Pub Date : 2025-02-19DOI: 10.1097/RLI.0000000000001162
Johannes Haubold, Olivia Barbara Pollok, Mathias Holtkamp, Luca Salhöfer, Cynthia Sabrina Schmidt, Christian Bojahr, Jannis Straus, Benedikt Michael Schaarschmidt, Katarzyna Borys, Judith Kohnke, Yutong Wen, Marcel Opitz, Lale Umutlu, Michael Forsting, Christoph M Friedrich, Felix Nensa, René Hosch
Objectives: Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences.
Methods: Initial BCA segmentations (10 body regions and 4 body parts) were generated by mapping CT segmentations from body and organ analysis (BOA) model to synthetic MR images created using an in-house trained CycleGAN. In total, 30 synthetic data pairs were used to train an initial nnU-Net V2 in 3D, and this preliminary model was then applied to segment 120 real T2-weighted MRI sequences from 120 patients (46% female) with a median age of 56 (interquartile range, 17.75), generating early segmentation proposals. These proposals were refined by human annotators, and nnU-Net V2 2D and 3D models were trained using 5-fold cross-validation on this optimized dataset of real MR images. Performance was evaluated using Sørensen-Dice, Surface Dice, and Hausdorff Distance metrics including 95% confidence intervals for cross-validation and ensemble models.
Results: The 3D ensemble segmentation model achieved the highest Dice scores for the body region classes: bone 0.926 (95% confidence interval [CI], 0.914-0.937), muscle 0.968 (95% CI, 0.961-0.975), subcutaneous fat 0.98 (95% CI, 0.971-0.986), nervous system 0.973 (95% CI, 0.965-0.98), thoracic cavity 0.978 (95% CI, 0.969-0.984), abdominal cavity 0.989 (95% CI, 0.986-0.991), mediastinum 0.92 (95% CI, 0.901-0.936), pericardium 0.945 (95% CI, 0.924-0.96), brain 0.966 (95% CI, 0.927-0.989), and glands 0.905 (95% CI, 0.886-0.921). Furthermore, body part 2D ensemble model reached the highest Dice scores for all labels: arms 0.952 (95% CI, 0.937-0.965), head + neck 0.965 (95% CI, 0.953-0.976), legs 0.978 (95% CI, 0.968-0.988), and torso 0.99 (95% CI, 0.988-0.991). The overall average Dice across body parts (2D = 0.971, 3D = 0.969, P = ns) and body regions (2D = 0.935, 3D = 0.955, P < 0.001) ensemble models indicates stable performance across all classes.
Conclusions: The presented approach facilitates efficient and automated extraction of BCA parameters from T2-weighted MRI sequences, providing precise and detailed body composition information across various regions and body parts.
{"title":"Moving Beyond CT Body Composition Analysis: Using Style Transfer for Bringing CT-Based Fully-Automated Body Composition Analysis to T2-Weighted MRI Sequences.","authors":"Johannes Haubold, Olivia Barbara Pollok, Mathias Holtkamp, Luca Salhöfer, Cynthia Sabrina Schmidt, Christian Bojahr, Jannis Straus, Benedikt Michael Schaarschmidt, Katarzyna Borys, Judith Kohnke, Yutong Wen, Marcel Opitz, Lale Umutlu, Michael Forsting, Christoph M Friedrich, Felix Nensa, René Hosch","doi":"10.1097/RLI.0000000000001162","DOIUrl":"10.1097/RLI.0000000000001162","url":null,"abstract":"<p><strong>Objectives: </strong>Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences.</p><p><strong>Methods: </strong>Initial BCA segmentations (10 body regions and 4 body parts) were generated by mapping CT segmentations from body and organ analysis (BOA) model to synthetic MR images created using an in-house trained CycleGAN. In total, 30 synthetic data pairs were used to train an initial nnU-Net V2 in 3D, and this preliminary model was then applied to segment 120 real T2-weighted MRI sequences from 120 patients (46% female) with a median age of 56 (interquartile range, 17.75), generating early segmentation proposals. These proposals were refined by human annotators, and nnU-Net V2 2D and 3D models were trained using 5-fold cross-validation on this optimized dataset of real MR images. Performance was evaluated using Sørensen-Dice, Surface Dice, and Hausdorff Distance metrics including 95% confidence intervals for cross-validation and ensemble models.</p><p><strong>Results: </strong>The 3D ensemble segmentation model achieved the highest Dice scores for the body region classes: bone 0.926 (95% confidence interval [CI], 0.914-0.937), muscle 0.968 (95% CI, 0.961-0.975), subcutaneous fat 0.98 (95% CI, 0.971-0.986), nervous system 0.973 (95% CI, 0.965-0.98), thoracic cavity 0.978 (95% CI, 0.969-0.984), abdominal cavity 0.989 (95% CI, 0.986-0.991), mediastinum 0.92 (95% CI, 0.901-0.936), pericardium 0.945 (95% CI, 0.924-0.96), brain 0.966 (95% CI, 0.927-0.989), and glands 0.905 (95% CI, 0.886-0.921). Furthermore, body part 2D ensemble model reached the highest Dice scores for all labels: arms 0.952 (95% CI, 0.937-0.965), head + neck 0.965 (95% CI, 0.953-0.976), legs 0.978 (95% CI, 0.968-0.988), and torso 0.99 (95% CI, 0.988-0.991). The overall average Dice across body parts (2D = 0.971, 3D = 0.969, P = ns) and body regions (2D = 0.935, 3D = 0.955, P < 0.001) ensemble models indicates stable performance across all classes.</p><p><strong>Conclusions: </strong>The presented approach facilitates efficient and automated extraction of BCA parameters from T2-weighted MRI sequences, providing precise and detailed body composition information across various regions and body parts.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-19DOI: 10.1097/RLI.0000000000001166
Robert Haase, Thomas Pinetz, Erich Kobler, Zeynep Bendella, Stefan Zülow, Arndt-Hendrik Schievelkamp, Frederic Carsten Schmeel, Sarah Panahabadi, Anna Magdalena Stylianou, Daniel Paech, Martha Foltyn-Dumitru, Verena Wagner, Kai Schlamp, Gudula Heussel, Mathias Holtkamp, Claus Peter Heussel, Martin Vahlensieck, Julian A Luetkens, Heinz-Peter Schlemmer, Johannes Haubold, Alexander Radbruch, Alexander Effland, Cornelius Deuschl, Katerina Deike
Objectives: Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this study was to test the benefit of a deep learning-based contrast signal amplification in true single-dose T1-weighted (T-SD) images creating artificial double-dose (A-DD) images for metastasis detection in brain magnetic resonance imaging.
Materials and methods: In this prospective, multicenter study, a deep learning-based method originally trained on noncontrast, low-dose, and T-SD brain images was applied to T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8 years; 23 women) acquired externally between November 2022 and June 2023. Four readers with different levels of experience independently reviewed T-SD and A-DD images for metastases with 4 weeks between readings. A reference reader reviewed additionally acquired true double-dose images to determine any metastases present. Performances were compared using Mid-p McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.
Results: All readers found more metastases using A-DD images. The 2 experienced neuroradiologists achieved the same level of sensitivity using T-SD images (62 of 91 metastases, 68.1%). While the increase in sensitivity using A-DD images was only descriptive for 1 of them (A-DD: 65 of 91 metastases, +3.3%, P = 0.424), the second neuroradiologist benefited significantly with a sensitivity increase of 12.1% (73 of 91 metastases, P = 0.008). The 2 less experienced readers (1 resident and 1 fellow) both found significantly more metastases on A-DD images (resident, T-SD: 61.5%, A-DD: 68.1%, P = 0.039; fellow, T-SD: 58.2%, A-DD: 70.3%, P = 0.008). They were therefore able to use A-DD images to increase their sensitivity to the neuroradiologists' initial level on regular T-SD images. False-positive findings did not differ significantly between sequences. However, readers showed descriptively more false-positive findings on A-DD images. The benefit in sensitivity particularly applied to metastases ≤5 mm (5.7%-17.3% increase in sensitivity).
Conclusions: A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.
{"title":"Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.","authors":"Robert Haase, Thomas Pinetz, Erich Kobler, Zeynep Bendella, Stefan Zülow, Arndt-Hendrik Schievelkamp, Frederic Carsten Schmeel, Sarah Panahabadi, Anna Magdalena Stylianou, Daniel Paech, Martha Foltyn-Dumitru, Verena Wagner, Kai Schlamp, Gudula Heussel, Mathias Holtkamp, Claus Peter Heussel, Martin Vahlensieck, Julian A Luetkens, Heinz-Peter Schlemmer, Johannes Haubold, Alexander Radbruch, Alexander Effland, Cornelius Deuschl, Katerina Deike","doi":"10.1097/RLI.0000000000001166","DOIUrl":"10.1097/RLI.0000000000001166","url":null,"abstract":"<p><strong>Objectives: </strong>Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this study was to test the benefit of a deep learning-based contrast signal amplification in true single-dose T1-weighted (T-SD) images creating artificial double-dose (A-DD) images for metastasis detection in brain magnetic resonance imaging.</p><p><strong>Materials and methods: </strong>In this prospective, multicenter study, a deep learning-based method originally trained on noncontrast, low-dose, and T-SD brain images was applied to T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8 years; 23 women) acquired externally between November 2022 and June 2023. Four readers with different levels of experience independently reviewed T-SD and A-DD images for metastases with 4 weeks between readings. A reference reader reviewed additionally acquired true double-dose images to determine any metastases present. Performances were compared using Mid-p McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.</p><p><strong>Results: </strong>All readers found more metastases using A-DD images. The 2 experienced neuroradiologists achieved the same level of sensitivity using T-SD images (62 of 91 metastases, 68.1%). While the increase in sensitivity using A-DD images was only descriptive for 1 of them (A-DD: 65 of 91 metastases, +3.3%, P = 0.424), the second neuroradiologist benefited significantly with a sensitivity increase of 12.1% (73 of 91 metastases, P = 0.008). The 2 less experienced readers (1 resident and 1 fellow) both found significantly more metastases on A-DD images (resident, T-SD: 61.5%, A-DD: 68.1%, P = 0.039; fellow, T-SD: 58.2%, A-DD: 70.3%, P = 0.008). They were therefore able to use A-DD images to increase their sensitivity to the neuroradiologists' initial level on regular T-SD images. False-positive findings did not differ significantly between sequences. However, readers showed descriptively more false-positive findings on A-DD images. The benefit in sensitivity particularly applied to metastases ≤5 mm (5.7%-17.3% increase in sensitivity).</p><p><strong>Conclusions: </strong>A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-14DOI: 10.1097/RLI.0000000000001164
Fredrik Wärnberg, Oskar Axelsson, Dan Curiac, Paul Hargreaves, Andreas Karakatsanis, Sujinna Lekmeechai, Mats Hansen
Objectives: The primary objective of the first-in-human (FIH) study was to evaluate the safety and tolerability of the manganese (Mn)-based contrast agent pegfosimer manganese in participants with newly diagnosed breast cancer, and secondary objectives included preliminary efficacy, and pharmacokinetics (PK) of the agent.
Methods: A single intravenous 1-hour infusion of pegfosimer manganese was administered to 2 cohorts; 6 participants at the starting dose of 10 μmol Mn/kg, followed by 8 participants at the expansion dose of 20 μmol Mn/kg, cohorts 1 and 2, respectively. The safety was evaluated based on reported adverse events (AEs), including serious AEs, physical examination, vital signs, electrocardiogram, and safety laboratory parameters. Magnetic resonance imaging (MRI) acquisition was performed precontrast and postcontrast to assess the clinical relevance of images in primary breast tumors, liver, and pancreas relative to reference tissue. PK parameters were calculated from a noncompartmental analysis of the plasma Mn concentrations versus time.
Results: There was a total of 29 AEs reported to all participants of the 2 cohorts. The AEs were mostly of mild to moderate severity and possibly or probably related to the contrast agent. No clinically significant changes in the safety laboratory parameters were reported, except for transiently elevated transaminases observed at the end of the infusion. Clinically relevant low-background MRI scans for clinical visualization of primary breast tumor, liver, and pancreas were obtained at the expanded dose level. Pegfosimer manganese has an initial plasma half-life of approximately 7 minutes.
Conclusion: The FIH study of pegfosimer manganese demonstrated an acceptable safety profile and sufficient contrast enhancement for clinically relevant MRI sequences in participants with primary breast tumors.
{"title":"First-in-Human Safety, Tolerability, Efficacy, and Pharmacokinetics of Pegfosimer Manganese (SN132D) for Contrast-Enhanced MRI of Breast Cancer.","authors":"Fredrik Wärnberg, Oskar Axelsson, Dan Curiac, Paul Hargreaves, Andreas Karakatsanis, Sujinna Lekmeechai, Mats Hansen","doi":"10.1097/RLI.0000000000001164","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001164","url":null,"abstract":"<p><strong>Objectives: </strong>The primary objective of the first-in-human (FIH) study was to evaluate the safety and tolerability of the manganese (Mn)-based contrast agent pegfosimer manganese in participants with newly diagnosed breast cancer, and secondary objectives included preliminary efficacy, and pharmacokinetics (PK) of the agent.</p><p><strong>Methods: </strong>A single intravenous 1-hour infusion of pegfosimer manganese was administered to 2 cohorts; 6 participants at the starting dose of 10 μmol Mn/kg, followed by 8 participants at the expansion dose of 20 μmol Mn/kg, cohorts 1 and 2, respectively. The safety was evaluated based on reported adverse events (AEs), including serious AEs, physical examination, vital signs, electrocardiogram, and safety laboratory parameters. Magnetic resonance imaging (MRI) acquisition was performed precontrast and postcontrast to assess the clinical relevance of images in primary breast tumors, liver, and pancreas relative to reference tissue. PK parameters were calculated from a noncompartmental analysis of the plasma Mn concentrations versus time.</p><p><strong>Results: </strong>There was a total of 29 AEs reported to all participants of the 2 cohorts. The AEs were mostly of mild to moderate severity and possibly or probably related to the contrast agent. No clinically significant changes in the safety laboratory parameters were reported, except for transiently elevated transaminases observed at the end of the infusion. Clinically relevant low-background MRI scans for clinical visualization of primary breast tumor, liver, and pancreas were obtained at the expanded dose level. Pegfosimer manganese has an initial plasma half-life of approximately 7 minutes.</p><p><strong>Conclusion: </strong>The FIH study of pegfosimer manganese demonstrated an acceptable safety profile and sufficient contrast enhancement for clinically relevant MRI sequences in participants with primary breast tumors.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1097/RLI.0000000000001158
Caroline Wilpert, Maximilian Frederic Russe, Jakob Weiss, Christian Voss, Stephan Rau, Ralph Strecker, Marco Reisert, Ricardo Bedin, Horst Urbach, Maxim Zaitsev, Fabian Bamberg, Alexander Rau
Objectives: Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence.
Materials and methods: In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used.
Results: Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise (P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences (P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses.
Conclusions: In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.
{"title":"Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics.","authors":"Caroline Wilpert, Maximilian Frederic Russe, Jakob Weiss, Christian Voss, Stephan Rau, Ralph Strecker, Marco Reisert, Ricardo Bedin, Horst Urbach, Maxim Zaitsev, Fabian Bamberg, Alexander Rau","doi":"10.1097/RLI.0000000000001158","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001158","url":null,"abstract":"<p><strong>Objectives: </strong>Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence.</p><p><strong>Materials and methods: </strong>In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used.</p><p><strong>Results: </strong>Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise (P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences (P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses.</p><p><strong>Conclusions: </strong>In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-07DOI: 10.1097/RLI.0000000000001157
Annette Schwarz, Christian Hofmann, Jannis Dickmann, Arndt Simon, Andreas Maier, Frank K Wacker, Hans-Jürgen Raatschen, Stephan Gleitz, Martina Schmidbauer
Objective: Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients. The study aimed to validate a dedicated triggering algorithm for initiating respiratory-triggered high-pitch computed tomography (RT-HPCT) scans in end inspiration and end expiration in complex and irregular respiratory patterns using an anthropomorphic dynamic chest phantom. Additionally, a patient study was conducted to compare the image quality and lung expansion between RT-HPCT and standard HPCT.
Materials and methods: The study utilized an algorithm that processes the patient's breathing motion in real-time to determine the appropriate time to initiate a scan. This algorithm was tested on a dynamic, tissue-equivalent chest motion phantom to replicate and simulate 3-dimensional target motion using 28 breathing motion patterns taken from patient with irregular breathing. To evaluate the performance on human patients, prospective RT-HPCT was performed in 18 free-breathing patients. As a reference, unenhanced HPCT of the chest was performed in 20 patients without respiratory triggering during free-breathing. The mean CTDI was 1.73 mGy ± 0.1 mGy for HPCT and 1.68 mGy ± 0.1 mGy for RT-HPCT. For phantom tests, the deviation from the target position of the phantom inlay is known. Image quality is approximated by evaluating stationary versus moving acquisitions. For patient scans, respiratory motion artifacts and inspiration depth were analyzed using expert knowledge of lung anatomy and automated lung volume estimation. Statistical analysis was performed to compare image quality and lung volumes between conventional HPCT and RT-HPCT.
Results: In phantom scans, the average deviation from the desired excursion phase was 1.6 mm ± 4.7 mm or 15% ± 24% of the phantom movement range. In patients, the overall image quality significantly improved with respiratory triggering compared with conventional HPCT ( P < 0.001). Quantitative average lung volume was 4.0 L ± 1.1 L in the RT group and 3.6 L ± 1.0 L in the control group.
Conclusions: This study demonstrated the feasibility of using a patient-adaptive respiratory triggering algorithm for high-pitch lung CT in both phantom and patients. Respiratory-triggered high-pitch CT scanning significantly reduces breathing artifacts compared with conventional nontriggered free-breathing scans.
{"title":"Free-Breathing Respiratory Triggered High-Pitch Lung CT: Insights From Phantom and Patient Scans.","authors":"Annette Schwarz, Christian Hofmann, Jannis Dickmann, Arndt Simon, Andreas Maier, Frank K Wacker, Hans-Jürgen Raatschen, Stephan Gleitz, Martina Schmidbauer","doi":"10.1097/RLI.0000000000001157","DOIUrl":"10.1097/RLI.0000000000001157","url":null,"abstract":"<p><strong>Objective: </strong>Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients. The study aimed to validate a dedicated triggering algorithm for initiating respiratory-triggered high-pitch computed tomography (RT-HPCT) scans in end inspiration and end expiration in complex and irregular respiratory patterns using an anthropomorphic dynamic chest phantom. Additionally, a patient study was conducted to compare the image quality and lung expansion between RT-HPCT and standard HPCT.</p><p><strong>Materials and methods: </strong>The study utilized an algorithm that processes the patient's breathing motion in real-time to determine the appropriate time to initiate a scan. This algorithm was tested on a dynamic, tissue-equivalent chest motion phantom to replicate and simulate 3-dimensional target motion using 28 breathing motion patterns taken from patient with irregular breathing. To evaluate the performance on human patients, prospective RT-HPCT was performed in 18 free-breathing patients. As a reference, unenhanced HPCT of the chest was performed in 20 patients without respiratory triggering during free-breathing. The mean CTDI was 1.73 mGy ± 0.1 mGy for HPCT and 1.68 mGy ± 0.1 mGy for RT-HPCT. For phantom tests, the deviation from the target position of the phantom inlay is known. Image quality is approximated by evaluating stationary versus moving acquisitions. For patient scans, respiratory motion artifacts and inspiration depth were analyzed using expert knowledge of lung anatomy and automated lung volume estimation. Statistical analysis was performed to compare image quality and lung volumes between conventional HPCT and RT-HPCT.</p><p><strong>Results: </strong>In phantom scans, the average deviation from the desired excursion phase was 1.6 mm ± 4.7 mm or 15% ± 24% of the phantom movement range. In patients, the overall image quality significantly improved with respiratory triggering compared with conventional HPCT ( P < 0.001). Quantitative average lung volume was 4.0 L ± 1.1 L in the RT group and 3.6 L ± 1.0 L in the control group.</p><p><strong>Conclusions: </strong>This study demonstrated the feasibility of using a patient-adaptive respiratory triggering algorithm for high-pitch lung CT in both phantom and patients. Respiratory-triggered high-pitch CT scanning significantly reduces breathing artifacts compared with conventional nontriggered free-breathing scans.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1097/RLI.0000000000001159
Lukas Jakob Moser, Konstantin Klambauer, Maria Carolina Diaz Machicado, Diana Frey, Victor Mergen, Matthias Eberhard, Tristan Nowak, Bernhard Schmidt, Thomas Flohr, Oliver Distler, Hatem Alkadhi
Purpose: The aim of this study was to determine in a prospective patient study the accuracy of areal bone mineral density (aBMD) measurements with spectral localizer radiographs obtained with a clinical photon-counting detector computed tomography (PCD-CT) scanner in comparison with dual-energy x-ray absorptiometry (DXA).
Methods: In this institutional review board-approved, prospective study, 41 patients (15 females, 26 males; mean age 61.3 years, age range 35-78 years) underwent PCD-CT of the abdomen with a spectral localizer radiograph (tube voltage 140 kVp, tube current 30 mA) and DXA within a median of 45 days. aBMD values were derived for lumbar vertebrae L1-L4 from both methods and were compared with linear regression, Pearson correlation, intraclass correlation coefficients (ICCs), and Bland-Altman plots. T-scores were calculated on a patient level and were compared between methods.
Results: DXA and spectral localizer radiographs showed strong correlation in aBMD measurements (R = 0.97, P < 0.001) and patient level T-scores (R = 0.99, P < 0.001). There was a strong agreement between aBMD from both methods (ICC, 0.96; 95% CI, 0.94-0.97). Bland-Altman analysis revealed a very small mean difference in aBMD between methods (mean absolute error 0.019 g/cm2) with narrow limits of agreement (-0.083 g/cm2 to 0.121 g/cm2). Similarly, there were small differences in regard to the T-score (mean absolute error 0.156) with narrow limits of agreement (-0.422 to 0.734) between methods. ICCs indicated an excellent agreement between T-scores from DXA and spectral localizer radiographs (ICC, 0.98; 95% confidence interval, 0.95-0.99).
Conclusions: Our prospective patient study indicates that spectral localizer radiographs obtained with a clinical PCD-CT system enable accurate quantification of the lumbar bone areal mineral density. This opens up the opportunity for opportunistic screening of osteoporosis in patients who undergo CT for other indications.
{"title":"In Vivo Bone Mineral Density Assessment With Spectral Localizer Radiographs From Photon-Counting Detector CT: Prospective Comparison With DXA.","authors":"Lukas Jakob Moser, Konstantin Klambauer, Maria Carolina Diaz Machicado, Diana Frey, Victor Mergen, Matthias Eberhard, Tristan Nowak, Bernhard Schmidt, Thomas Flohr, Oliver Distler, Hatem Alkadhi","doi":"10.1097/RLI.0000000000001159","DOIUrl":"https://doi.org/10.1097/RLI.0000000000001159","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of this study was to determine in a prospective patient study the accuracy of areal bone mineral density (aBMD) measurements with spectral localizer radiographs obtained with a clinical photon-counting detector computed tomography (PCD-CT) scanner in comparison with dual-energy x-ray absorptiometry (DXA).</p><p><strong>Methods: </strong>In this institutional review board-approved, prospective study, 41 patients (15 females, 26 males; mean age 61.3 years, age range 35-78 years) underwent PCD-CT of the abdomen with a spectral localizer radiograph (tube voltage 140 kVp, tube current 30 mA) and DXA within a median of 45 days. aBMD values were derived for lumbar vertebrae L1-L4 from both methods and were compared with linear regression, Pearson correlation, intraclass correlation coefficients (ICCs), and Bland-Altman plots. T-scores were calculated on a patient level and were compared between methods.</p><p><strong>Results: </strong>DXA and spectral localizer radiographs showed strong correlation in aBMD measurements (R = 0.97, P < 0.001) and patient level T-scores (R = 0.99, P < 0.001). There was a strong agreement between aBMD from both methods (ICC, 0.96; 95% CI, 0.94-0.97). Bland-Altman analysis revealed a very small mean difference in aBMD between methods (mean absolute error 0.019 g/cm2) with narrow limits of agreement (-0.083 g/cm2 to 0.121 g/cm2). Similarly, there were small differences in regard to the T-score (mean absolute error 0.156) with narrow limits of agreement (-0.422 to 0.734) between methods. ICCs indicated an excellent agreement between T-scores from DXA and spectral localizer radiographs (ICC, 0.98; 95% confidence interval, 0.95-0.99).</p><p><strong>Conclusions: </strong>Our prospective patient study indicates that spectral localizer radiographs obtained with a clinical PCD-CT system enable accurate quantification of the lumbar bone areal mineral density. This opens up the opportunity for opportunistic screening of osteoporosis in patients who undergo CT for other indications.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01Epub Date: 2024-07-30DOI: 10.1097/RLI.0000000000001107
Robert Haase, Thomas Pinetz, Erich Kobler, Zeynep Bendella, Christian Gronemann, Daniel Paech, Alexander Radbruch, Alexander Effland, Katerina Deike
Objectives: Reducing gadolinium-based contrast agents to lower costs, the environmental impact of gadolinium-containing wastewater, and patient exposure is still an unresolved issue. Published methods have never been compared. The purpose of this study was to compare the performance of 2 reimplemented state-of-the-art deep learning methods (settings A and B) and a proposed method for contrast signal extraction (setting C) to synthesize artificial T1-weighted full-dose images from corresponding noncontrast and low-dose images.
Materials and methods: In this prospective study, 213 participants received magnetic resonance imaging of the brain between August and October 2021 including low-dose (0.02 mmol/kg) and full-dose images (0.1 mmol/kg). Fifty participants were randomly set aside as test set before training (mean age ± SD, 52.6 ± 15.3 years; 30 men). Artificial and true full-dose images were compared using a reader-based study. Two readers noted all false-positive lesions and scored the overall interchangeability in regard to the clinical conclusion. Using a 5-point Likert scale (0 being the worst), they scored the contrast enhancement of each lesion and its conformity to the respective reference in the true image.
Results: The average counts of false-positives per participant were 0.33 ± 0.93, 0.07 ± 0.33, and 0.05 ± 0.22 for settings A-C, respectively. Setting C showed a significantly higher proportion of scans scored as fully or mostly interchangeable (70/100) than settings A (40/100, P < 0.001) and B (57/100, P < 0.001), and generated the smallest mean enhancement reduction of scored lesions (-0.50 ± 0.55) compared with the true images (setting A: -1.10 ± 0.98; setting B: -0.91 ± 0.67, both P < 0.001). The average scores of conformity of the lesion were 1.75 ± 1.07, 2.19 ± 1.04, and 2.48 ± 0.91 for settings A-C, respectively, with significant differences among all settings (all P < 0.001).
Conclusions: The proposed method for contrast signal extraction showed significant improvements in synthesizing postcontrast images. A relevant proportion of images showing inadequate interchangeability with the reference remains at this dosage.
{"title":"Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction.","authors":"Robert Haase, Thomas Pinetz, Erich Kobler, Zeynep Bendella, Christian Gronemann, Daniel Paech, Alexander Radbruch, Alexander Effland, Katerina Deike","doi":"10.1097/RLI.0000000000001107","DOIUrl":"10.1097/RLI.0000000000001107","url":null,"abstract":"<p><strong>Objectives: </strong>Reducing gadolinium-based contrast agents to lower costs, the environmental impact of gadolinium-containing wastewater, and patient exposure is still an unresolved issue. Published methods have never been compared. The purpose of this study was to compare the performance of 2 reimplemented state-of-the-art deep learning methods (settings A and B) and a proposed method for contrast signal extraction (setting C) to synthesize artificial T1-weighted full-dose images from corresponding noncontrast and low-dose images.</p><p><strong>Materials and methods: </strong>In this prospective study, 213 participants received magnetic resonance imaging of the brain between August and October 2021 including low-dose (0.02 mmol/kg) and full-dose images (0.1 mmol/kg). Fifty participants were randomly set aside as test set before training (mean age ± SD, 52.6 ± 15.3 years; 30 men). Artificial and true full-dose images were compared using a reader-based study. Two readers noted all false-positive lesions and scored the overall interchangeability in regard to the clinical conclusion. Using a 5-point Likert scale (0 being the worst), they scored the contrast enhancement of each lesion and its conformity to the respective reference in the true image.</p><p><strong>Results: </strong>The average counts of false-positives per participant were 0.33 ± 0.93, 0.07 ± 0.33, and 0.05 ± 0.22 for settings A-C, respectively. Setting C showed a significantly higher proportion of scans scored as fully or mostly interchangeable (70/100) than settings A (40/100, P < 0.001) and B (57/100, P < 0.001), and generated the smallest mean enhancement reduction of scored lesions (-0.50 ± 0.55) compared with the true images (setting A: -1.10 ± 0.98; setting B: -0.91 ± 0.67, both P < 0.001). The average scores of conformity of the lesion were 1.75 ± 1.07, 2.19 ± 1.04, and 2.48 ± 0.91 for settings A-C, respectively, with significant differences among all settings (all P < 0.001).</p><p><strong>Conclusions: </strong>The proposed method for contrast signal extraction showed significant improvements in synthesizing postcontrast images. A relevant proportion of images showing inadequate interchangeability with the reference remains at this dosage.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"105-113"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01Epub Date: 2024-07-23DOI: 10.1097/RLI.0000000000001110
Marianna Chaika, Jan M Brendel, Stephan Ursprung, Judith Herrmann, Sebastian Gassenmaier, Andreas Brendlin, Sebastian Werner, Marcel Dominik Nickel, Konstantin Nikolaou, Saif Afat, Haidara Almansour
<p><strong>Objective: </strong>Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-Dixon DL ). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).</p><p><strong>Methods: </strong>This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed.</p><p><strong>Results: </strong>Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-Dixon DL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-Dixon DL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-Dixon DL ( P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-Dixon DL technique ( P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-Dixon DL . Interreader agreement between VIBE-Dixon and VIBE-Dixon DL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXON DL was observed in both the precontrast ( P = 0.025) and postcontrast images ( P < 0.001). Also, an increase of splenic SNR in VIBE-DIXON DL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images ( P = 0.34 and P = 0.003, respec
{"title":"Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression.","authors":"Marianna Chaika, Jan M Brendel, Stephan Ursprung, Judith Herrmann, Sebastian Gassenmaier, Andreas Brendlin, Sebastian Werner, Marcel Dominik Nickel, Konstantin Nikolaou, Saif Afat, Haidara Almansour","doi":"10.1097/RLI.0000000000001110","DOIUrl":"10.1097/RLI.0000000000001110","url":null,"abstract":"<p><strong>Objective: </strong>Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-Dixon DL ). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).</p><p><strong>Methods: </strong>This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed.</p><p><strong>Results: </strong>Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-Dixon DL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-Dixon DL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-Dixon DL ( P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-Dixon DL technique ( P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-Dixon DL . Interreader agreement between VIBE-Dixon and VIBE-Dixon DL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXON DL was observed in both the precontrast ( P = 0.025) and postcontrast images ( P < 0.001). Also, an increase of splenic SNR in VIBE-DIXON DL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images ( P = 0.34 and P = 0.003, respec","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"123-130"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01Epub Date: 2024-07-30DOI: 10.1097/RLI.0000000000001111
Damien Racine, Tilo Niemann, Bence Nemeth, Lucia Gallego Manzano, Hatem Alkadhi, Anaïs Viry, Rahel A Kubik-Huch, Thomas Frauenfelder, André Euler
Objectives: The aim of this study was to evaluate the potential use of simulated radiation doses from a dual-split CT scan for dose optimization by comparing their lesion detectability to dose-matched single-energy CT acquisitions at different radiation dose levels using a mathematical model observer.
Materials and methods: An anthropomorphic abdominal phantom with liver lesions (5-10 mm, both hyperattenuating and hypoattenuating) was imaged using a third-generation dual-source CT in single-energy dual-source mode at 100 kVp and 3 radiation doses (5, 2.5, 1.25 mGy). The tube current was 67% for tube A and 33% for tube B. For each dose, 5 simulated radiation doses (100%, 67%, 55%, 45%, 39%, and 33%) were generated through linear image blending. The phantom was also imaged using traditional single-source single-energy mode at equivalent doses. Each setup was repeated 10 times. Image noise texture was evaluated by the average spatial frequency (f av ) of the noise power spectrum. Liver lesion detection was measured by the area under the receiver operating curve (AUC), using a channelized Hotelling model observer with 10 dense Gaussian channels.
Results: F av decreased at lower radiation doses and differed between simulated and single-energy images (eg, 0.16 mm -1 vs 0.14 mm -1 for simulated and single-energy images at 1.25 mGy), indicating slightly blotchier noise texture for dual-split CT. For hyperattenuating lesions, the mean AUC ranged between 0.92-0.99, 0.81-0.96, and 0.68-0.89 for single-energy, and between 0.91-0.99, 0.78-0.91, and 0.70-0.85 for dual-split at 5 mGy, 2.5 mGy, and 1.25 mGy, respectively. For hypoattenuating lesions, the AUC ranged between 0.90-0.98, 0.75-0.93, and 0.69-0.86 for the single-energy, and between 0.92-0.99, 0.76-0.87, and 0.67-0.81 for dual-split at 5 mGy, 2.5 mGy, and 1.25 mGy, respectively. AUC values were similar between both modes at 5 mGy, and slightly lower, albeit not significantly, for the dual-split mode at 2.5 and 1.25 mGy.
Conclusions: Lesion detectability was comparable between multiple simulated radiation doses from a dual-split CT scan and dose-matched single-energy CT. Noise texture was slightly blotchier in the simulated images. Simulated doses using dual-split CT can be used to assess the impact of radiation dose reduction on lesion detectability without the need for repeated patient scans.
{"title":"Dual-Split CT to Simulate Multiple Radiation Doses From a Single Scan-Liver Lesion Detection Compared With Dose-Matched Single-Energy CT.","authors":"Damien Racine, Tilo Niemann, Bence Nemeth, Lucia Gallego Manzano, Hatem Alkadhi, Anaïs Viry, Rahel A Kubik-Huch, Thomas Frauenfelder, André Euler","doi":"10.1097/RLI.0000000000001111","DOIUrl":"10.1097/RLI.0000000000001111","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study was to evaluate the potential use of simulated radiation doses from a dual-split CT scan for dose optimization by comparing their lesion detectability to dose-matched single-energy CT acquisitions at different radiation dose levels using a mathematical model observer.</p><p><strong>Materials and methods: </strong>An anthropomorphic abdominal phantom with liver lesions (5-10 mm, both hyperattenuating and hypoattenuating) was imaged using a third-generation dual-source CT in single-energy dual-source mode at 100 kVp and 3 radiation doses (5, 2.5, 1.25 mGy). The tube current was 67% for tube A and 33% for tube B. For each dose, 5 simulated radiation doses (100%, 67%, 55%, 45%, 39%, and 33%) were generated through linear image blending. The phantom was also imaged using traditional single-source single-energy mode at equivalent doses. Each setup was repeated 10 times. Image noise texture was evaluated by the average spatial frequency (f av ) of the noise power spectrum. Liver lesion detection was measured by the area under the receiver operating curve (AUC), using a channelized Hotelling model observer with 10 dense Gaussian channels.</p><p><strong>Results: </strong>F av decreased at lower radiation doses and differed between simulated and single-energy images (eg, 0.16 mm -1 vs 0.14 mm -1 for simulated and single-energy images at 1.25 mGy), indicating slightly blotchier noise texture for dual-split CT. For hyperattenuating lesions, the mean AUC ranged between 0.92-0.99, 0.81-0.96, and 0.68-0.89 for single-energy, and between 0.91-0.99, 0.78-0.91, and 0.70-0.85 for dual-split at 5 mGy, 2.5 mGy, and 1.25 mGy, respectively. For hypoattenuating lesions, the AUC ranged between 0.90-0.98, 0.75-0.93, and 0.69-0.86 for the single-energy, and between 0.92-0.99, 0.76-0.87, and 0.67-0.81 for dual-split at 5 mGy, 2.5 mGy, and 1.25 mGy, respectively. AUC values were similar between both modes at 5 mGy, and slightly lower, albeit not significantly, for the dual-split mode at 2.5 and 1.25 mGy.</p><p><strong>Conclusions: </strong>Lesion detectability was comparable between multiple simulated radiation doses from a dual-split CT scan and dose-matched single-energy CT. Noise texture was slightly blotchier in the simulated images. Simulated doses using dual-split CT can be used to assess the impact of radiation dose reduction on lesion detectability without the need for repeated patient scans.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"131-137"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01Epub Date: 2024-08-21DOI: 10.1097/RLI.0000000000001109
Guillaume Fahrni, Sara Boccalini, Allal Mahmoudi, Hugo Lacombe, Angèle Houmeau, Meyer Elbaz, David Rotzinger, Marjorie Villien, Thomas Bochaton, Philippe Douek, Salim A Si-Mohamed
Objective: Development of spectral photon-counting computed tomography (SPCCT) for ultra-high-resolution coronary CT angiography (CCTA) has the potential to accurately evaluate the coronary arteries of very-high-risk patients. The aim of this study was to compare the diagnostic performances of SPCCT against conventional CT for quantifying coronary stenosis in very-high-risk patients, with invasive coronary angiography (ICA) as the reference method.
Materials and methods: In this prospective institutional review board-approved study, very-high-risk patients addressed for ICA following an acute coronary syndrome were consecutively included. CCTA was performed for each patient with both SPCCT and conventional CT before ICA within 3 days. Stenoses were assessed using the minimal diameter over proximal and distal diameters method for CCTA and the quantitative coronary angiography method for ICA. Intraclass correlation coefficients and mean errors were assessed. Sensitivity and specificity were calculated for a >50% diameter stenosis threshold. Reclassification rates for conventional CT and SPCCT were assessed according to CAD-RADS 2.0, using ICA as the gold standard.
Results: Twenty-six coronary stenoses were identified in 26 patients (4 women [15%]; age 64 ± 8 years) with 19 (73%) above 50% and 9 (35%) equal or above 70%. The median stenosis value was 64% (interquartile range, 48%-73%). SPCCT showed a lower mean error (6% [5%, 8%]) than conventional CT (12% [9%, 16%]). SPCCT demonstrated greater sensitivity (100%) and specificity (90%) than conventional CT (75% and 50%, respectively). Ten (38%) stenoses were reclassified with SPCCT and one (4%) with conventional CT.
Conclusions: In very-high-risk patients, ultra-high-resolution SPCCT coronary angiography showed greater accuracy, sensitivity, and specificity, and led to more stenosis reclassifications than conventional CT.
{"title":"Quantification of Coronary Artery Stenosis in Very-High-Risk Patients Using Ultra-High Resolution Spectral Photon-Counting CT.","authors":"Guillaume Fahrni, Sara Boccalini, Allal Mahmoudi, Hugo Lacombe, Angèle Houmeau, Meyer Elbaz, David Rotzinger, Marjorie Villien, Thomas Bochaton, Philippe Douek, Salim A Si-Mohamed","doi":"10.1097/RLI.0000000000001109","DOIUrl":"10.1097/RLI.0000000000001109","url":null,"abstract":"<p><strong>Objective: </strong>Development of spectral photon-counting computed tomography (SPCCT) for ultra-high-resolution coronary CT angiography (CCTA) has the potential to accurately evaluate the coronary arteries of very-high-risk patients. The aim of this study was to compare the diagnostic performances of SPCCT against conventional CT for quantifying coronary stenosis in very-high-risk patients, with invasive coronary angiography (ICA) as the reference method.</p><p><strong>Materials and methods: </strong>In this prospective institutional review board-approved study, very-high-risk patients addressed for ICA following an acute coronary syndrome were consecutively included. CCTA was performed for each patient with both SPCCT and conventional CT before ICA within 3 days. Stenoses were assessed using the minimal diameter over proximal and distal diameters method for CCTA and the quantitative coronary angiography method for ICA. Intraclass correlation coefficients and mean errors were assessed. Sensitivity and specificity were calculated for a >50% diameter stenosis threshold. Reclassification rates for conventional CT and SPCCT were assessed according to CAD-RADS 2.0, using ICA as the gold standard.</p><p><strong>Results: </strong>Twenty-six coronary stenoses were identified in 26 patients (4 women [15%]; age 64 ± 8 years) with 19 (73%) above 50% and 9 (35%) equal or above 70%. The median stenosis value was 64% (interquartile range, 48%-73%). SPCCT showed a lower mean error (6% [5%, 8%]) than conventional CT (12% [9%, 16%]). SPCCT demonstrated greater sensitivity (100%) and specificity (90%) than conventional CT (75% and 50%, respectively). Ten (38%) stenoses were reclassified with SPCCT and one (4%) with conventional CT.</p><p><strong>Conclusions: </strong>In very-high-risk patients, ultra-high-resolution SPCCT coronary angiography showed greater accuracy, sensitivity, and specificity, and led to more stenosis reclassifications than conventional CT.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"114-122"},"PeriodicalIF":7.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142008852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}