Journal of Medical Imaging最新文献

{"title":"Assessment of image quality and establishment of local acceptable quality dose for computed tomography based on patient effective diameter.","authors":"Nada Hasan, Chadia Rizk, Fatema Marzooq, Khalid Khan, Maryam AlKhaja, Esameldeen Babikir","doi":"10.1117/1.JMI.11.4.043502","DOIUrl":"10.1117/1.JMI.11.4.043502","url":null,"abstract":"<p><strong>Purpose: </strong>We aim to develop modified clinical indication (CI)-based image quality scoring criteria (IQSC) for assessing image quality (IQ) and establishing acceptable quality doses (AQDs) in adult computed tomography (CT) examinations, based on CIs and patient sizes.</p><p><strong>Approach: </strong>CT images, volume CT dose index ( <math> <mrow> <msub><mrow><mi>CTDI</mi></mrow> <mrow><mi>vol</mi></mrow> </msub> </mrow> </math> ), and dose length product (DLP) were collected retrospectively between September 2020 and September 2021 for eight common CIs from two CT scanners at a central hospital in the Kingdom of Bahrain. Using the modified CI-based IQSC and a Likert scale (0 to 4), three radiologists assessed the IQ of each examination. AQDs were then established as the median value of <math> <mrow> <msub><mrow><mi>CTDI</mi></mrow> <mrow><mi>vol</mi></mrow> </msub> </mrow> </math> and DLP for images with an average score of 3 and compared to national diagnostic reference levels (NDRLs).</p><p><strong>Results: </strong>Out of 581 examinations, 60 were excluded from the study due to average scores above or below 3. The established AQDs were lower than the NDRLs for all CIs, except <math><mrow><mi>AQDs</mi> <mo>/</mo> <msub><mrow><mi>CTDI</mi></mrow> <mrow><mi>vol</mi></mrow> </msub> </mrow> </math> for oncologic follow-up for large patients (28 versus 26 mGy) in scanner A, besides abdominal pain for medium patients (16 versus 15 mGy) and large patients (34 versus 27 mGy), and diverticulitis/appendicitis for medium patients (15 versus 12 mGy) and large patients (33 versus 30 mGy) in scanner B, indicating the need for optimization.</p><p><strong>Conclusions: </strong>CI-based IQSC is crucial for IQ assessment and establishing AQDs according to patient size. It identifies stations requiring optimization of patient radiation exposure.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"043502"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11328147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001020","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}

{"title":"Use of reporting templates for chest radiographs in a coronavirus disease 2019 context: measuring concordance of radiologists with three international templates.","authors":"Sarah J Lewis, Jayden B Wells, Warren M Reed, Claudia Mello-Thoms, Peter A O'Reilly, Marion Dimigen","doi":"10.1117/1.JMI.11.4.045504","DOIUrl":"https://doi.org/10.1117/1.JMI.11.4.045504","url":null,"abstract":"<p><strong>Purpose: </strong>Reporting templates for chest radiographs (CXRs) for patients presenting or being clinically managed for severe acute respiratory syndrome coronavirus 2 [coronavirus disease 2019 (COVID-19)] has attracted advocacy from international radiology societies. We aim to explore the effectiveness and useability of three international templates through the concordance of, and between, radiologists reporting on the presence and severity of COVID-19 on CXRs.</p><p><strong>Approach: </strong>Seventy CXRs were obtained from a referral hospital, 50 from patients with COVID-19 (30 rated \"classic\" COVID-19 appearance and 20 \"indeterminate\") and 10 \"normal\" and 10 \"alternative pathology\" CXRs. The recruited radiologists were assigned to three test sets with the same CXRs but with different template orders. Each radiologist read their test set three times and assigned a classification to the CXR using the Royal Australian New Zealand College of Radiology (RANZCR), British Society of Thoracic Imaging (BSTI), and Modified COVID-19 Reporting and Data System (Dutch; mCO-RADS) templates. Inter-reader variability and intra-reader variability were measured using Fleiss' kappa coefficient.</p><p><strong>Results: </strong>Twelve Australian radiologists participated. The BSTI template had the highest inter-reader agreement (0.46; \"moderate\" agreement), followed by RANZCR (0.45) and mCO-RADS (0.32). Concordance was driven by strong agreement in \"normal\" and \"alternative\" classifications and was lowest for \"indeterminate.\" General consistency was observed across classifications and templates, with intra-reader variability ranging from \"good\" to \"very good\" for COVID-19 CXRs (0.61), \"normal\" CXRs (0.76), and \"alternative\" (0.68).</p><p><strong>Conclusions: </strong>Reporting templates may be useful in reducing variation among radiology reports, with intra-reader variability showing promise. Feasibility and implementation require a wider approach including referring and treating doctors plus the development of training packages for radiologists specific to the template being used.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"045504"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113460","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}

{"title":"Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data.","authors":"Hinrich Rahlfs, Markus Hüllebrand, Sebastian Schmitter, Christoph Strecker, Andreas Harloff, Anja Hennemuth","doi":"10.1117/1.JMI.11.4.044503","DOIUrl":"10.1117/1.JMI.11.4.044503","url":null,"abstract":"<p><strong>Purpose: </strong>Atherosclerosis of the carotid artery is a major risk factor for stroke. Quantitative assessment of the carotid vessel wall can be based on cross-sections of three-dimensional (3D) black-blood magnetic resonance imaging (MRI). To increase reproducibility, a reliable automatic segmentation in these cross-sections is essential.</p><p><strong>Approach: </strong>We propose an automatic segmentation of the carotid artery in cross-sections perpendicular to the centerline to make the segmentation invariant to the image plane orientation and allow a correct assessment of the vessel wall thickness (VWT). We trained a residual U-Net on eight sparsely sampled cross-sections per carotid artery and evaluated if the model can segment areas that are not represented in the training data. We used 218 MRI datasets of 121 subjects that show hypertension and plaque in the ICA or CCA measuring <math><mrow><mo>≥</mo> <mn>1.5</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> in ultrasound.</p><p><strong>Results: </strong>The model achieves a high mean Dice coefficient of 0.948/0.859 for the vessel's lumen/wall, a low mean Hausdorff distance of <math><mrow><mn>0.417</mn> <mo>/</mo> <mn>0.660</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> , and a low mean average contour distance of <math><mrow><mn>0.094</mn> <mo>/</mo> <mn>0.119</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> on the test set. The model reaches similar results for regions of the carotid artery that are not incorporated in the training set and on MRI of young, healthy subjects. The model also achieves a low median Hausdorff distance of <math><mrow><mn>0.437</mn> <mo>/</mo> <mn>0.552</mn> <mtext>  </mtext> <mi>mm</mi></mrow> </math> on the 2021 Carotid Artery Vessel Wall Segmentation Challenge test set.</p><p><strong>Conclusions: </strong>The proposed method can reduce the effort for carotid artery vessel wall assessment. Together with human supervision, it can be used for clinical applications, as it allows a reliable measurement of the VWT for different patient demographics and MRI acquisition settings.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"044503"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11245174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617377","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}

{"title":"Optimizing neurointerventional procedures: an algorithm for embolization coil detection and automated collimation to enable dose reduction.","authors":"Arpitha Ravi, Philipp Bernhardt, Mathis Hoffmann, Richard Obler, Cuong Nguyen, Andreas Berting, René Chapot, Andreas Maier","doi":"10.1117/1.JMI.11.4.044003","DOIUrl":"10.1117/1.JMI.11.4.044003","url":null,"abstract":"<p><strong>Purpose: </strong>Monitoring radiation dose and time parameters during radiological interventions is crucial, especially in neurointerventional procedures, such as aneurysm treatment with embolization coils. The algorithm presented detects the presence of these embolization coils in medical images. It establishes a bounding box as a reference for automated collimation, with the primary objective being to enhance the efficiency and safety of neurointerventional procedures by actively optimizing image quality while minimizing patient dose.</p><p><strong>Methods: </strong>Two distinct methodologies are evaluated in our study. The first involves deep learning, employing the Faster R-CNN model with a ResNet-50 FPN as a backbone and a RetinaNet model. The second method utilizes a classical blob detection approach, serving as a benchmark for comparison.</p><p><strong>Results: </strong>We performed a fivefold cross-validation, and our top-performing model achieved mean mAP@75 of 0.84 across all folds on validation data and mean mAP@75 of 0.94 on independent test data. Since we use an upscaled bounding box, achieving 100% overlap between ground truth and prediction is not necessary. To highlight the real-world applications of our algorithm, we conducted a simulation featuring a coil constructed from an alloy wire, effectively showcasing the implementation of automatic collimation. This resulted in a notable reduction in the dose area product, signifying the reduction of stochastic risks for both patients and medical staff by minimizing scatter radiation. Additionally, our algorithm assists in avoiding extreme brightness or darkness in X-ray angiography images during narrow collimation, ultimately streamlining the collimation process for physicians.</p><p><strong>Conclusion: </strong>To our knowledge, this marks the initial attempt at an approach successfully detecting embolization coils, showcasing the extended applications of integrating detection results into the X-ray angiography system. The method we present has the potential for broader application, allowing its extension to detect other medical objects utilized in interventional procedures.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"044003"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11259374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735411","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}

{"title":"Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.","authors":"Maksym Sharma, Miranda Kirby, Aaron Fenster, David G McCormack, Grace Parraga","doi":"10.1117/1.JMI.11.4.046001","DOIUrl":"10.1117/1.JMI.11.4.046001","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Our objective was to train machine-learning algorithms on hyperpolarized &lt;math&gt; &lt;mrow&gt; &lt;mmultiscripts&gt;&lt;mrow&gt;&lt;mi&gt;He&lt;/mi&gt;&lt;/mrow&gt; &lt;mprescripts&gt;&lt;/mprescripts&gt; &lt;none&gt;&lt;/none&gt; &lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt; &lt;/mmultiscripts&gt; &lt;/mrow&gt; &lt;/math&gt; magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1 s ( &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;FEV&lt;/mi&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;/mrow&gt; &lt;/math&gt; ) across 3 years.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Approach: &lt;/strong&gt;Hyperpolarized &lt;math&gt; &lt;mrow&gt; &lt;mmultiscripts&gt;&lt;mrow&gt;&lt;mi&gt;He&lt;/mi&gt;&lt;/mrow&gt; &lt;mprescripts&gt;&lt;/mprescripts&gt; &lt;none&gt;&lt;/none&gt; &lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt; &lt;/mmultiscripts&gt; &lt;/mrow&gt; &lt;/math&gt; MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We evaluated 88 ex-smoker participants with &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;31&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;7&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; months follow-up data, 57 of whom (22 females/35 males, &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;70&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;9&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; years) had negligible changes in &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;FEV&lt;/mi&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;/mrow&gt; &lt;/math&gt; and 31 participants (7 females/24 males, &lt;math&gt;&lt;mrow&gt;&lt;mn&gt;68&lt;/mn&gt; &lt;mo&gt;±&lt;/mo&gt; &lt;mn&gt;9&lt;/mn&gt;&lt;/mrow&gt; &lt;/math&gt; years) with worsening &lt;math&gt; &lt;mrow&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;FEV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;mo&gt;≥&lt;/mo&gt; &lt;mn&gt;60&lt;/mn&gt; &lt;mtext&gt;  &lt;/mtext&gt; &lt;mi&gt;mL&lt;/mi&gt; &lt;mo&gt;/&lt;/mo&gt; &lt;mtext&gt;year&lt;/mtext&gt;&lt;/mrow&gt; &lt;/math&gt; . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;FEV&lt;/mi&gt; &lt;mn&gt;1&lt;/mn&gt;&lt;/msub&gt; &lt;/mrow&gt; &lt;/math&gt; decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;For the first time, we have employed hyperpolarized &lt;math&gt; &lt;mrow&gt; &lt;mmultiscripts&gt;&lt;mrow&gt;&lt;mi&gt;He&lt;/mi&gt;&lt;/mrow&gt; &lt;mprescripts&gt;&lt;/mprescripts&gt; &lt;none&gt;&lt;/none&gt; &lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt; &lt;/mmultiscripts&gt; &lt;/mrow&gt; &lt;/math&gt; MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in &lt;math&gt; &lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;FEV","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"046001"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11259551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735410","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}

{"title":"CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.","authors":"Shudong Li, Xiao Jiang, Matthew Tivnan, Grace J Gang, Yuan Shen, J Webster Stayman","doi":"10.1117/1.JMI.11.4.043504","DOIUrl":"10.1117/1.JMI.11.4.043504","url":null,"abstract":"<p><strong>Purpose: </strong>Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model.</p><p><strong>Approach: </strong>We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies.</p><p><strong>Results: </strong>The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols.</p><p><strong>Conclusion: </strong>This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"043504"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113459","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}

{"title":"Capability and reliability of deep learning models to make density predictions on low-dose mammograms.","authors":"Steven Squires, Alistair Mackenzie, Dafydd Gareth Evans, Sacha J Howell, Susan M Astley","doi":"10.1117/1.JMI.11.4.044506","DOIUrl":"10.1117/1.JMI.11.4.044506","url":null,"abstract":"<p><strong>Purpose: </strong>Breast density is associated with the risk of developing cancer and can be automatically estimated using deep learning models from digital mammograms. Our aim is to evaluate the capacity and reliability of such models to predict density from low-dose mammograms taken to enable risk estimates for younger women.</p><p><strong>Approach: </strong>We trained deep learning models on standard-dose and simulated low-dose mammograms. The models were then tested on a mammography dataset with paired standard- and low-dose images. The effect of different factors (including age, density, and dose ratio) on the differences between predictions on standard and low doses is analyzed. Methods to improve performance are assessed, and factors that reduce the model quality are demonstrated.</p><p><strong>Results: </strong>We showed that, although many factors have no significant effect on the quality of low-dose density prediction, both density and breast area have an impact. The correlation between density predictions on low- and standard-dose images of breasts with the largest breast area is 0.985 (0.949 to 0.995), whereas that with the smallest is 0.882 (0.697 to 0.961). We also demonstrated that averaging across craniocaudal-mediolateral oblique (CC-MLO) images and across repeatedly trained models can improve predictive performance.</p><p><strong>Conclusions: </strong>Low-dose mammography can be used to produce density and risk estimates that are comparable to standard-dose images. Averaging across CC-MLO and model predictions should improve this performance. The model quality is reduced when making predictions on denser and smaller breasts.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"044506"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11301609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903210","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}

{"title":"Dose robustness of deep learning models for anatomic segmentation of computed tomography images.","authors":"Artyom Tsanda, Hannes Nickisch, Tobias Wissel, Tobias Klinder, Tobias Knopp, Michael Grass","doi":"10.1117/1.JMI.11.4.044005","DOIUrl":"10.1117/1.JMI.11.4.044005","url":null,"abstract":"<p><strong>Purpose: </strong>The trend towards lower radiation doses and advances in computed tomography (CT) reconstruction may impair the operation of pretrained segmentation models, giving rise to the problem of estimating the dose robustness of existing segmentation models. Previous studies addressing the issue suffer either from a lack of registered low- and full-dose CT images or from simplified simulations.</p><p><strong>Approach: </strong>We employed raw data from full-dose acquisitions to simulate low-dose CT scans, avoiding the need to rescan a patient. The accuracy of the simulation is validated using a real CT scan of a phantom. We consider down to 20% reduction of radiation dose, for which we measure deviations of several pretrained segmentation models from the full-dose prediction. In addition, compatibility with existing denoising methods is considered.</p><p><strong>Results: </strong>The results reveal the surprising robustness of the TotalSegmentator approach, showing minimal differences at the pixel level even without denoising. Less robust models show good compatibility with the denoising methods, which help to improve robustness in almost all cases. With denoising based on a convolutional neural network (CNN), the median Dice between low- and full-dose data does not fall below 0.9 (12 for the Hausdorff distance) for all but one model. We observe volatile results for labels with effective radii less than 19 mm and improved results for contrasted CT acquisitions.</p><p><strong>Conclusion: </strong>The proposed approach facilitates clinically relevant analysis of dose robustness for human organ segmentation models. The results outline the robustness properties of a diverse set of models. Further studies are needed to identify the robustness of approaches for lesion segmentation and to rank the factors contributing to dose robustness.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"044005"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890472","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}

{"title":"Highlights from JMI Issue 4.","authors":"Bennett Landman","doi":"10.1117/1.JMI.11.4.040101","DOIUrl":"https://doi.org/10.1117/1.JMI.11.4.040101","url":null,"abstract":"<p><p>The editorial discusses highlights from JMI Issue 4.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"040101"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126996","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}

{"title":"Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected <i>in vivo</i> lesions.","authors":"Daniel D Liang, David D Liang, Marc J Pomeroy, Yongfeng Gao, Licheng R Kuo, Lihong C Li","doi":"10.1117/1.JMI.11.4.044501","DOIUrl":"10.1117/1.JMI.11.4.044501","url":null,"abstract":"<p><strong>Purpose: </strong>Medical imaging-based machine learning (ML) for computer-aided diagnosis of <i>in vivo</i> lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.</p><p><strong>Approach: </strong>Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions' tissue pathological reports were used as the learning labels.</p><p><strong>Results: </strong>Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value.</p><p><strong>Conclusions: </strong>The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 4","pages":"044501"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11234229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591735","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}