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Assessment of first-trimester utero-placental vascular morphology by 3D power Doppler ultrasound image analysis using a skeletonization algorithm: the Rotterdam Periconception Cohort

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HUMAN REPRODUCTION 2022年第11期37卷 2532-2545页

作者： de Vos, Eline S. Koning, Anton H. J. Steegers-Theunissen, Regine P. M. Willemsen, Sten P. van Rijn, Bas B. Steegers, Eric A. P. Mulders, Annemarie G. M. G. J. Erasmus MC Dept Obstet & Gynecol Univ Med Ctr Rotterdam POB 2040 NL-3000 CA Rotterdam Netherlands Erasmus MC Dept Pathol Univ Med Ctr Rotterdam Rotterdam Netherlands Erasmus MC Dept Biostat Univ Med Ctr Rotterdam Rotterdam Netherlands

STUDY QUESTION Can three-dimensional (3D) Power Doppler (PD) ultrasound and a skeletonization algorithm be used to assess first-trimester development of the utero-placental vascular morphology? SUMMARY ANSWER The application of 3D PD ultrasonography and a skeletonization algorithm facilitates morphologic assessment of utero-placental vascular development in the first trimester and reveals less advanced vascular morphologic development in pregnancies with placenta-related complications than in pregnancies without placenta-related complications. WHAT IS KNOWN ALREADY Suboptimal development of the utero-placental vasculature is one of the main contributors to the periconceptional origin of placenta-related complications. The nature and attribution of aberrant vascular structure and branching patterns remain unclear, as validated markers monitoring first-trimester utero-placental vascular morphologic development are lacking. STUDY DESIGN, SIZE, DURATION In this prospective observational cohort, 214 ongoing pregnancies were included before 10 weeks gestational age (GA) at a tertiary hospital between January 2017 and July 2018, as a subcohort of the ongoing Rotterdam Periconception Cohort study. PARTICIPANTS/MATERIALS, SETTING, METHODS By combining 3D PD ultrasonography and virtual reality, utero-placental vascular volume (uPVV) measurements were obtained at 7, 9 and 11 weeks GA. A skeletonization algorithm was applied to the uPVV measurements to generate the utero-placental vascular skeleton (uPVS), a network-like structure containing morphologic characteristics of the vasculature. Quantification of vascular morphology was performed by assigning a morphologic characteristic to each voxel in the uPVS (end-, vessel-, bifurcation- or crossing-point) and calculating total vascular network length. A Mann-Whitney U test was performed to investigate differences in morphologic development of the first-trimester utero-placental vasculature between pregnancies with and without pla

关键词： computer-assisted image processing virtual reality ultrasonography placental vascular development preeclampsia

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Clinical feasibility of deep learning-based automatic head CBCT image segmentation and landmark detection in computer- aided surgical simulation for orthognathic surgery

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INTERNATIONAL JOURNAL OF ORAL AND MAXILLOFACIAL SURGERY 2023年第7期52卷 793-800页

作者： Deng, H. H. Liu, Q. Chen, A. Kuang, T. Yuan, P. Gateno, J. Kim, D. Barber, J. C. Xiong, K. G. Yu, P. Gu, K. J. Xu, X. Yan, P. Shen, D. Xia, J. J. Houston Methodist Res Inst Dept Oral & Maxillofacial Surg Houston TX 77030 USA Univ N Carolina Dept Radiol Chapel Hill NC USA Cornell Univ Weill Med Coll Dept Surg Oral & Maxillofacial Surg New York NY USA Rensselaer Polytech Inst Dept Biomed Engn Troy NY USA

The purpose of this ambispective study was to investigate whether deep learning-based automatic segmentation and landmark detection, the SkullEngine, could be used for orthognathic surgical planning. Sixty-one sets of cone beam computed tomography (CBCT) images were automatically inferred for midface, mandible, upper and lower teeth, and 68 landmarks. The experimental group included automatic segmentation and landmarks, while the control group included manual ones that were previously used to plan orthognathic surgery. The qualitative analysis of segmentation showed that all of the automatic results could be used for computeraided surgical simulation. Among these, 98.4% of midface, 70.5% of mandible, 98.4% of upper teeth, and 93.4% of lower teeth could be directly used without manual revision. The Dice similarity coefficient was 96% and the average symmetric surface distance was 0.1 mm for all four structures. With SkullEngine, it took 4 minutes to complete the automatic segmentation and an additional 10 minutes for a manual touchup. The results also showed the overall mean difference between the two groups was 2.3 mm for the midface and 2.4 mm for the mandible. In summary, the authors believe that automatic segmentation using SkullEngine is ready for daily practice. However, the accuracy of automatic landmark digitization needs to be improved.

关键词： Feasibility studies Deep learning computer-assisted image processing Algorithms computer-assisted Three-Dimensional Imaging Cone-beam computed tomography Orthognathic surgery computer-aided surgery

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image registration improves human knee cartilage T1 mapping with delayed gadolinium-enhanced MRI of cartilage (dGEMRIC)

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EUROPEAN RADIOLOGY 2013年第1期23卷 246-252页

作者： Bron, Esther E. van Tiel, Jasper Smit, Henk Poot, Dirk H. J. Niessen, Wiro J. Krestin, Gabriel P. Weinans, Harrie Oei, Edwin H. G. Kotek, Gyula Klein, Stefan Univ Med Ctr Rotterdam Erasmus MC Biomed Imaging Grp Rotterdam Dept Med Informat NL-3000 CA Rotterdam Netherlands Univ Med Ctr Rotterdam Erasmus MC Biomed Imaging Grp Rotterdam Dept Radiol NL-3000 CA Rotterdam Netherlands Erasmus MC Dept Orthopaed Surg Rotterdam Netherlands Delft Univ Technol Dept Appl Sci Delft Netherlands Delft Univ Technol Dept Biomech Engn Delft Netherlands

To evaluate the effect of automated registration in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of the knee on the occurrence of movement artefacts on the T1 map and the reproducibility of region-of-interest (ROI)-based measurements. Eleven patients with early-stage knee osteoarthritis and ten healthy controls underwent dGEMRIC twice at 3 T. Controls underwent unenhanced imaging. ROIs were manually drawn on the femoral and tibial cartilage. T1 calculation was performed with and without registration of the T1-weighted images. Automated three-dimensional rigid registration was performed on the femur and tibia cartilage separately. Registration quality was evaluated using the square root Cram,r-Rao lower bound (CRLB sigma). Additionally, the reproducibility of dGEMRIC was assessed by comparing automated registration with manual slice-matching. Automated registration of the T1-weighted images improved the T1 maps as the 90% percentile of the CRLB sigma was significantly (P < 0.05) reduced with a median reduction of 55.8 ms (patients) and 112.9 ms (controls). Manual matching and automated registration of the re-imaged T1 map gave comparable intraclass correlation coefficients of respectively 0.89/0.90 (patients) and 0.85/0.85 (controls). Registration in dGEMRIC reduces movement artefacts on T1 maps and provides a good alternative to manual slice-matching in longitudinal studies.

关键词： Magnetic resonance imaging Articular cartilage Knee osteoarthritis computer-assisted image processing Reproducibility of results

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Metallic dental artifact reduction in computed tomography (Smart MAR): Improvement of image quality and diagnostic confidence in patients with suspected head and neck pathology and oral implants

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EUROPEAN JOURNAL OF RADIOLOGY 2019年 118卷 153-160页

作者： Feldhaus, Felix Boening, Georg Jonczyk, Martin Kahn, Johannes Fehrenbach, Uli Maurerb, M. Benz, D. Hamm, Bernd Streitparth, Florian Charite Univ Med Berlin Augustenburger Pl 1 D-13353 Berlin Germany Free Univ Berlin Augustenburger Pl 1 D-13353 Berlin Germany Humboldt Univ Augustenburger Pl 1 D-13353 Berlin Germany Berlin Inst Hlth Dept Radiol Augustenburger Pl 1 D-13353 Berlin Germany Univ Bern Inselspital Dept Diagnost Intervent & Pediat Radiol CH-3010 Bern Switzerland Univ Jena Dept Radiol Klinikum 1 D-07747 Jena Germany Ludwig Maximilians Univ Munchen Dept Radiol Marchioninistr 15 D-81377 Munich Germany

Purpose: We determined whether the Smart MAR metal artifact reduction tool - a three-stage, projection-based, post processing algorithm - improves subjective and objective image quality and diagnostic confidence in patients with dental artifacts and suspected head and neck pathology compared to standard adaptive statistical iterative reconstructions (ASIR V) alone. Method: The study included 100 consecutive patients with nonremovable oral implants or dental fillings and suspected oropharyngeal cancer or abscess. CT raw data of a single-source multislice CT scanner were post-processed using ASIR V alone and with additional Smart MAR reconstruction. image quality of baseline ASIR V and Smart MAR-based reconstruction series was compared both quantitatively (5 regions of interest, ROIs) and qualitatively (two independent raters). Results: Additional Smart MAR reconstruction significantly seems to improve both attenuation and noise adjacent to implants and in more distant areas (all p < 0.001) compared to standard ASIR V reconstructions alone. Signal-to-noise ratio (SNR;p = 0.001) and contrast-to-noise ratio were improved significantly (CNR;p = 0.001). Smart MAR improved visualization of tumor/abscess (detected in 36 of 100 patients, 36%) and representative oropharyngeal tissue (p < 0.001). In 8 of 36 patients (22%), tumor was only detected in Smart MAR series. Mean total DLP was 506.8mGy*cm;average CTDIvol was 5.5 mGy. Conclusions: The supplementary use of the Smart MAR post-processing tool seems to significantly improve both subjective and objective image quality as well as diagnostic confidence and lesion detection in CT of the head and neck. In 22% of cases, the tumor was detected only in Smart MAR reconstructed images.

关键词： computer-assisted image processing Artifacts Neck Squamous cell carcinoma

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image Fusion of Preprocedural CTA with Real-time Fluoroscopy to Guide Proper Hepatic Artery Catheterization During Transarterial Chemoembolization of Hepatocellular Carcinoma: A Feasibility Study

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CARDIOVASCULAR AND INTERVENTIONAL RADIOLOGY 2013年第2期36卷 526-530页

作者： Bargellini, Irene Turini, Francesca Bozzi, Elena Lauretti, Dario Cicorelli, Antonio Lunardi, Alessandro Cioni, Roberto Bartolozzi, Carlo Univ Pisa Dept Diagnost & Intervent Radiol I-56100 Pisa Italy

To assess feasibility of proper hepatic artery catheterization using a 3D model obtained from preprocedural computed tomographic angiography (CTA), fused with real-time fluoroscopy, during transarterial chemoembolization of hepatocellular carcinoma. Twenty consecutive cirrhotic patients with hepatocellular carcinoma undergoing transarterial chemoembolization were prospectively enrolled onto the study. The early arterial phase axial images of the preprocedural CTA were postprocessed on an independent workstation connected to the angiographic system (Innova 4100;GE Healthcare, Milwaukee, WI), obtaining a 3D volume rendering image (VR) that included abdominal aorta, splanchnic arteries, and first and second lumbar vertebrae. The VR image was manually registered to the real-time X-ray fluoroscopy, with the lumbar spine used as the reference. The VR image was then used as guidance to selectively catheterize the proper hepatic artery. The procedure was considered successful when performed with no need for intraarterial contrast injections or angiographic acquisitions. The procedure was successful in 19 (95 %) of 20 patients. In one patient, celiac trunk angiography was required for the presence of a significant ostial stenosis that was underestimated at computed tomography. Time for image reconstruction and registration was < 10 min in all cases. The use of preprocedural CTA model with fluoroscopy enables confident and direct catheterization of the proper hepatic artery with no need for preliminary celiac trunk angiography, thus reducing radiation exposure and contrast media administration.

关键词： Catheterization Chemoembolization Computed tomography computer-assisted image processing Fluoroscopy Hepatocellular carcinoma

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Demystification of AI-driven medical image interpretation: past, present and future

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EUROPEAN RADIOLOGY 2019年第3期29卷 1616-1624页

作者： Savadjiev, Peter Chong, Jaron Dohan, Anthony Vakalopoulou, Maria Reinhold, Caroline Paragios, Nikos Gallix, Benoit McGill Univ Dept Diagnost Radiol Montreal PQ Canada McGill Univ Hlth Ctr Dept Diagnost Radiol Montreal PQ Canada Univ Diderot Paris 7 Hop Lariboisiere AP HP Dept Body & Intervent Imaging F-75475 Paris 10 France INSERM U965 F-75475 Paris 10 France Ecole Cent Supelec F-91190 Gif Sur Yvette France Ecole Cent Supelec F-91120 PAL Palaiseau France Inria Saclay Ile de France F-91120 Palaiseau France TheraPanacea F-75014 Paris France

The recent explosion of big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from *** Points center dot Artificial intelligence (AI) research in medical imaging has a long history center dot The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning *** dot A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.

关键词： Diagnostic imaging Artificial intelligence (AI) Machine learning computer-assisted image processing computer-assisted image interpretation

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Sub-milliSievert (sub-mSv) CT colonography: a prospective comparison of image quality and polyp conspicuity at reduced-dose versus standard-dose imaging

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EUROPEAN RADIOLOGY 2015年第7期25卷 2089-2102页

作者： Lubner, Meghan G. Pooler, B. Dustin Kitchin, Douglas R. Tang, Jie Li, Ke Kim, David H. del Rio, Alejandro Munoz Chen, Guang-Hong Pickhardt, Perry J. Univ Wisconsin Sch Med & Publ Hlth Dept Radiol Madison WI 53792 USA Univ Wisconsin Sch Med & Publ Hlth Med Phys Madison WI 53792 USA

To prospectively compare reduced-dose (RD) CT colonography (CTC) with standard-dose (SD) imaging using several reconstruction algorithms. Following SD supine CTC, 40 patients (mean age, 57.3 years;17 M/23 F;mean BMI, 27.2) underwent an additional RD supine examination (targeted dose reduction, 70-90 %). DLP, CTDIvol, effective dose, and SSDE were compared. Several reconstruction algorithms were applied to RD series. SD-FBP served as reference standard. Objective image noise, subjective image quality and polyp conspicuity were assessed. Mean CTDIvol and effective dose for RD series was 0.89 mGy (median 0.65) and 0.6 mSv (median 0.44), compared with 3.8 mGy (median 3.1) and 2.8 mSv (median 2.3) for SD series, respectively. Mean dose reduction was 78 %. Mean image noise was significantly reduced on RD-PICCS (24.3 +/- 19HU) and RD-MBIR (19 +/- 18HU) compared with RD-FBP (90 +/- 33), RD-ASIR (72 +/- 27) and SD-FBP (47 +/- 14 HU). 2D image quality score was higher with RD-PICCS, RD-MBIR, and SD-FBP (2.7 +/- 0.4/2.8 +/- 0.4/2.9 +/- 0.6) compared with RD-FBP (1.5 +/- 0.4) and RD-ASIR (1.8 +/- 0.44). A similar trend was seen with 3D image quality scores. Polyp conspicuity scores were similar between SD-FBP/RD-PICCS/RD-MBIR (3.5 +/- 0.6/3.2 +/- 0.8/3.3 +/- 0.6). Sub-milliSievert CTC performed with iterative reconstruction techniques demonstrate decreased image quality compared to SD, but improved image quality compared to RD images reconstructed with FBP. aEuro cent CT colonography dose can be substantially lowered using advanced iterative reconstruction techniques. aEuro cent Iterative reconstruction techniques (MBIR/PICCS) reduce image noise and improve image quality. aEuro cent The PICCS/MBIR-reconstructed, reduced-dose series shows decreased 2D/3D image quality compared to the standard-dose series. aEuro cent Polyp conspicuity was similar on standard-dose images compared to reduced-dose images reconstructed with MBIR/PICCS.

关键词： Computed Tomographic Colonography Radiation Dosage Multidetector Computed Tomography Colonic Neoplasms computer-assisted image processing

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Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

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INTERNATIONAL JOURNAL OF computer assisted RADIOLOGY AND SURGERY 2008年第1-2期3卷 123-130页

作者： de Azevedo-Marques, Paulo Mazzoncini Rosa, Natalia Abdala Machado Traina, Agma Juci Traina, Caetano, Jr. Kinoshita, Sergio Koodi Rangayyan, Rangaraj Mandayam Univ Sao Paulo Fac Med Ribeirao Preto Dept Internal Med Image Sci & Med Phys Ctr BR-14048900 Ribeirao Preto SP Brazil Univ Sao Paulo Inst Math & Comp Sci BR-13560970 Sao Carlos SP Brazil Univ Calgary Schulich Sch Engn Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Object We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms. Materials and methods Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval. Results The results indicate improvement due to RFb of up to 62% in precision and 39% in RWP. Conclusion The gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.

关键词： computer-assisted radiographic image interpretation computer-assisted image processing Automated pattern recognition Information retrieval

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Statistical image reconstruction for inconsistent CT projection data

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METHODS OF INFORMATION IN MEDICINE 2007年第3期46卷 261-269页

作者： Oehler, M. Buzug, T. M. Univ Lubeck Inst Med Engn D-23538 Lubeck Germany

Objectives: The filtered backprojection is not able to cope with metal-induced inconsistencies in the Radon space which leads to artifacts in reconstructed CF images. A new algorithm is presented that reduces the drawbacks of existing artifact reduction strategies. Methods. Inconsistent projection data are bridged by directed interpolation. These projections are reconstructed using a weighted maximum likelihood algorithm (lambda-MLEM). The correlation coefficient between images of a torso phantom marked with steel markers reconstructed with lambda-MLEM and images of the some torso slice without markers quantifies the quality achieved. For clinical data, entropy maximization is presented to obtain appropriate weightings. Results. Different interpolation strategies have been applied. The quality of reconstruction sensitively depends on the complexity of interpolation. A directional interpolation gives best results. However, the quality of the images can be further improved by an appropriate weighing within eta-MLEM. This has been demonstrated with data from a torso phantom, a jaw with amalgam fillings and a hip prosthesis. Conclusions: eta-MLEM image reconstruction using data from directional Radon space interpolation is a new approach for metal artifact reduction. The weighting in this statistical approach is used to reduce the influence of residual inconsistencies in a way that optimal artifact suppression is obtained by optimizing a compromise between residual inconsistencies and void data. The image quality is superior compared with other artifact reduction strategies.

关键词： transmission computed tomography artifacts computer-assisted image processing image reconstruction

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image-based biomarkers for solid tumor quantification

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EUROPEAN RADIOLOGY 2019年第10期29卷 5431-5440页

作者： Savadjiev, Peter Chong, Jaron Dohan, Anthony Agnus, Vincent Forghani, Reza Reinhold, Caroline Gallix, Benoit McGill Univ Dept Diagnost Radiol Montreal PQ Canada McGill Univ Ctr Hlth Dept Diagnost Radiol 1001 Decarie Blvd Montreal PQ H4A 3J1 Canada Univ Diderot Paris 7 Hop Lariboisiere AP HP Dept Body & Intervent Imaging 2 Rue Ambroise Pare F-75475 Paris 10 France INSERM U965 2 Rue Ambroise Pare F-75475 Paris 10 France Inst Chirurg Guidee Image IHU Strasbourg 1 Pl Hop F-67091 Strasbourg France Jewish Gen Hosp Dept Radiol 3755 Chemin Cote St Catherine Montreal PQ H3T 1E2 Canada

The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. Currently, the imaging biomarker field is attracting increasing attention not only because of the tremendous interest in cutting-edge therapeutic developments and personalized medicine but also because of the recent progress in the application of artificial intelligence (AI) algorithms to large-scale datasets. Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning. Key Points center dot Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization. center dot image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy. center dot We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.

关键词： Diagnostic imaging Biomarkers Artificial intelligence (AI) computer-assisted image processing computer-assisted image interpretation

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