Pub Date : 2024-09-18DOI: 10.1101/2024.09.16.24313435
Joshua Zhu, Michela Destito, Chitanya Dhanireddy, Tommy Hager, Sajid Hossain, Saahil Chadha, Durga Sritharan, Anish Dhawan, Keervani Kandala, Christian Pedersen, Nicoletta Anzalone, Teresa Calimeri, Elena De Momi, Maria Francesca Spadea, Mariam Aboian, Sanjay Aneja
Purpose: Primary central nervous system lymphoma (PCNSL) is typically treated with chemotherapy, steroids, and/or whole brain radiotherapy (WBRT). Identifying which patients benefit from WBRT following chemotherapy, and which patients can be adequately treated with chemotherapy alone remains a persistent clinical challenge. Although WBRT is associated with improved outcomes, it also carries a risk of neuro-cognitive side effects. This study aims to refine patient phenotyping for PCNSL by leveraging deep learning (DL) extracted imaging biomarkers to enable personalized therapy. Methods: Our study included 71 patients treated at our institution between 2009-2021. The primary outcome of interest was overall survival (OS) assessed at one-year, two-year, and median cohort survival cutoffs. The DL model leveraged an 8-layer 2D convolutional neural network which analyzed individual slices of post-contrast T1-weighted pre-treatment MRI scans. Survival predictions were made using a weighted voting system related to tumor size. Model performance was assessed with accuracy, sensitivity, specificity, and F1 scores. Time-dependent AUCs were calculated and C-statistics were computed to summarize the results. Kaplan-Meier (KM) survival analysis assessed differences between low and high-risk groups and statistically evaluated using the log-rank test. External validation of our model was performed with a cohort of 40 patients from an external institution. Results: The cohort's average age was 65.6 years with an average OS of 2.80 years. The one-year, two-year, and median OS models achieved AUCs of 0.73 (95% C.I., 0.60-0.85), 0.70 (95% C.I., 0.58-0.82), and 0.73 (95% C.I., 0.58-0.82) respectively. KM survival curves showcased discrimination between low and high-risk groups in all models. External validation with our one-year model achieved AUC of 0.64 (95% C.I., 0.63-0.65) and significant risk discrimination. A sub-analysis showcased stable model performance across different tumor volumes and focality. Conclusion: DL classifiers of PCNSL MRIs can stratify patient phenotypes beyond traditional risk paradigms. Given dissensus surrounding PCNSL treatment, DL can augment risk stratification and treatment personalization, especially with regards to WBRT decision making. Keywords: PCNSL; deep learning; convolutional neural network; magnetic resonance imaging; prognosis; personalized medicine
{"title":"Deriving Imaging Biomarkers for Primary Central Nervous System Lymphoma Using Deep Learning","authors":"Joshua Zhu, Michela Destito, Chitanya Dhanireddy, Tommy Hager, Sajid Hossain, Saahil Chadha, Durga Sritharan, Anish Dhawan, Keervani Kandala, Christian Pedersen, Nicoletta Anzalone, Teresa Calimeri, Elena De Momi, Maria Francesca Spadea, Mariam Aboian, Sanjay Aneja","doi":"10.1101/2024.09.16.24313435","DOIUrl":"https://doi.org/10.1101/2024.09.16.24313435","url":null,"abstract":"<strong>Purpose</strong>: Primary central nervous system lymphoma (PCNSL) is typically treated with chemotherapy, steroids, and/or whole brain radiotherapy (WBRT). Identifying which patients benefit from WBRT following chemotherapy, and which patients can be adequately treated with chemotherapy alone remains a persistent clinical challenge. Although WBRT is associated with improved outcomes, it also carries a risk of neuro-cognitive side effects. This study aims to refine patient phenotyping for PCNSL by leveraging deep learning (DL) extracted imaging biomarkers to enable personalized therapy.\u0000<strong>Methods</strong>: Our study included 71 patients treated at our institution between 2009-2021. The primary outcome of interest was overall survival (OS) assessed at one-year, two-year, and median cohort survival cutoffs. The DL model leveraged an 8-layer 2D convolutional neural network which analyzed individual slices of post-contrast T1-weighted pre-treatment MRI scans. Survival predictions were made using a weighted voting system related to tumor size. Model performance was assessed with accuracy, sensitivity, specificity, and F1 scores. Time-dependent AUCs were calculated and C-statistics were computed to summarize the results. Kaplan-Meier (KM) survival analysis assessed differences between low and high-risk groups and statistically evaluated using the log-rank test. External validation of our model was performed with a cohort of 40 patients from an external institution. <strong>Results</strong>: The cohort's average age was 65.6 years with an average OS of 2.80 years. The one-year, two-year, and median OS models achieved AUCs of 0.73 (95% C.I., 0.60-0.85), 0.70 (95% C.I., 0.58-0.82), and 0.73 (95% C.I., 0.58-0.82) respectively. KM survival curves showcased discrimination between low and high-risk groups in all models. External validation with our one-year model achieved AUC of 0.64 (95% C.I., 0.63-0.65) and significant risk discrimination. A sub-analysis showcased stable model performance across different tumor volumes and focality.\u0000<strong>Conclusion</strong>: DL classifiers of PCNSL MRIs can stratify patient phenotypes beyond traditional risk paradigms. Given dissensus surrounding PCNSL treatment, DL can augment risk stratification and treatment personalization, especially with regards to WBRT decision making.\u0000<strong>Keywords</strong>: PCNSL; deep learning; convolutional neural network; magnetic resonance imaging; prognosis; personalized medicine","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252346","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}
Pub Date : 2024-09-18DOI: 10.1101/2024.09.17.24313673
Francesco Santini, Michele Giovanni Croce, Xeni Deligianni, Matteo Paoletti, Leonardo Barzaghi, Niels Bergsland, Arianna Faggioli, Giulia Manco, Chiara Bonizzoni, Ning Jin, Sabrina Ravaglia, Anna Pichiecchio
Thanks to the rapid evolution of therapeutic strategies for muscular and neuromuscular diseases, the identification of quantitative biomarkers for disease identification and monitoring has become crucial. Magnetic resonance imaging (MRI) has been playing an important role by noninvasively assessing structural and functional muscular changes. This exploratory study investigated the potential of dynamic MRI during neuromuscular electrical stimulation (NMES) to detect differences between healthy controls (HCs) and patients with metabolic and myotonic myopathies. The study included 14 HCs and 10 patients with confirmed muscular diseases. All individuals were scanned with 3T MRI with a protocol that included a multi-echo gradient echo sequence for fat fraction quantification, multi-echo spin-echo for water T2 relaxation time calculation, and 3D phase contrast sequences during NMES. The strain tensor, buildup and release rates were calculated from velocity datasets. Results showed that strain and strain buildup rate were reduced in the soleus muscle of patients compared to HCs, suggesting these parameters could serve as biomarkers of muscle dysfunction. Notably, there were no significant differences in fat fraction or water T2 measurements between patients and HCs, indicating that the observed changes reflect alterations in muscle contractile properties that are not reflected by structural changes. The findings provide preliminary evidence that dynamic muscle MRI during NMES can detect abnormalities in muscle contraction in patients with myotonia and metabolic myopathies, warranting further research with larger, more homogeneous patient cohorts.
{"title":"Dynamic MR of muscle contraction during electrical muscle stimulation as a potential diagnostic tool for neuromuscular disease","authors":"Francesco Santini, Michele Giovanni Croce, Xeni Deligianni, Matteo Paoletti, Leonardo Barzaghi, Niels Bergsland, Arianna Faggioli, Giulia Manco, Chiara Bonizzoni, Ning Jin, Sabrina Ravaglia, Anna Pichiecchio","doi":"10.1101/2024.09.17.24313673","DOIUrl":"https://doi.org/10.1101/2024.09.17.24313673","url":null,"abstract":"Thanks to the rapid evolution of therapeutic strategies for muscular and neuromuscular diseases, the identification of quantitative biomarkers for disease identification and monitoring has become crucial. Magnetic resonance imaging (MRI) has been playing an important role by noninvasively assessing structural and functional muscular changes. This exploratory study investigated the potential of dynamic MRI during neuromuscular electrical stimulation (NMES) to detect differences between healthy controls (HCs) and patients with metabolic and myotonic myopathies. The study included 14 HCs and 10 patients with confirmed muscular diseases. All individuals were scanned with 3T MRI with a protocol that included a multi-echo gradient echo sequence for fat fraction quantification, multi-echo spin-echo for water T2 relaxation time calculation, and 3D phase contrast sequences during NMES. The strain tensor, buildup and release rates were calculated from velocity datasets. Results showed that strain and strain buildup rate were reduced in the soleus muscle of patients compared to HCs, suggesting these parameters could serve as biomarkers of muscle dysfunction. Notably, there were no significant differences in fat fraction or water T2 measurements between patients and HCs, indicating that the observed changes reflect alterations in muscle contractile properties that are not reflected by structural changes. The findings provide preliminary evidence that dynamic muscle MRI during NMES can detect abnormalities in muscle contraction in patients with myotonia and metabolic myopathies, warranting further research with larger, more homogeneous patient cohorts.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252345","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}
Pub Date : 2024-09-18DOI: 10.1101/2024.09.17.24313704
YUSUF AKHTAR, JAYARAM K. UDUPA, Yubing Tong, Caiyun Wu, Tiange Liu, Leihui Tong, Mahdie Hosseini, Mostafa Al-Noury, Manali Chodvadiya, Joseph M. McDonough, Oscar H. Mayer, David M. Biko, Jason B. Anari, Patrick J. Cahill, Drew A. Torigian
Purpose: In respiratory disorders such as thoracic insufficiency syndrome (TIS), the quantitative study of the regional motion of the left hemi-diaphragm (LHD) and right hemi-diaphragm (RHD) can give detailed insights into the distribution and severity of the abnormalities in individual patients. Dynamic magnetic resonance imaging (dMRI) is a preferred imaging modality for capturing dynamic images of respiration since dMRI does not involve ionizing radiation and can be obtained under free-breathing conditions. Using 4D images constructed from dMRI of sagittal locations, diaphragm segmentation is an evident step for the said quantitative analysis of LHD and RHD in these 4D images. Methods: In this paper, we segment the LHD and RHD in three steps: recognition of diaphragm, delineation of diaphragm, and separation of diaphragm along the mid-sagittal plane into LHD and RHD. The challenges involved in dMRI images are low resolution, motion blur, suboptimal contrast resolution, inconsistent meaning of gray-level intensities for the same object across multiple scans, and low signal-to-noise ratio. We have utilized deep learning (DL) concepts such as Path Aggregation Network and Dual Attention Network for the recognition step, Dense-Net and Residual-Net in an enhanced encoder-decoder architecture for the delineation step, and a combination of GoogleNet and Recurrent Neural Network for the identification of the mid-sagittal plane in the separation step. Due to the challenging images of TIS patients attributed to their highly distorted and variable anatomy of the thorax, in such images we localize the diaphragm using the auto-segmentations of the lungs and the thoraco-abdominal skin. Results: We achieved an average and SD mean-Hausdorff distance of ~3 and 3 mm for the delineation step and a positional error of ~3 and 3 mm in recognizing the mid-sagittal plane in 100 3D test images of TIS patients with a different set of ~430 3D images of TIS patients utilized for building the models for delineation, and separation. We showed that auto-segmentations of the diaphragm are indistinguishable from segmentations by experts, in images of near-normal subjects. In addition, the algorithmic identification of the mid-sagittal plane is indistinguishable from its identification by experts in images of near-normal subjects. Conclusions: Motivated by applications in surgical planning for disorders such as TIS, we have shown an auto-segmentation set-up for the diaphragm in dMRI images of TIS pediatric subjects. The results are promising, showing that our system can handle the aforesaid challenges. We intend to use the auto-segmentations of the diaphragm to create the initial ground truth (GT) for newly acquired data and then refining them, to expedite the process of creating GT for diaphragm motion analysis, and to test the efficacy of our proposed method to optimize pre-treatment planning and post-operative assessment of patients with TIS and other disorders.
{"title":"Auto-segmentation of hemi-diaphragms in free-breathing dynamic MRI of pediatric subjects with thoracic insufficiency syndrome","authors":"YUSUF AKHTAR, JAYARAM K. UDUPA, Yubing Tong, Caiyun Wu, Tiange Liu, Leihui Tong, Mahdie Hosseini, Mostafa Al-Noury, Manali Chodvadiya, Joseph M. McDonough, Oscar H. Mayer, David M. Biko, Jason B. Anari, Patrick J. Cahill, Drew A. Torigian","doi":"10.1101/2024.09.17.24313704","DOIUrl":"https://doi.org/10.1101/2024.09.17.24313704","url":null,"abstract":"Purpose: In respiratory disorders such as thoracic insufficiency syndrome (TIS), the quantitative study of the regional motion of the left hemi-diaphragm (LHD) and right hemi-diaphragm (RHD) can give detailed insights into the distribution and severity of the abnormalities in individual patients. Dynamic magnetic resonance imaging (dMRI) is a preferred imaging modality for capturing dynamic images of respiration since dMRI does not involve ionizing radiation and can be obtained under free-breathing conditions. Using 4D images constructed from dMRI of sagittal locations, diaphragm segmentation is an evident step for the said quantitative analysis of LHD and RHD in these 4D images. Methods: In this paper, we segment the LHD and RHD in three steps: recognition of diaphragm, delineation of diaphragm, and separation of diaphragm along the mid-sagittal plane into LHD and RHD. The challenges involved in dMRI images are low resolution, motion blur, suboptimal contrast resolution, inconsistent meaning of gray-level intensities for the same object across multiple scans, and low signal-to-noise ratio. We have utilized deep learning (DL) concepts such as Path Aggregation Network and Dual Attention Network for the recognition step, Dense-Net and Residual-Net in an enhanced encoder-decoder architecture for the delineation step, and a combination of GoogleNet and Recurrent Neural Network for the identification of the mid-sagittal plane in the separation step. Due to the challenging images of TIS patients attributed to their highly distorted and variable anatomy of the thorax, in such images we localize the diaphragm using the auto-segmentations of the lungs and the thoraco-abdominal skin.\u0000Results: We achieved an average and SD mean-Hausdorff distance of ~3 and 3 mm for the delineation step and a positional error of ~3 and 3 mm in recognizing the mid-sagittal plane in 100 3D test images of TIS patients with a different set of ~430 3D images of TIS patients utilized for building the models for delineation, and separation. We showed that auto-segmentations of the diaphragm are indistinguishable from segmentations by experts, in images of near-normal subjects. In addition, the algorithmic identification of the mid-sagittal plane is indistinguishable from its identification by experts in images of near-normal subjects.\u0000Conclusions: Motivated by applications in surgical planning for disorders such as TIS, we have shown an auto-segmentation set-up for the diaphragm in dMRI images of TIS pediatric subjects. The results are promising, showing that our system can handle the aforesaid challenges. We intend to use the auto-segmentations of the diaphragm to create the initial ground truth (GT) for newly acquired data and then refining them, to expedite the process of creating GT for diaphragm motion analysis, and to test the efficacy of our proposed method to optimize pre-treatment planning and post-operative assessment of patients with TIS and other disorders.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252343","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}
Pub Date : 2024-09-17DOI: 10.1101/2024.09.16.24313700
Benedikt Sundermann, Reinhold Feldmann, Christian Mathys, Stefan Garde, Johanna M. H. Rau, Anke McLeod, Josef Weglage, Bettina Pfleiderer
Objective: Phenylketonuria (PKU) is an inherited disorder of amino acid metabolism. Despite early dietary treatment, cognitive functioning of patients has been reported as being inferior to healthy controls. Objective of this study was to assess functional connectivity (FC) alterations in PKU in cognition-related brain networks by resting-state functional magnetic resonance imaging. We followed a hierarchical analysis approach partially based on higher criticism (HC) statistics as previously applied in a larger sister-project in fetal alcohol syndrome. Results: After exclusions for excessive head movement, 11 female young adults with early-treated PKU (age: 27.2 +- 3.7 years) and 11 age-matched female healthy controls (age: 25.9 +- 3.8 years) were included in the analysis. We observed effects within attention networks and the default mode network, but not in fronto-parietal networks, at the HC-based intermediate analysis level. No between-network FC differences were found. In the most detailed analysis level, we could not identify single affected functional connections. Despite statistical power limitations in this small sample, findings are in line with previously reported FC alterations in PKU and the cognitive profile in young adults with PKU, particularly with the still uncertain notion that cognitive control deficits might become less pronounced when PKU patients reach adulthood.
{"title":"Exploring subthreshold functional network alterations in women with phenylketonuria by higher criticism","authors":"Benedikt Sundermann, Reinhold Feldmann, Christian Mathys, Stefan Garde, Johanna M. H. Rau, Anke McLeod, Josef Weglage, Bettina Pfleiderer","doi":"10.1101/2024.09.16.24313700","DOIUrl":"https://doi.org/10.1101/2024.09.16.24313700","url":null,"abstract":"Objective: Phenylketonuria (PKU) is an inherited disorder of amino acid metabolism. Despite early dietary treatment, cognitive functioning of patients has been reported as being inferior to healthy controls. Objective of this study was to assess functional connectivity (FC) alterations in PKU in cognition-related brain networks by resting-state functional magnetic resonance imaging. We followed a hierarchical analysis approach partially based on higher criticism (HC) statistics as previously applied in a larger sister-project in fetal alcohol syndrome. Results: After exclusions for excessive head movement, 11 female young adults with early-treated PKU (age: 27.2 +- 3.7 years) and 11 age-matched female healthy controls (age: 25.9 +- 3.8 years) were included in the analysis. We observed effects within attention networks and the default mode network, but not in fronto-parietal networks, at the HC-based intermediate analysis level. No between-network FC differences were found. In the most detailed analysis level, we could not identify single affected functional connections. Despite statistical power limitations in this small sample, findings are in line with previously reported FC alterations in PKU and the cognitive profile in young adults with PKU, particularly with the still uncertain notion that cognitive control deficits might become less pronounced when PKU patients reach adulthood.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252347","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}
Abstract This paper demonstrates that simplified Convolutional Neural Network (CNN) models can outperform traditional complex architectures, such as VGG-16, in the analysis of radiological images, particularly in datasets with fewer samples. We introduce two adopted CNN architectures, LightCnnRad and DepthNet, designed to optimize computational efficiency while maintaining high performance. These models were applied to nine radiological image datasets, both public and in-house, including MRI, CT, X-ray, and Ultrasound, to evaluate their robustness and generalizability. Our results show that these models achieve competitive accuracy with lower computational costs and resource requirements. This finding underscores the potential of streamlined models in clinical settings, offering an effective and efficient alternative for radiological image analysis. The implications for medical diagnostics are significant, suggesting that simpler, more efficient algorithms can deliver better performance, challenging the prevailing reliance on transfer learning and complex models. The complete codebase and detailed architecture of the LightCnnRad and DepthNet, along with step-by-step instructions, are accessible in our GitHub repository at https://github.com/PKhosravi-CityTech/LightCNNRad-DepthNet.
{"title":"Beyond Algorithms: The Impact of Simplified CNN Models and Multifactorial Influences on Radiological Image Analysis","authors":"Saber Mohammadi, Abhinita S. Mohanty, Shady Saikali, Doori Rose, WintPyae LynnHtaik, Raecine Greaves, Tassadit Lounes, Eshaan Haque, Aashi Hirani, Javad Zahiri, Iman Dehzangi, Vipul Patel, Pegah Khosravi","doi":"10.1101/2024.09.15.24313585","DOIUrl":"https://doi.org/10.1101/2024.09.15.24313585","url":null,"abstract":"Abstract This paper demonstrates that simplified Convolutional Neural Network (CNN) models can outperform traditional complex architectures, such as VGG-16, in the analysis of radiological images, particularly in datasets with fewer samples. We introduce two adopted CNN architectures, LightCnnRad and DepthNet, designed to optimize computational efficiency while maintaining high performance. These models were applied to nine radiological image datasets, both public and in-house, including MRI, CT, X-ray, and Ultrasound, to evaluate their robustness and generalizability. Our results show that these models achieve competitive accuracy with lower computational costs and resource requirements. This finding underscores the potential of streamlined models in clinical settings, offering an effective and efficient alternative for radiological image analysis. The implications for medical diagnostics are significant, suggesting that simpler, more efficient algorithms can deliver better performance, challenging the prevailing reliance on transfer learning and complex models. The complete codebase and detailed architecture of the LightCnnRad and DepthNet, along with step-by-step instructions, are accessible in our GitHub repository at https://github.com/PKhosravi-CityTech/LightCNNRad-DepthNet.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252348","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}
Pub Date : 2024-09-12DOI: 10.1101/2024.09.10.24313302
Pascal Wodtke, Mary A McLean, Ines Horvat-Menih, Jonathan R Birchall, Maria J Zamora-Morales, Ashley Grimmer, Elizabeth Latimer, Marta Wylot, Rolf F Schulte, Ferdia A Gallagher
Background The Warburg effect is a hallmark of cancer and is characterized by increased glucose consumption and lactate formation. Deuterium metabolic imaging (DMI) is an emerging non-invasive MRI method for probing this metabolic reprogramming in the field of neuroimaging. Here we show the feasibility of the technique for abdominal imaging using a routine 3 T MRI system, which has previously presented significant technical challenges. Purpose This study aimed to translate abdominal DMI to clinical field strength by optimizing the radiofrequency coil setup, the administered dose of deuterium (2H)-labelled glucose, and the data processing pipeline for quantitative characterization of DMI signals over time in the kidney and liver, establishing a basis for routine clinical studies in the future. Materials and Methods Five healthy volunteers were recruited and imaged on 2 or 3 occasions, with different 2H-glucose doses (totalling 13 DMI scan sessions). DMI was performed at 3 T using a flexible 20 x 30 cm22H-tuned transmit-receive surface coil. We have defined three novel quantitative parameters as metrics of metabolism and compared these between doses and organs. Results The careful positioning of a dedicated surface coil minimized unwanted gastric signals while maintaining excellent hepatic and renal measurements. The timecourses derived from the liver and kidney were reproducible and comparable across different doses, with a trend towards lower quantitative measurements with decreasing dose. An increase in the 2H-water signal over time particularly in the liver, could be used as an indirect measure of metabolism. Conclusion DMI of the human abdomen is feasible using a routine MRI system and the metabolism measured in the kidney and liver can serve as a reference for future clinical studies. The 2H-glucose dose can be reduced from 0.75 to 0.25 g/kg to minimize gastric signal without substantially affecting the reliability of organ quantification.
{"title":"Deuterium metabolic imaging of the human abdomen at clinical field strength","authors":"Pascal Wodtke, Mary A McLean, Ines Horvat-Menih, Jonathan R Birchall, Maria J Zamora-Morales, Ashley Grimmer, Elizabeth Latimer, Marta Wylot, Rolf F Schulte, Ferdia A Gallagher","doi":"10.1101/2024.09.10.24313302","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313302","url":null,"abstract":"<strong>Background</strong>\u0000The Warburg effect is a hallmark of cancer and is characterized by increased glucose consumption and lactate formation. Deuterium metabolic imaging (DMI) is an emerging non-invasive MRI method for probing this metabolic reprogramming in the field of neuroimaging. Here we show the feasibility of the technique for abdominal imaging using a routine 3 T MRI system, which has previously presented significant technical challenges. <strong>Purpose</strong>\u0000This study aimed to translate abdominal DMI to clinical field strength by optimizing the radiofrequency coil setup, the administered dose of deuterium (<sup>2</sup>H)-labelled glucose, and the data processing pipeline for quantitative characterization of DMI signals over time in the kidney and liver, establishing a basis for routine clinical studies in the future. <strong>Materials and Methods</strong>\u0000Five healthy volunteers were recruited and imaged on 2 or 3 occasions, with different <sup>2</sup>H-glucose doses (totalling 13 DMI scan sessions). DMI was performed at 3 T using a flexible 20 x 30 cm<sup>2</sup> <sup>2</sup>H-tuned transmit-receive surface coil. We have defined three novel quantitative parameters as metrics of metabolism and compared these between doses and organs. <strong>Results</strong>\u0000The careful positioning of a dedicated surface coil minimized unwanted gastric signals while maintaining excellent hepatic and renal measurements. The timecourses derived from the liver and kidney were reproducible and comparable across different doses, with a trend towards lower quantitative measurements with decreasing dose. An increase in the <sup>2</sup>H-water signal over time particularly in the liver, could be used as an indirect measure of metabolism. <strong>Conclusion</strong>\u0000DMI of the human abdomen is feasible using a routine MRI system and the metabolism measured in the kidney and liver can serve as a reference for future clinical studies. The <sup>2</sup>H-glucose dose can be reduced from 0.75 to 0.25 g/kg to minimize gastric signal without substantially affecting the reliability of organ quantification.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181561","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}
Pub Date : 2024-09-11DOI: 10.1101/2024.08.29.24311575
Madison T Baxter, Christopher C Conlin, Aditya Bagrodia, Tristan Barrett, Hauke Bartsch, Anja Brau, Matthew Cooperberg, Anders M Dale, Arnaud Guidon, Michael E Hahn, Mukesh G Harisinghani, Juan F Javier-Desloges, Sophia Kamran (Capuano), Christopher J Kane, Joshu M Kuperman, Daniel JA Margolis, Paul M Murphy, Nabih Nakrour, Michael A Ohliger, Rebecca Rakow-Penner, Ahmed Shabaik, Jeffry P Simko, Clare M Tempany, Natasha Wehrli, Sean A Woolen, Jingjing Zou, Tyler M Seibert
Background: Multiparametric MRI (mpMRI) is strongly recommended by current clinical guidelines for improved detection of clinically significant prostate cancer (csPCa). However, major limitations of mpMRI are the need for intravenous (IV) contrast and dependence on reader expertise. Efforts to address these issues include use of biparametric MRI (bpMRI) and advanced, quantitative MRI techniques. One such advanced technique is the Restriction Spectrum Imaging restriction score (RSIrs), an imaging biomarker that has been shown to improve quantitative accuracy of patient-level csPCa detection. Purpose: To evaluate whether IV contrast can be avoided in the setting of standardized, state-of-the-art image acquisition, with or without addition of RSIrs, and to evaluate characteristics of RSIrs as a stand-alone, quantitative biomarker. Design, setting, and participants: ART-Pro is a multisite, multinational trial that will be conducted in two stages, evaluating bpMRI, mpMRI, and RSIrs on accuracy of expert (ART-Pro-1) and non-expert (ART-Pro-2) radiologists' detection of csPCa. Additionally, RSIrs will be evaluated as a stand-alone, quantitative, objective biomarker (ART-Pro-1). This study will include a total of 500 patients referred for a multiparametric prostate MRI with a clinical suspicion of prostate cancer at any of the five participating sites (100 patients per site). Intervention: In ART-Pro-1, patients receive standard of care mpMRI, with addition of the RSI sequence, and subsets of the patients' images are read separately by two expert radiologists, one of whom is the standard of care radiologist (Reader 1). Three research reports are generated using: bpMRI only (Reader 1), mpMRI (Reader 1), and bpMRI + RSIrs (Reader 2). The clinical report is submitted by Reader 1. Patients' future prostate cancer management will be recorded and used to evaluate the performance of the MRI techniques being tested. In ART-Pro-2, the dataset created in ART-Pro-1 will be retrospectively reviewed by radiologists of varying experience level (novice, basic, and expert). Radiologists will be assigned to read cases and record research reports while viewing subsets of either mpMRI only or RSIrs + mpMRI. Patient cases will be read by two readers from each experience level (6 reads total), and findings will be evaluated against the expertly created dataset from ART-Pro-1. Outcome measurements and statistical analysis: The primary endpoint is to evaluate if bpMRI is non-inferior to mpMRI among expert radiologists (ART-Pro-1) and non-expert radiologists (ART-Pro-2) for detection of grade group (GG) ≥2 csPCa. We will conduct one-sided non-inferiority tests of correlated proportions (ART-Pro-1) and use McNemar's test and AUC to test the null hypothesis of non-inferiority (ART-Pro-1 and ART-Pro-2). Conclusions: This trial is registered in the US National Library of Medicine Trial Registry (NCT number: NCT06579417) at ClinicalTrials.gov. Patient accrual at the first site (UC S
{"title":"Advanced Restriction imaging and reconstruction Technology for Prostate MRI (ART-Pro): Study protocol for a multicenter, multinational trial evaluating biparametric MRI and advanced, quantitative diffusion MRI for detection of prostate cancer","authors":"Madison T Baxter, Christopher C Conlin, Aditya Bagrodia, Tristan Barrett, Hauke Bartsch, Anja Brau, Matthew Cooperberg, Anders M Dale, Arnaud Guidon, Michael E Hahn, Mukesh G Harisinghani, Juan F Javier-Desloges, Sophia Kamran (Capuano), Christopher J Kane, Joshu M Kuperman, Daniel JA Margolis, Paul M Murphy, Nabih Nakrour, Michael A Ohliger, Rebecca Rakow-Penner, Ahmed Shabaik, Jeffry P Simko, Clare M Tempany, Natasha Wehrli, Sean A Woolen, Jingjing Zou, Tyler M Seibert","doi":"10.1101/2024.08.29.24311575","DOIUrl":"https://doi.org/10.1101/2024.08.29.24311575","url":null,"abstract":"Background: Multiparametric MRI (mpMRI) is strongly recommended by current clinical guidelines for improved detection of clinically significant prostate cancer (csPCa). However, major limitations of mpMRI are the need for intravenous (IV) contrast and dependence on reader expertise. Efforts to address these issues include use of biparametric MRI (bpMRI) and advanced, quantitative MRI techniques. One such advanced technique is the Restriction Spectrum Imaging restriction score (RSIrs), an imaging biomarker that has been shown to improve quantitative accuracy of patient-level csPCa detection. Purpose: To evaluate whether IV contrast can be avoided in the setting of standardized, state-of-the-art image acquisition, with or without addition of RSIrs, and to evaluate characteristics of RSIrs as a stand-alone, quantitative biomarker. Design, setting, and participants: ART-Pro is a multisite, multinational trial that will be conducted in two stages, evaluating bpMRI, mpMRI, and RSIrs on accuracy of expert (ART-Pro-1) and non-expert (ART-Pro-2) radiologists' detection of csPCa. Additionally, RSIrs will be evaluated as a stand-alone, quantitative, objective biomarker (ART-Pro-1). This study will include a total of 500 patients referred for a multiparametric prostate MRI with a clinical suspicion of prostate cancer at any of the five participating sites (100 patients per site). Intervention: In ART-Pro-1, patients receive standard of care mpMRI, with addition of the RSI sequence, and subsets of the patients' images are read separately by two expert radiologists, one of whom is the standard of care radiologist (Reader 1). Three research reports are generated using: bpMRI only (Reader 1), mpMRI (Reader 1), and bpMRI + RSIrs (Reader 2). The clinical report is submitted by Reader 1. Patients' future prostate cancer management will be recorded and used to evaluate the performance of the MRI techniques being tested. In ART-Pro-2, the dataset created in ART-Pro-1 will be retrospectively reviewed by radiologists of varying experience level (novice, basic, and expert). Radiologists will be assigned to read cases and record research reports while viewing subsets of either mpMRI only or RSIrs + mpMRI. Patient cases will be read by two readers from each experience level (6 reads total), and findings will be evaluated against the expertly created dataset from ART-Pro-1. Outcome measurements and statistical analysis: The primary endpoint is to evaluate if bpMRI is non-inferior to mpMRI among expert radiologists (ART-Pro-1) and non-expert radiologists (ART-Pro-2) for detection of grade group (GG) ≥2 csPCa. We will conduct one-sided non-inferiority tests of correlated proportions (ART-Pro-1) and use McNemar's test and AUC to test the null hypothesis of non-inferiority (ART-Pro-1 and ART-Pro-2). Conclusions: This trial is registered in the US National Library of Medicine Trial Registry (NCT number: NCT06579417) at ClinicalTrials.gov. Patient accrual at the first site (UC S","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181560","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}
Pub Date : 2024-09-11DOI: 10.1101/2024.09.09.24313316
Marek Nikolic, Pedro Mediano, Tom Froese, David Reydellet, Tomas Palenicek
Psilocybin is a classic psychedelic and a novel treatment for mood disorders. Psilocybin induces dose-dependent transient (4-6 hours) usually pleasant changes in perception, cognition, and emotion by non-selectively agonizing the 5-HT2A receptors and negatively regulating serotonin reuptake, and long-term positive antidepressant effect on mood and well-being. Long-term effects are ascribed to the psychological quality of the acute experience, increase in synaptodensity and temporary (1-week) down-regulation of 5-HT2A receptors. Electroencephalography, a non-invasive neuroimaging tool, can track the acute effects of psilocybin; these include the suppression of alpha activity, decreased global connectivity, and increased brain entropy (i.e. brain signal diversity) in eyes-closed resting-state. However, few studies investigated how these modalities are affected together through the psychedelic experience. The current research aimed to evaluate the psilocybin intoxication temporal EEG profile. 20 healthy individuals (10 women) underwent oral administration of psilocybin (0.26 mg/kg) as part of a placebo-controlled cross-over study, resting-state 5-minute eyes closed EEG was obtained at baseline and 1, 1.5, 3, 6, and 24 hours after psilocybin administration. Absolute power, relative power spectral density (PSD), power envelope global functional connectivity (GFC), Lempel-Ziv complexity (LZ), and a Complexity via State-Space Entropy Rate (CSER) were obtained together with measures of subjective intensity of experience. Absolute power decreased in alpha and beta band, but increased in delta and gamma frequencies. 24h later was observed a broadband decrease. The PSD showed a decrease in alpha occipitally between 1 and 3 hours and a decrease in beta frontally at 3 hours, but power spectra distribution stayed the same 24h later. The GFC showed decrease acutely at 1, 1.5, and 3 hours in the alpha band. LZ and showed an increase at 1 and 1.5 hours. Decomposition of CSER into functional bands shows a decrease in alpha band but increase over higher frequencies. Further, complexity over a source space showed opposing changes in the Default Mode Network (DMN) and visual network between conditions, suggesting a relationship between signal complexity, stimulus integration, and perception of self. In an exploratory attempt, we found that a change in gamma GFC in DMN correlates with oceanic boundlessness. Psychological effects of psilocybin may be wrapped in personal interpretations and history unrelated to underlying neurobiological changes, but changes to perception of self may be bound to perceived loss of boundary based on whole brain synchrony with the DMN in higher frequency bands.
{"title":"Psilocybin alters brain activity related to sensory and cognitive processing in a time-dependent manner","authors":"Marek Nikolic, Pedro Mediano, Tom Froese, David Reydellet, Tomas Palenicek","doi":"10.1101/2024.09.09.24313316","DOIUrl":"https://doi.org/10.1101/2024.09.09.24313316","url":null,"abstract":"Psilocybin is a classic psychedelic and a novel treatment for mood disorders. Psilocybin induces dose-dependent transient (4-6 hours) usually pleasant changes in perception, cognition, and emotion by non-selectively agonizing the 5-HT2A receptors and negatively regulating serotonin reuptake, and long-term positive antidepressant effect on mood and well-being. Long-term effects are ascribed to the psychological quality of the acute experience, increase in synaptodensity and temporary (1-week) down-regulation of 5-HT2A receptors. Electroencephalography, a non-invasive neuroimaging tool, can track the acute effects of psilocybin; these include the suppression of alpha activity, decreased global connectivity, and increased brain entropy (i.e. brain signal diversity) in eyes-closed resting-state. However, few studies investigated how these modalities are affected together through the psychedelic experience. The current research aimed to evaluate the psilocybin intoxication temporal EEG profile. 20 healthy individuals (10 women) underwent oral administration of psilocybin (0.26 mg/kg) as part of a placebo-controlled cross-over study, resting-state 5-minute eyes closed EEG was obtained at baseline and 1, 1.5, 3, 6, and 24 hours after psilocybin administration. Absolute power, relative power spectral density (PSD), power envelope global functional connectivity (GFC), Lempel-Ziv complexity (LZ), and a Complexity via State-Space Entropy Rate (CSER) were obtained together with measures of subjective intensity of experience. Absolute power decreased in alpha and beta band, but increased in delta and gamma frequencies. 24h later was observed a broadband decrease. The PSD showed a decrease in alpha occipitally between 1 and 3 hours and a decrease in beta frontally at 3 hours, but power spectra distribution stayed the same 24h later. The GFC showed decrease acutely at 1, 1.5, and 3 hours in the alpha band. LZ and showed an increase at 1 and 1.5 hours. Decomposition of CSER into functional bands shows a decrease in alpha band but increase over higher frequencies. Further, complexity over a source space showed opposing changes in the Default Mode Network (DMN) and visual network between conditions, suggesting a relationship between signal complexity, stimulus integration, and perception of self. In an exploratory attempt, we found that a change in gamma GFC in DMN correlates with oceanic boundlessness. Psychological effects of psilocybin may be wrapped in personal interpretations and history unrelated to underlying neurobiological changes, but changes to perception of self may be bound to perceived loss of boundary based on whole brain synchrony with the DMN in higher frequency bands.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181548","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}
Pub Date : 2024-09-11DOI: 10.1101/2024.09.10.24313079
Muhammad Faizyab Ali Chaudhary, Hira Anees Awan, Sarah E Gerard, Sandeep Bodduluri, Alejandro P Comellas, Igor Z Barjaktarevic, R Graham Barr, Christopher B Cooper, Craig J Galban, MeiLan K Han, Jeffrey L Curtis, Nadia N Hansel, Jerry A Krishnan, Martha G Menchaca, Fernando J Martinez, Jill Ohar, Luis G. Vargas Buonfiglio, Robert Paine, Surya P Bhatt, Eric A Hoffman, Joseph M Reinhardt
Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). Objectives: To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD). Methods: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Measurements and Main Results: Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88). Conclusions: Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.
{"title":"Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV1 Decline in COPD","authors":"Muhammad Faizyab Ali Chaudhary, Hira Anees Awan, Sarah E Gerard, Sandeep Bodduluri, Alejandro P Comellas, Igor Z Barjaktarevic, R Graham Barr, Christopher B Cooper, Craig J Galban, MeiLan K Han, Jeffrey L Curtis, Nadia N Hansel, Jerry A Krishnan, Martha G Menchaca, Fernando J Martinez, Jill Ohar, Luis G. Vargas Buonfiglio, Robert Paine, Surya P Bhatt, Eric A Hoffman, Joseph M Reinhardt","doi":"10.1101/2024.09.10.24313079","DOIUrl":"https://doi.org/10.1101/2024.09.10.24313079","url":null,"abstract":"Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC).\u0000Objectives: To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD).\u0000Methods: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Measurements and Main Results: Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.β = -0.034, P < 0.001), FEV1/FVC (adj.β = -0.008, P < 0.001), SGRQ (adj.β = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.β = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.β = -0.032, P < 0.001), FEV1/FVC (adj.β = -0.007, P < 0.001), SGRQ (adj.β = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.β = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88).\u0000Conclusions: Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181549","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}
Pub Date : 2024-09-06DOI: 10.1101/2024.09.05.24313135
Emily R. Thompson, Li Wei Chen, Albert John Victor Miller
This study presents the development and evaluation of a 16-channel general-purpose MRI coil array constructed using 50-micron copper foil sheets. The coils were rapidly manufactured using a die cut process and assembled into a square-shaped array designed for flexible, high-performance imaging. The copper foil coil demonstrated superior signal-to-noise ratio (SNR), lower noise correlation, and better parallel imaging performance compared to a commercially available 16-channel flexible coil. Phantom testing showed a 17-20% improvement in SNR with the copper foil coil, while noise correlation matrices indicated reduced interference between coil elements. In vivo testing further validated the coil’s performance, with higher SNR and enhanced image quality observed in axial and sagittal scans. The use of copper foil sheets, which are widely available and cost-effective, enabled rapid production of the coils without compromising quality. This approach offers significant advantages over existing flexible coil technologies that rely on more complex and expensive materials, such as copper threads and liquid metal. The ability to quickly tailor these coils for specific patient needs makes them particularly suitable for clinical applications where flexibility and speed are essential. The results of this study suggest that copper foil-based coils represent a promising solution for improving the accessibility, adaptability, and performance of MRI technology in a cost-effective manner.
{"title":"Flexible Copper Foil Sheet Receive Coil Array for MRI","authors":"Emily R. Thompson, Li Wei Chen, Albert John Victor Miller","doi":"10.1101/2024.09.05.24313135","DOIUrl":"https://doi.org/10.1101/2024.09.05.24313135","url":null,"abstract":"This study presents the development and evaluation of a 16-channel general-purpose MRI coil array constructed using 50-micron copper foil sheets. The coils were rapidly manufactured using a die cut process and assembled into a square-shaped array designed for flexible, high-performance imaging. The copper foil coil demonstrated superior signal-to-noise ratio (SNR), lower noise correlation, and better parallel imaging performance compared to a commercially available 16-channel flexible coil. Phantom testing showed a 17-20% improvement in SNR with the copper foil coil, while noise correlation matrices indicated reduced interference between coil elements. In vivo testing further validated the coil’s performance, with higher SNR and enhanced image quality observed in axial and sagittal scans. The use of copper foil sheets, which are widely available and cost-effective, enabled rapid production of the coils without compromising quality. This approach offers significant advantages over existing flexible coil technologies that rely on more complex and expensive materials, such as copper threads and liquid metal. The ability to quickly tailor these coils for specific patient needs makes them particularly suitable for clinical applications where flexibility and speed are essential. The results of this study suggest that copper foil-based coils represent a promising solution for improving the accessibility, adaptability, and performance of MRI technology in a cost-effective manner.","PeriodicalId":501358,"journal":{"name":"medRxiv - Radiology and Imaging","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181562","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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