. Sharma, Shivani, R. Y. Shopa, M. Skurzok, K. Tayefi Ardebili, S. Vandenberghe, W. Wislicki, E. Stepien, P. Moskal","doi":"arxiv-2408.00574","DOIUrl":"https://doi.org/arxiv-2408.00574","url":null,"abstract":"The growing interest in human-grade total body positron emission tomography\u0000(PET) systems has also application in small animal research. Due to the\u0000existing limitations in human-based studies involving drug development and\u0000novel treatment monitoring, animal-based research became a necessary step for\u0000testing and protocol preparation. In this simulation-based study two\u0000unconventional, cost-effective small animal total body PET scanners (for mouse\u0000and rat studies) have been investigated in order to inspect their feasibility\u0000for preclinical research. They were designed with the novel technology explored\u0000by the Jagiellonian-PET (J-PET) Collaboration. Two main PET characteristics:\u0000sensitivity and spatial resolution were mainly inspected to evaluate their\u0000performance. Moreover, the impact of the scintillator dimension and\u0000time-of-flight on the latter parameter was examined in order to design the most\u0000efficient tomographs. The presented results show that for mouse TB J-PET the\u0000achievable system sensitivity is equal to 2.35% and volumetric spatial\u0000resolution to 9.46 +- 0.54 mm3, while for rat TB J-PET they are equal to 2.6%\u0000and 14.11 +- 0.80 mm3, respectively. Furthermore, it was shown that the\u0000designed tomographs are almost parallax-free systems, hence, they resolve the\u0000problem of the acceptance criterion tradeoff between enhancing spatial\u0000resolution and reducing sensitivity.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis of COVID-19. Method: Electronic search was performed using Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that examined machine learning or deep learning methods to determine the prognosis of COVID-19 using CT or chest X-ray images were included. Polled sensitivity, specificity area under the curve and diagnostic odds ratio were calculated. Result: A total of 36 articles were included; various prognosis-related issues, including disease severity, mechanical ventilation or admission to the intensive care unit and mortality, were investigated. Several AI models and architectures were employed, such as the Siamense model, support vector machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural networks. The models achieved 71%, 88% and 67% sensitivity for mortality, severity assessment and need for ventilation, respectively. The specificity of 69%, 89% and 89% were reported for the aforementioned variables. Conclusion: Based on the included articles, machine learning and deep learning methods used for the prognosis of COVID-19 patients using radiomic features from CT or CXR images can help clinicians manage patients and allocate resources more effectively. These studies also demonstrate that combining patient demographic, clinical data, laboratory tests and radiomic features improves model performances.
{"title":"Prognosis of COVID-19 using Artificial Intelligence: A Systematic Review and Meta-analysis","authors":"SaeedReza Motamedian, Sadra Mohaghegh, Elham Babadi Oregani, Mahrsa Amjadi, Parnian Shobeiri, Negin Cheraghi, Niusha Solouki, Nikoo Ahmadi, Hossein Mohammad-Rahimi, Yassine Bouchareb, Arman Rahmim","doi":"arxiv-2408.00208","DOIUrl":"https://doi.org/arxiv-2408.00208","url":null,"abstract":"Purpose: Artificial intelligence (AI) techniques have been extensively\u0000utilized for diagnosing and prognosis of several diseases in recent years. This\u0000study identifies, appraises and synthesizes published studies on the use of AI\u0000for the prognosis of COVID-19. Method: Electronic search was performed using\u0000Medline, Google Scholar, Scopus, Embase, Cochrane and ProQuest. Studies that\u0000examined machine learning or deep learning methods to determine the prognosis\u0000of COVID-19 using CT or chest X-ray images were included. Polled sensitivity,\u0000specificity area under the curve and diagnostic odds ratio were calculated.\u0000Result: A total of 36 articles were included; various prognosis-related issues,\u0000including disease severity, mechanical ventilation or admission to the\u0000intensive care unit and mortality, were investigated. Several AI models and\u0000architectures were employed, such as the Siamense model, support vector\u0000machine, Random Forest , eXtreme Gradient Boosting, and convolutional neural\u0000networks. The models achieved 71%, 88% and 67% sensitivity for mortality,\u0000severity assessment and need for ventilation, respectively. The specificity of\u000069%, 89% and 89% were reported for the aforementioned variables. Conclusion:\u0000Based on the included articles, machine learning and deep learning methods used\u0000for the prognosis of COVID-19 patients using radiomic features from CT or CXR\u0000images can help clinicians manage patients and allocate resources more\u0000effectively. These studies also demonstrate that combining patient demographic,\u0000clinical data, laboratory tests and radiomic features improves model\u0000performances.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yongyi Shi, Chuang Niu, Amber L. Simpson, Bruno De Man, Richard Do, Ge Wang
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative diffusion prior to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases.
{"title":"CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior","authors":"Yongyi Shi, Chuang Niu, Amber L. Simpson, Bruno De Man, Richard Do, Ge Wang","doi":"arxiv-2408.00092","DOIUrl":"https://doi.org/arxiv-2408.00092","url":null,"abstract":"CT is a main modality for imaging liver diseases, valuable in detecting and\u0000localizing liver tumors. Traditional anomaly detection methods analyze\u0000reconstructed images to identify pathological structures. However, these\u0000methods may produce suboptimal results, overlooking subtle differences among\u0000various tissue types. To address this challenge, here we employ generative\u0000diffusion prior to inpaint the liver as the reference facilitating anomaly\u0000detection. Specifically, we use an adaptive threshold to extract a mask of\u0000abnormal regions, which are then inpainted using a diffusion prior to\u0000calculating an anomaly score based on the discrepancy between the original CT\u0000image and the inpainted counterpart. Our methodology has been tested on two\u0000liver CT datasets, demonstrating a significant improvement in detection\u0000accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the\u0000state-of-the-art. This performance gain underscores the potential of our\u0000approach to refine the radiological assessment of liver diseases.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In radiotherapy, the dose-volume histogram (DVH) curve is an important means of evaluating the clinical feasibility of tumor control and side effects in normal organs against actual treatment. Fractionation, distributing the amounts of irradiation, is used to enhance the treatment effectiveness of tumor control and mitigation of normal tissue damage. Therefore, dose and volume receive time-varying effects per fractional treatment event. However, the difficulty of DVH superimposition of different situations prevents evaluation of the total DVH despite different shapes and receiving dose distributions of organs in each fraction. However, an actual evaluation is determined traditionally by the initial treatment plan because of summation difficulty. Mathematically, this difficulty can be regarded as a kind of optimal transport of DVH. For this study, we introduced DVH transportation on the curvilinear orthogonal space with respect to arbitrary time ($T$), time-varying dose ($D$), and time-varying volume ($V$), which was designated as the TDV space embedded in the Riemannian manifold.Transportation in the TDV space should satisfy the following: (a) the metrics between dose and volume must be equivalent for any fractions and (b) the cumulative characteristic of DVH must hold irrespective of the lapse of time. With consideration of the Ricci-flat condition for the $D$-direction and $V$-direction, we obtained the probability density distribution, which is described by Poisson's equation with radial diffusion process toward $T$. This geometrical requirement and transportation equation rigorously provided the feasible total DVH.
{"title":"A feasible dose-volume estimation of radiotherapy treatment with optimal transport using a concept for transportation of Ricci-flat time-varying dose-volume","authors":"Yusuke Anetai, Jun'ichi Kotoku","doi":"arxiv-2407.19876","DOIUrl":"https://doi.org/arxiv-2407.19876","url":null,"abstract":"In radiotherapy, the dose-volume histogram (DVH) curve is an important means\u0000of evaluating the clinical feasibility of tumor control and side effects in\u0000normal organs against actual treatment. Fractionation, distributing the amounts\u0000of irradiation, is used to enhance the treatment effectiveness of tumor control\u0000and mitigation of normal tissue damage. Therefore, dose and volume receive\u0000time-varying effects per fractional treatment event. However, the difficulty of\u0000DVH superimposition of different situations prevents evaluation of the total\u0000DVH despite different shapes and receiving dose distributions of organs in each\u0000fraction. However, an actual evaluation is determined traditionally by the\u0000initial treatment plan because of summation difficulty. Mathematically, this\u0000difficulty can be regarded as a kind of optimal transport of DVH. For this\u0000study, we introduced DVH transportation on the curvilinear orthogonal space\u0000with respect to arbitrary time ($T$), time-varying dose ($D$), and time-varying\u0000volume ($V$), which was designated as the TDV space embedded in the Riemannian\u0000manifold.Transportation in the TDV space should satisfy the following: (a) the\u0000metrics between dose and volume must be equivalent for any fractions and (b)\u0000the cumulative characteristic of DVH must hold irrespective of the lapse of\u0000time. With consideration of the Ricci-flat condition for the $D$-direction and\u0000$V$-direction, we obtained the probability density distribution, which is\u0000described by Poisson's equation with radial diffusion process toward $T$. This\u0000geometrical requirement and transportation equation rigorously provided the\u0000feasible total DVH.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"1402 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Blum, Ulrich Steinseifer, Michael Neidlin
Purpose: The purpose of this study is to address the lack of uncertainty quantification in numerical hemolysis models, which are critical for medical device evaluations. Specifically, we aim to incorporate experimental variability into these models using the Markov Chain Monte Carlo (MCMC) method to enhance predictive accuracy and robustness. Methods: We applied the MCMC method to an experimental hemolysis dataset to derive detailed stochastic distributions for the hemolysis Power Law model parameters $C$, $alpha$ and $beta$. These distributions were then propagated through a reduced order model of the FDA benchmark pump to quantify the experimental uncertainty in hemolysis measurements with respect to the predicted pump hemolysis. Results: The MCMC analysis revealed multiple local minima in the sum of squared errors, highlighting the non-uniqueness of traditional Power Law model fitting. The MCMC results showed a constant optimal $C=3.515x10-5$ and log normal distributions of $alpha$ and $beta$ with means of 0.614 and 1.795, respectively. The MCMC model closely matched the mean and variance of experimental data. In comparison, conventional deterministic models are not able to describe experimental variation. Conclusion: Incorporating Uncertainty quantification through MCMC enhances the robustness and predictive accuracy of hemolysis models. This method allows for better comparison of simulated hemolysis outcomes with in-vivo experiments and can integrate additional datasets, potentially setting a new standard in hemolysis modeling.
{"title":"Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance","authors":"Christopher Blum, Ulrich Steinseifer, Michael Neidlin","doi":"arxiv-2407.18757","DOIUrl":"https://doi.org/arxiv-2407.18757","url":null,"abstract":"Purpose: The purpose of this study is to address the lack of uncertainty\u0000quantification in numerical hemolysis models, which are critical for medical\u0000device evaluations. Specifically, we aim to incorporate experimental\u0000variability into these models using the Markov Chain Monte Carlo (MCMC) method\u0000to enhance predictive accuracy and robustness. Methods: We applied the MCMC method to an experimental hemolysis dataset to\u0000derive detailed stochastic distributions for the hemolysis Power Law model\u0000parameters $C$, $alpha$ and $beta$. These distributions were then propagated\u0000through a reduced order model of the FDA benchmark pump to quantify the\u0000experimental uncertainty in hemolysis measurements with respect to the\u0000predicted pump hemolysis. Results: The MCMC analysis revealed multiple local minima in the sum of\u0000squared errors, highlighting the non-uniqueness of traditional Power Law model\u0000fitting. The MCMC results showed a constant optimal $C=3.515x10-5$ and log\u0000normal distributions of $alpha$ and $beta$ with means of 0.614 and 1.795,\u0000respectively. The MCMC model closely matched the mean and variance of\u0000experimental data. In comparison, conventional deterministic models are not\u0000able to describe experimental variation. Conclusion: Incorporating Uncertainty quantification through MCMC enhances\u0000the robustness and predictive accuracy of hemolysis models. This method allows\u0000for better comparison of simulated hemolysis outcomes with in-vivo experiments\u0000and can integrate additional datasets, potentially setting a new standard in\u0000hemolysis modeling.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"10881 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amirhosein Toosi, Sara Harsini, François Bénard, Carlos Uribe, Arman Rahmim
Prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) imaging provides a tremendously exciting frontier in visualization of prostate cancer (PCa) metastatic lesions. However, accurate segmentation of metastatic lesions is challenging due to low signal-to-noise ratios and variable sizes, shapes, and locations of the lesions. This study proposes a novel approach for automated segmentation of metastatic lesions in PSMA PET/CT 3D volumetric images using 2D denoising diffusion probabilistic models (DDPMs). Instead of 2D trans-axial slices or 3D volumes, the proposed approach segments the lesions on generated multi-angle maximum intensity projections (MA-MIPs) of the PSMA PET images, then obtains the final 3D segmentation masks from 3D ordered subset expectation maximization (OSEM) reconstruction of 2D MA-MIPs segmentations. Our proposed method achieved superior performance compared to state-of-the-art 3D segmentation approaches in terms of accuracy and robustness in detecting and segmenting small metastatic PCa lesions. The proposed method has significant potential as a tool for quantitative analysis of metastatic burden in PCa patients.
{"title":"How To Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-Angle Maximum Intensity Projections and Diffusion Models","authors":"Amirhosein Toosi, Sara Harsini, François Bénard, Carlos Uribe, Arman Rahmim","doi":"arxiv-2407.18555","DOIUrl":"https://doi.org/arxiv-2407.18555","url":null,"abstract":"Prostate specific membrane antigen (PSMA) positron emission\u0000tomography/computed tomography (PET/CT) imaging provides a tremendously\u0000exciting frontier in visualization of prostate cancer (PCa) metastatic lesions.\u0000However, accurate segmentation of metastatic lesions is challenging due to low\u0000signal-to-noise ratios and variable sizes, shapes, and locations of the\u0000lesions. This study proposes a novel approach for automated segmentation of\u0000metastatic lesions in PSMA PET/CT 3D volumetric images using 2D denoising\u0000diffusion probabilistic models (DDPMs). Instead of 2D trans-axial slices or 3D\u0000volumes, the proposed approach segments the lesions on generated multi-angle\u0000maximum intensity projections (MA-MIPs) of the PSMA PET images, then obtains\u0000the final 3D segmentation masks from 3D ordered subset expectation maximization\u0000(OSEM) reconstruction of 2D MA-MIPs segmentations. Our proposed method achieved\u0000superior performance compared to state-of-the-art 3D segmentation approaches in\u0000terms of accuracy and robustness in detecting and segmenting small metastatic\u0000PCa lesions. The proposed method has significant potential as a tool for\u0000quantitative analysis of metastatic burden in PCa patients.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Direct3{gamma}PET is a novel, comprehensive pipelinefor direct estimation of emission points in three-gamma (3-{gamma})positron emission tomography (PET) imaging using b{eta}+ and {gamma}emitters. This approach addresses limitations in existing directreconstruction methods for 3-{gamma} PET, which often struggle withdetector imperfections and uncertainties in estimated intersectionpoints. The pipeline begins by processing raw data, managingprompt photon order in detectors, and propagating energy andspatial uncertainties on the line of response (LOR). It thenconstructs histo-images backprojecting non-symmetric Gaussianprobability density functions (PDFs) in the histo-image, withattenuation correction applied when such data is available. Athree-dimensional (3-D) convolutional neural network (CNN)performs image translation, mapping the histo-image to radioac-tivity image. This architecture is trained using both supervisedand adversarial approaches. Our evaluation demonstrates thesuperior performance of this method in balancing event inclu-sion and accuracy. For image reconstruction, we compare bothsupervised and adversarial neural network (NN) approaches.The adversarial approach shows better structural preservation,while the supervised approach provides slightly improved noisereduction.
直接3{gamma}PET是一种新颖、全面的管道，用于使用b{eta}+和{gamma}发射体直接估计三伽马（3-{gamma}）正电子发射断层成像（PET）中的发射点。这种方法解决了现有 3-{gamma} PET 直接重建方法的局限性，因为这种方法通常会因探测器的不完善和估计交点的不确定性而受到影响。该流水线首先处理原始数据，管理探测器中的前向光子顺序，并在响应线（LOR）上传播能量和空间不确定性。然后，它在组织图像中反向推算非对称高斯概率密度函数（PDF），并在有此类数据时应用衰减校正。三维（3-D）卷积神经网络（CNN）执行图像转换，将组织图像映射到射电透射率图像。该架构采用监督和对抗两种方法进行训练。我们的评估结果表明，这种方法在兼顾事件包容性和准确性方面具有更优越的性能。在图像重建方面，我们比较了监督和对抗两种神经网络（NN）方法。
{"title":"Direct3γ PET: A Pipeline for Direct Three-gamma PET Image Reconstruction","authors":"Youness Mellak, Alexandre Bousse, Thibaut Merlin, Debora Giovagnoli, Dimitris Visvikis","doi":"arxiv-2407.18337","DOIUrl":"https://doi.org/arxiv-2407.18337","url":null,"abstract":"Direct3{gamma}PET is a novel, comprehensive pipelinefor direct estimation of\u0000emission points in three-gamma (3-{gamma})positron emission tomography (PET)\u0000imaging using b{eta}+ and {gamma}emitters. This approach addresses\u0000limitations in existing directreconstruction methods for 3-{gamma} PET, which\u0000often struggle withdetector imperfections and uncertainties in estimated\u0000intersectionpoints. The pipeline begins by processing raw data, managingprompt\u0000photon order in detectors, and propagating energy andspatial uncertainties on\u0000the line of response (LOR). It thenconstructs histo-images backprojecting\u0000non-symmetric Gaussianprobability density functions (PDFs) in the histo-image,\u0000withattenuation correction applied when such data is available.\u0000Athree-dimensional (3-D) convolutional neural network (CNN)performs image\u0000translation, mapping the histo-image to radioac-tivity image. This architecture\u0000is trained using both supervisedand adversarial approaches. Our evaluation\u0000demonstrates thesuperior performance of this method in balancing event\u0000inclu-sion and accuracy. For image reconstruction, we compare bothsupervised\u0000and adversarial neural network (NN) approaches.The adversarial approach shows\u0000better structural preservation,while the supervised approach provides slightly\u0000improved noisereduction.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"150 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aristea Grammoustianou, Ali Saeidi, Johan Longo, Felix Risch, Adrian M. Ionescu
Traditional detection methods of C-reactive protein (CRP) inflammation biomarker, in blood are expensive, time-consuming and labor-intensive. Such existing point-of-care CRP detection devices remain invasive, since they need blood sampling (finger-pricking or venous puncture). Here, we propose an electrochemical impedance spectroscopy (EIS)-based sensor for the real-time, fast, specific, sensitive, and label-free detection of C-reactive protein in the interstitial fluid (ISF) that can be accessed with minimally invasive microneedle arrays. The sensor has the potential to be integrated in a wearable device similar with the continuous glucose monitoring, that will detect CRP in interstitial fluid in a non-invasive, inexpensive and straightforward manner. The affinity based assay was tested in both buffer and ISF-like solution. The limit of detection achieved was 0.7 ug/mL of CRP in buffer, and 0.8 ug/mL of CRP in ISF-like solution and the sensor shows excellent linearity up to 10 ug/mL. It is worth noting that the proposed sensor operates in low sample volume (down to 5 uL), and has a response time of 100 seconds.
{"title":"Real time detection of C reactive protein in interstitial fluid using electrochemical impedance spectroscopy, towards wearable health monitoring","authors":"Aristea Grammoustianou, Ali Saeidi, Johan Longo, Felix Risch, Adrian M. Ionescu","doi":"arxiv-2407.16734","DOIUrl":"https://doi.org/arxiv-2407.16734","url":null,"abstract":"Traditional detection methods of C-reactive protein (CRP) inflammation\u0000biomarker, in blood are expensive, time-consuming and labor-intensive. Such\u0000existing point-of-care CRP detection devices remain invasive, since they need\u0000blood sampling (finger-pricking or venous puncture). Here, we propose an\u0000electrochemical impedance spectroscopy (EIS)-based sensor for the real-time,\u0000fast, specific, sensitive, and label-free detection of C-reactive protein in\u0000the interstitial fluid (ISF) that can be accessed with minimally invasive\u0000microneedle arrays. The sensor has the potential to be integrated in a wearable\u0000device similar with the continuous glucose monitoring, that will detect CRP in\u0000interstitial fluid in a non-invasive, inexpensive and straightforward manner.\u0000The affinity based assay was tested in both buffer and ISF-like solution. The\u0000limit of detection achieved was 0.7 ug/mL of CRP in buffer, and 0.8 ug/mL of\u0000CRP in ISF-like solution and the sensor shows excellent linearity up to 10\u0000ug/mL. It is worth noting that the proposed sensor operates in low sample\u0000volume (down to 5 uL), and has a response time of 100 seconds.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Umberto Deut, Aurora Camperi, Cristiano Cavicchi, Roberto Cirio, Emanuele Data, Elisabetta Durisi, Veronica Ferrero, Arianna Ferro, Simona Giordanengo, Oscar A. Martì Villarreal, Felix Mas Milian, Elisabetta Medina, Diango M. Montalvan Olivares, Franco Mostardi, Valeria Monti, Roberto Sacchi, Edoardo Salmeri, Anna Vignati
Irradiations at Ultra High Dose Rate (UHDR) regimes, exceeding 40 Gy/s in single fractions lasting less than 200 ms, have shown an equivalent antitumor effect compared to conventional radio-therapy with reduced harm to normal tissues. This work details the hardware and software modi-fications implemented to deliver 10 MeV UHDR electron beams with a Linear Accelerator Elekta SL 18 MV and the beam characteristics obtained. GafChromic EBT XD films and an Advanced Markus chamber were used for the dosimetry characterization, while a silicon sensor assessed the machine's beam pulses stability and repeatability. Dose per pulse, average dose rate and instantaneous dose rate in the pulse were evaluated for four experimental settings, varying the source-to-surface dis-tance and the beam collimation, i.e. with and without the use of a cylindrical applicator. Results showed dose per pulse from 0.6 Gy to a few tens of Gy and average dose rate up to 300 Gy/s. The obtained results demonstrate the possibility to perform in-vitro radiobiology experiments and test of new technologies for beam monitoring and dosimetry at the upgraded LINAC, thus contributing to the electron UHDR research field.
{"title":"Characterization of a modified clinical linear accelerator for ultra-high dose rate electron beam delivery","authors":"Umberto Deut, Aurora Camperi, Cristiano Cavicchi, Roberto Cirio, Emanuele Data, Elisabetta Durisi, Veronica Ferrero, Arianna Ferro, Simona Giordanengo, Oscar A. Martì Villarreal, Felix Mas Milian, Elisabetta Medina, Diango M. Montalvan Olivares, Franco Mostardi, Valeria Monti, Roberto Sacchi, Edoardo Salmeri, Anna Vignati","doi":"arxiv-2407.16027","DOIUrl":"https://doi.org/arxiv-2407.16027","url":null,"abstract":"Irradiations at Ultra High Dose Rate (UHDR) regimes, exceeding 40 Gy/s in\u0000single fractions lasting less than 200 ms, have shown an equivalent antitumor\u0000effect compared to conventional radio-therapy with reduced harm to normal\u0000tissues. This work details the hardware and software modi-fications implemented\u0000to deliver 10 MeV UHDR electron beams with a Linear Accelerator Elekta SL 18 MV\u0000and the beam characteristics obtained. GafChromic EBT XD films and an Advanced\u0000Markus chamber were used for the dosimetry characterization, while a silicon\u0000sensor assessed the machine's beam pulses stability and repeatability. Dose per\u0000pulse, average dose rate and instantaneous dose rate in the pulse were\u0000evaluated for four experimental settings, varying the source-to-surface\u0000dis-tance and the beam collimation, i.e. with and without the use of a\u0000cylindrical applicator. Results showed dose per pulse from 0.6 Gy to a few tens\u0000of Gy and average dose rate up to 300 Gy/s. The obtained results demonstrate\u0000the possibility to perform in-vitro radiobiology experiments and test of new\u0000technologies for beam monitoring and dosimetry at the upgraded LINAC, thus\u0000contributing to the electron UHDR research field.","PeriodicalId":501378,"journal":{"name":"arXiv - PHYS - Medical Physics","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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