Pub Date : 2023-09-05eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1225215
Wenhui Zhang, Surajit Ray
With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, such as intensity modulated radiation therapy (IMRT) dose painting, demand the precise delineation of multiple intensity value contours to ensure optimal tumour dose distribution. Recently, convolutional neural networks (CNNs) have made significant strides in 3D image segmentation tasks, most of which present the output map at a voxel-wise level. However, because of information loss in subsequent downsampling layers, they frequently fail to precisely identify precise object boundaries. Moreover, in the context of dose painting strategies, there is an imperative need for reliable and precise image segmentation techniques to delineate high recurrence-risk contours. To address these challenges, we introduce a 3D coarse-to-fine framework, integrating a CNN with a kernel smoothing-based probability volume contour approach (KsPC). This integrated approach generates contour-based segmentation volumes, mimicking expert-level precision and providing accurate probability contours crucial for optimizing dose painting/IMRT strategies. Our final model, named KsPC-Net, leverages a CNN backbone to automatically learn parameters in the kernel smoothing process, thereby obviating the need for user-supplied tuning parameters. The 3D KsPC-Net exploits the strength of KsPC to simultaneously identify object boundaries and generate corresponding probability volume contours, which can be trained within an end-to-end framework. The proposed model has demonstrated promising performance, surpassing state-of-the-art models when tested against the MICCAI 2021 challenge dataset (HECKTOR).
{"title":"From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images.","authors":"Wenhui Zhang, Surajit Ray","doi":"10.3389/fradi.2023.1225215","DOIUrl":"https://doi.org/10.3389/fradi.2023.1225215","url":null,"abstract":"<p><p>With the increasing integration of functional imaging techniques like Positron Emission Tomography (PET) into radiotherapy (RT) practices, a paradigm shift in cancer treatment methodologies is underway. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, novel tumour control methods, such as intensity modulated radiation therapy (IMRT) dose painting, demand the precise delineation of multiple intensity value contours to ensure optimal tumour dose distribution. Recently, convolutional neural networks (CNNs) have made significant strides in 3D image segmentation tasks, most of which present the output map at a voxel-wise level. However, because of information loss in subsequent downsampling layers, they frequently fail to precisely identify precise object boundaries. Moreover, in the context of dose painting strategies, there is an imperative need for reliable and precise image segmentation techniques to delineate high recurrence-risk contours. To address these challenges, we introduce a 3D coarse-to-fine framework, integrating a CNN with a kernel smoothing-based probability volume contour approach (KsPC). This integrated approach generates contour-based segmentation volumes, mimicking expert-level precision and providing accurate probability contours crucial for optimizing dose painting/IMRT strategies. Our final model, named KsPC-Net, leverages a CNN backbone to automatically learn parameters in the kernel smoothing process, thereby obviating the need for user-supplied tuning parameters. The 3D KsPC-Net exploits the strength of KsPC to simultaneously identify object boundaries and generate corresponding probability volume contours, which can be trained within an end-to-end framework. The proposed model has demonstrated promising performance, surpassing state-of-the-art models when tested against the MICCAI 2021 challenge dataset (HECKTOR).</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41155957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1168901
Chaowei Wu, Nan Wang, Srinivas Gaddam, Lixia Wang, Hui Han, Kyunghyun Sung, Anthony G Christodoulou, Yibin Xie, Stephen Pandol, Debiao Li
Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated.
Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well.
Results: The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant , fractional extravascular extracellular volume , and rate constant ) achieved intraclass correlation coefficient and R2 between 0.84-0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas.
Discussion: Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.
{"title":"Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study.","authors":"Chaowei Wu, Nan Wang, Srinivas Gaddam, Lixia Wang, Hui Han, Kyunghyun Sung, Anthony G Christodoulou, Yibin Xie, Stephen Pandol, Debiao Li","doi":"10.3389/fradi.2023.1168901","DOIUrl":"https://doi.org/10.3389/fradi.2023.1168901","url":null,"abstract":"<p><strong>Introduction: </strong>Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated.</p><p><strong>Method: </strong>Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well.</p><p><strong>Results: </strong>The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant <math><msup><mi>K</mi><mrow><mrow><mi>trans</mi></mrow></mrow></msup></math>, fractional extravascular extracellular volume <math><msub><mi>v</mi><mi>e</mi></msub></math>, and rate constant <math><msub><mi>k</mi><mrow><mrow><mi>ep</mi></mrow></mrow></msub></math>) achieved intraclass correlation coefficient and <i>R</i><sup>2</sup> between 0.84-0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas.</p><p><strong>Discussion: </strong>Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41168695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1240544
Bino A Varghese, Brandon K K Fields, Darryl H Hwang, Vinay A Duddalwar, George R Matcuk, Steven Y Cen
To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.
{"title":"Spatial assessments in texture analysis: what the radiologist needs to know.","authors":"Bino A Varghese, Brandon K K Fields, Darryl H Hwang, Vinay A Duddalwar, George R Matcuk, Steven Y Cen","doi":"10.3389/fradi.2023.1240544","DOIUrl":"10.3389/fradi.2023.1240544","url":null,"abstract":"<p><p>To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10225205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-08eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1241651
Joseph M Rich, Lokesh N Bhardwaj, Aman Shah, Krish Gangal, Mohitha S Rapaka, Assad A Oberai, Brandon K K Fields, George R Matcuk, Vinay A Duddalwar
Introduction: Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).
Method: The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.
Results: The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9.
Discussion: Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.
简介图像分割是量化恶性骨病变特征的重要过程,但对于放射科医生来说,这项任务既具有挑战性又费力。深度学习在放射学图像自动分割方面大有可为,包括恶性骨病变。本综述旨在研究计算机断层扫描(CT)、磁共振成像(MRI)和正电子发射断层扫描/CT(PET/CT)上基于深度学习的恶性骨病变图像分割方法:根据系统综述和元分析首选报告项目(Preferred Reporting Items for Systematic Reviews and Meta-Analyses,PRISMA)指南,在PubMed、Embase、Web of Science和Scopus电子数据库中对基于深度学习的CT和MRI恶性骨病变图像分割进行了文献检索。共有41篇发表于2017年2月至2023年3月期间的原创文章被纳入综述:大多数论文研究的是 MRI,其次是 CT、PET/CT 和 PET/MRI。研究原发性与继发性恶性肿瘤以及利用三维与二维数据的论文分布相对均匀。许多论文利用定制模型作为 U-Net 的修改或变体。最常用的评估指标是骰子相似系数(DSC)。大多数模型的骰子相似系数都在 0.6 以上,所有成像模式的中位数都在 0.85-0.9 之间:深度学习方法在分割 CT、MRI 和 PET/CT 上的恶性骨质病变方面表现出良好的能力。为帮助提高性能,通常采用的一些策略包括数据增强、利用大型公共数据集、预处理(包括去噪和裁剪)以及 U-Net 架构修改。未来的研究方向包括克服数据集和注释的同质性,以及临床应用的通用性。
{"title":"Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.","authors":"Joseph M Rich, Lokesh N Bhardwaj, Aman Shah, Krish Gangal, Mohitha S Rapaka, Assad A Oberai, Brandon K K Fields, George R Matcuk, Vinay A Duddalwar","doi":"10.3389/fradi.2023.1241651","DOIUrl":"10.3389/fradi.2023.1241651","url":null,"abstract":"<p><strong>Introduction: </strong>Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT).</p><p><strong>Method: </strong>The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review.</p><p><strong>Results: </strong>The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9.</p><p><strong>Discussion: </strong>Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10069334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-02eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1144004
Yitong Yang, Zahraw Shah, Athira J Jacob, Jackson Hair, Teodora Chitiboi, Tiziano Passerini, Jerome Yerly, Lorenzo Di Sopra, Davide Piccini, Zahra Hosseini, Puneet Sharma, Anurag Sahu, Matthias Stuber, John N Oshinski
Introduction: Deep learning (DL)-based segmentation has gained popularity for routine cardiac magnetic resonance (CMR) image analysis and in particular, delineation of left ventricular (LV) borders for LV volume determination. Free-breathing, self-navigated, whole-heart CMR exams provide high-resolution, isotropic coverage of the heart for assessment of cardiac anatomy including LV volume. The combination of whole-heart free-breathing CMR and DL-based LV segmentation has the potential to streamline the acquisition and analysis of clinical CMR exams. The purpose of this study was to compare the performance of a DL-based automatic LV segmentation network trained primarily on computed tomography (CT) images in two whole-heart CMR reconstruction methods: (1) an in-line respiratory motion-corrected (Mcorr) reconstruction and (2) an off-line, compressed sensing-based, multi-volume respiratory motion-resolved (Mres) reconstruction. Given that Mres images were shown to have greater image quality in previous studies than Mcorr images, we hypothesized that the LV volumes segmented from Mres images are closer to the manual expert-traced left ventricular endocardial border than the Mcorr images.
Method: This retrospective study used 15 patients who underwent clinically indicated 1.5 T CMR exams with a prototype ECG-gated 3D radial phyllotaxis balanced steady state free precession (bSSFP) sequence. For each reconstruction method, the absolute volume difference (AVD) of the automatically and manually segmented LV volumes was used as the primary quantity to investigate whether 3D DL-based LV segmentation generalized better on Mcorr or Mres 3D whole-heart images. Additionally, we assessed the 3D Dice similarity coefficient between the manual and automatic LV masks of each reconstructed 3D whole-heart image and the sharpness of the LV myocardium-blood pool interface. A two-tail paired Student's t-test (alpha = 0.05) was used to test the significance in this study.
Results & discussion: The AVD in the respiratory Mres reconstruction was lower than the AVD in the respiratory Mcorr reconstruction: 7.73 ± 6.54 ml vs. 20.0 ± 22.4 ml, respectively (n = 15, p-value = 0.03). The 3D Dice coefficient between the DL-segmented masks and the manually segmented masks was higher for Mres images than for Mcorr images: 0.90 ± 0.02 vs. 0.87 ± 0.03 respectively, with a p-value = 0.02. Sharpness on Mres images was higher than on Mcorr images: 0.15 ± 0.05 vs. 0.12 ± 0.04, respectively, with a p-value of 0.014 (n = 15).
Conclusion: We conclude that the DL-based 3D automatic LV segmentation network trained on CT images and fine-tuned on MR images generalized better on Mres images than on Mcorr images for quantifying LV volumes.
{"title":"Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions.","authors":"Yitong Yang, Zahraw Shah, Athira J Jacob, Jackson Hair, Teodora Chitiboi, Tiziano Passerini, Jerome Yerly, Lorenzo Di Sopra, Davide Piccini, Zahra Hosseini, Puneet Sharma, Anurag Sahu, Matthias Stuber, John N Oshinski","doi":"10.3389/fradi.2023.1144004","DOIUrl":"10.3389/fradi.2023.1144004","url":null,"abstract":"<p><strong>Introduction: </strong>Deep learning (DL)-based segmentation has gained popularity for routine cardiac magnetic resonance (CMR) image analysis and in particular, delineation of left ventricular (LV) borders for LV volume determination. Free-breathing, self-navigated, whole-heart CMR exams provide high-resolution, isotropic coverage of the heart for assessment of cardiac anatomy including LV volume. The combination of whole-heart free-breathing CMR and DL-based LV segmentation has the potential to streamline the acquisition and analysis of clinical CMR exams. The purpose of this study was to compare the performance of a DL-based automatic LV segmentation network trained primarily on computed tomography (CT) images in two whole-heart CMR reconstruction methods: (1) an in-line respiratory motion-corrected (Mcorr) reconstruction and (2) an off-line, compressed sensing-based, multi-volume respiratory motion-resolved (Mres) reconstruction. Given that Mres images were shown to have greater image quality in previous studies than Mcorr images, we <i>hypothesized</i> that the LV volumes segmented from Mres images are closer to the manual expert-traced left ventricular endocardial border than the Mcorr images.</p><p><strong>Method: </strong>This retrospective study used 15 patients who underwent clinically indicated 1.5 T CMR exams with a prototype ECG-gated 3D radial phyllotaxis balanced steady state free precession (bSSFP) sequence. For each reconstruction method, the absolute volume difference (AVD) of the automatically and manually segmented LV volumes was used as the primary quantity to investigate whether 3D DL-based LV segmentation generalized better on Mcorr or Mres 3D whole-heart images. Additionally, we assessed the 3D Dice similarity coefficient between the manual and automatic LV masks of each reconstructed 3D whole-heart image and the sharpness of the LV myocardium-blood pool interface. A two-tail paired Student's <i>t</i>-test (alpha = 0.05) was used to test the significance in this study.</p><p><strong>Results & discussion: </strong>The AVD in the respiratory Mres reconstruction was lower than the AVD in the respiratory Mcorr reconstruction: 7.73 ± 6.54 ml vs. 20.0 ± 22.4 ml, respectively (<i>n</i> = 15, <i>p</i>-value = 0.03). The 3D Dice coefficient between the DL-segmented masks and the manually segmented masks was higher for Mres images than for Mcorr images: 0.90 ± 0.02 vs. 0.87 ± 0.03 respectively, with a <i>p</i>-value = 0.02. Sharpness on Mres images was higher than on Mcorr images: 0.15 ± 0.05 vs. 0.12 ± 0.04, respectively, with a <i>p</i>-value of 0.014 (<i>n</i> = 15).</p><p><strong>Conclusion: </strong>We conclude that the DL-based 3D automatic LV segmentation network trained on CT images and fine-tuned on MR images generalized better on Mres images than on Mcorr images for quantifying LV volumes.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10234001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-20eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1151258
Eros Montin, Richard Kijowski, Thomas Youm, Riccardo Lattanzi
Introduction: Femoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images.
Material and methods: 17 patients diagnosed with monolateral FAI underwent pre-surgical MR imaging, including a 3D Dixon sequence of the pelvis. An expert radiologist drew regions of interest on the water-only Dixon images outlining femur and acetabulum in both impingement (IJ) and healthy joints (HJ). 182 radiomic features were extracted for each hip. The dataset numerosity was increased by 60 times with an ad-hoc data augmentation tool. Features were subdivided by type and region in 24 subsets. For each, a univariate ANOVA F-value analysis was applied to find the 5 features most correlated with IJ based on p-value, for a total of 48 subsets. For each subset, a K-nearest neighbor model was trained to differentiate between IJ and HJ using the values of the radiomic features in the subset as input. The training was repeated 100 times, randomly subdividing the data with 75%/25% training/testing.
Results: The texture-based gray level features yielded the highest prediction max accuracy (0.972) with the smallest subset of features. This suggests that the gray image values are more homogeneously distributed in the HJ in comparison to IJ, which could be due to stress-related inflammation resulting from impingement.
Conclusions: We showed that radiomics can automatically distinguish IJ from HJ using water-only Dixon MRI. To our knowledge, this is the first application of radiomics for FAI diagnosis. We reported an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Our proposed radiomic analysis could be combined with methods for automated joint segmentation to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology.
{"title":"A radiomics approach to the diagnosis of femoroacetabular impingement.","authors":"Eros Montin, Richard Kijowski, Thomas Youm, Riccardo Lattanzi","doi":"10.3389/fradi.2023.1151258","DOIUrl":"10.3389/fradi.2023.1151258","url":null,"abstract":"<p><strong>Introduction: </strong>Femoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images.</p><p><strong>Material and methods: </strong>17 patients diagnosed with monolateral FAI underwent pre-surgical MR imaging, including a 3D Dixon sequence of the pelvis. An expert radiologist drew regions of interest on the water-only Dixon images outlining femur and acetabulum in both impingement (IJ) and healthy joints (HJ). 182 radiomic features were extracted for each hip. The dataset numerosity was increased by 60 times with an ad-hoc data augmentation tool. Features were subdivided by type and region in 24 subsets. For each, a univariate ANOVA <i>F</i>-value analysis was applied to find the 5 features most correlated with IJ based on <i>p</i>-value, for a total of 48 subsets. For each subset, a K-nearest neighbor model was trained to differentiate between IJ and HJ using the values of the radiomic features in the subset as input. The training was repeated 100 times, randomly subdividing the data with 75%/25% training/testing.</p><p><strong>Results: </strong>The texture-based gray level features yielded the highest prediction max accuracy (0.972) with the smallest subset of features. This suggests that the gray image values are more homogeneously distributed in the HJ in comparison to IJ, which could be due to stress-related inflammation resulting from impingement.</p><p><strong>Conclusions: </strong>We showed that radiomics can automatically distinguish IJ from HJ using water-only Dixon MRI. To our knowledge, this is the first application of radiomics for FAI diagnosis. We reported an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Our proposed radiomic analysis could be combined with methods for automated joint segmentation to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10233997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-30eCollection Date: 2023-01-01DOI: 10.3389/fradi.2023.1112841
Ahmed Maiter, Mahan Salehi, Andrew J Swift, Samer Alabed
Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies-a systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine 9:956811). 209 studies were assessed for compliance with the Checklist for AI in Medical Imaging (CLAIM), a framework for reporting. We found variable-and sometimes poor-quality of reporting and identified significant and frequently missing information in publications. Compliance with CLAIM was high for descriptions of models (100%, IQR 80%-100%), but lower than expected for descriptions of study design (71%, IQR 63-86%), datasets used in training and testing (63%, IQR 50%-67%) and model performance (60%, IQR 50%-70%). Here, we present a summary of our key findings, aimed at general readers who may not be experts in AI, and use them as a framework to discuss the factors determining quality of reporting, making recommendations for improving the reporting of research in this field. We aim to assist researchers in presenting their work and readers in their appraisal of evidence. Finally, we emphasise the need for close scrutiny of studies presenting AI tools, even in the face of the excitement surrounding AI in cardiac imaging.
{"title":"How should studies using AI be reported? lessons from a systematic review in cardiac MRI.","authors":"Ahmed Maiter, Mahan Salehi, Andrew J Swift, Samer Alabed","doi":"10.3389/fradi.2023.1112841","DOIUrl":"10.3389/fradi.2023.1112841","url":null,"abstract":"<p><p>Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies-a systematic review and recommendations for future studies. <i>Frontiers in Cardiovascular Medicine</i> 9:956811). 209 studies were assessed for compliance with the Checklist for AI in Medical Imaging (CLAIM), a framework for reporting. We found variable-and sometimes poor-quality of reporting and identified significant and frequently missing information in publications. Compliance with CLAIM was high for descriptions of models (100%, IQR 80%-100%), but lower than expected for descriptions of study design (71%, IQR 63-86%), datasets used in training and testing (63%, IQR 50%-67%) and model performance (60%, IQR 50%-70%). Here, we present a summary of our key findings, aimed at general readers who may not be experts in AI, and use them as a framework to discuss the factors determining quality of reporting, making recommendations for improving the reporting of research in this field. We aim to assist researchers in presenting their work and readers in their appraisal of evidence. Finally, we emphasise the need for close scrutiny of studies presenting AI tools, even in the face of the excitement surrounding AI in cardiac imaging.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10364997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10252059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.3389/fradi.2023.1242902
Patrick Debs, Laura M Fayad
With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.
{"title":"The promise and limitations of artificial intelligence in musculoskeletal imaging.","authors":"Patrick Debs, Laura M Fayad","doi":"10.3389/fradi.2023.1242902","DOIUrl":"https://doi.org/10.3389/fradi.2023.1242902","url":null,"abstract":"<p><p>With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10048687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.3389/fradi.2023.1167901
Simone Bongiovanni, Marco Bozzolo, Simone Amabile, Enrico Peano, Alberto Balderi
Acute mesenteric ischemia (AMI) is a severe medical condition defined by insufficient vascular supply to the small bowel through mesenteric vessels, resulting in necrosis and eventual gangrene of bowel walls. We present the case of a 64-year-old man with recrudescence of prolonged epigastric pain at rest of few hours duration, cold sweating and episodes of vomiting. A computed tomography scan of his abdomen revealed multiple filling defects in the mid-distal part of the superior mesenteric artery (SMA) and the proximal part of jejunal branches, associated with small intestine walls thickening, suggesting SMA thromboembolism and initial intestinal ischemia. Considering the absence of signs of peritonitis at the abdominal examination and the presence of multiple arterial emboli was decided to perform an endovascular treatment with ultrasound assisted catheter-directed thrombolysis with EkoSonic Endovascular System-EKOS, which resulted in complete dissolution of the multiple emboli and improved blood flow into the intestine wall. The day after the procedure the patient's pain improved significantly and 5 days after he was discharged home asymptomatic on warfarin anticoagulation. After 1 year of follow-up the patient is fine with no further episodes of mesenteric ischemia or other embolisms.
{"title":"Case report: ultrasound assisted catheter directed thrombolysis of an embolic partial occlusion of the superior mesenteric artery.","authors":"Simone Bongiovanni, Marco Bozzolo, Simone Amabile, Enrico Peano, Alberto Balderi","doi":"10.3389/fradi.2023.1167901","DOIUrl":"https://doi.org/10.3389/fradi.2023.1167901","url":null,"abstract":"<p><p>Acute mesenteric ischemia (AMI) is a severe medical condition defined by insufficient vascular supply to the small bowel through mesenteric vessels, resulting in necrosis and eventual gangrene of bowel walls. We present the case of a 64-year-old man with recrudescence of prolonged epigastric pain at rest of few hours duration, cold sweating and episodes of vomiting. A computed tomography scan of his abdomen revealed multiple filling defects in the mid-distal part of the superior mesenteric artery (SMA) and the proximal part of jejunal branches, associated with small intestine walls thickening, suggesting SMA thromboembolism and initial intestinal ischemia. Considering the absence of signs of peritonitis at the abdominal examination and the presence of multiple arterial emboli was decided to perform an endovascular treatment with ultrasound assisted catheter-directed thrombolysis with EkoSonic Endovascular System-EKOS, which resulted in complete dissolution of the multiple emboli and improved blood flow into the intestine wall. The day after the procedure the patient's pain improved significantly and 5 days after he was discharged home asymptomatic on warfarin anticoagulation. After 1 year of follow-up the patient is fine with no further episodes of mesenteric ischemia or other embolisms.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10252060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.3389/fradi.2023.1088068
Ricardo Bigolin Lanfredi, Joyce D Schroeder, Tolga Tasdizen
Convolutional neural networks (CNNs) have been successfully applied to chest x-ray (CXR) images. Moreover, annotated bounding boxes have been shown to improve the interpretability of a CNN in terms of localizing abnormalities. However, only a few relatively small CXR datasets containing bounding boxes are available, and collecting them is very costly. Opportunely, eye-tracking (ET) data can be collected during the clinical workflow of a radiologist. We use ET data recorded from radiologists while dictating CXR reports to train CNNs. We extract snippets from the ET data by associating them with the dictation of keywords and use them to supervise the localization of specific abnormalities. We show that this method can improve a model's interpretability without impacting its image-level classification.
{"title":"Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation.","authors":"Ricardo Bigolin Lanfredi, Joyce D Schroeder, Tolga Tasdizen","doi":"10.3389/fradi.2023.1088068","DOIUrl":"https://doi.org/10.3389/fradi.2023.1088068","url":null,"abstract":"<p><p>Convolutional neural networks (CNNs) have been successfully applied to chest x-ray (CXR) images. Moreover, annotated bounding boxes have been shown to improve the interpretability of a CNN in terms of localizing abnormalities. However, only a few relatively small CXR datasets containing bounding boxes are available, and collecting them is very costly. Opportunely, eye-tracking (ET) data can be collected during the clinical workflow of a radiologist. We use ET data recorded from radiologists while dictating CXR reports to train CNNs. We extract snippets from the ET data by associating them with the dictation of keywords and use them to supervise the localization of specific abnormalities. We show that this method can improve a model's interpretability without impacting its image-level classification.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9930026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}