The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Richard L Wahl - One of the best experts on this subject based on the ideXlab platform.
-
evaluating multisite rcbv consistency from dsc mri Imaging protocols and postprocessing software across the nci Quantitative Imaging network sites using a digital reference object dro
Tomography (Ann Arbor Mich.), 2019Co-Authors: Laura C Bell, Richard L Wahl, Natenael Semmineh, Hongyu An, Cihat Eldeniz, Kathleen M Schmainda, Melissa Prah, Bradley J Erickson, Panagiotis Korfiatis, Chengyue WuAbstract:: Relative cerebral blood volume (rCBV) cannot be used as a response metric in clinical trials, in part, because of variations in biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and postprocessing methods (PMs). This study leveraged a dynamic susceptibility contrast magnetic resonance Imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific Imaging protocols (IPs; n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance Imaging analysis (eg, using a digital reference object where the ground truth is known).
-
evaluating multisite rcbv consistency from dsc mri Imaging protocols and postprocessing software across the nci Quantitative Imaging network sites using a digital reference object dro
Tomography: A Journal for Imaging Research, 2019Co-Authors: Laura C Bell, Richard L Wahl, Natenael Semmineh, Cihat Eldeniz, Kathleen M Schmainda, Melissa Prah, Bradley J Erickson, Panagiotis Korfiatis, Anna G Sorace, Thomas E YankeelovAbstract:The use of rCBV as a response metric in clinical trials has been hampered, in part, due to variations in the biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and post-processing methods. This study leveraged a dynamic susceptibility contrast magnetic resonance Imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific Imaging protocols (IPs; n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance Imaging analysis (eg, using a digital reference object where the ground truth is known).
-
Quantitative Imaging biomarkers a review of statistical methods for technical performance assessment
Statistical Methods in Medical Research, 2015Co-Authors: David Raunig, Constantine Gatsonis, Lisa M Mcshane, Brenda F Kurland, Gene Pennello, Paul L Carson, James T Voyvodic, Richard L Wahl, Adam J Schwarz, Mithat GonenAbstract:Technological developments and greater rigor in the Quantitative measurement of biological features in medical images have given rise to an increased interest in using Quantitative Imaging biomarkers to measure changes in these features. Critical to the performance of a Quantitative Imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a Quantitative Imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of Quantitative Imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.
-
Quantitative Imaging biomarkers a review of statistical methods for technical performance assessment
Statistical Methods in Medical Research, 2015Co-Authors: David Raunig, Constantine Gatsonis, Lisa M Mcshane, Brenda F Kurland, Gene Pennello, Paul L Carson, James T Voyvodic, Richard L Wahl, Adam J Schwarz, Mithat GonenAbstract:Technological developments and greater rigor in the Quantitative measurement of biological features in medical images have given rise to an increased interest in using Quantitative Imaging biomarkers to measure changes in these features. Critical to the performance of a Quantitative Imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a Quantitative Imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of...
-
Quantitative Imaging and sigmoidoscopy to assess distribution of rectal microbicide surrogates
Clinical Pharmacology & Therapeutics, 2008Co-Authors: Craig W Hendrix, Edward J Fuchs, Katarzyna J Macura, Linda A Lee, Teresa L Parsons, Rahul P Bakshi, Wasif Khan, A Guidos, Jeffrey Leal, Richard L WahlAbstract:Understanding the distribution of microbicide and human immunodeficiency virus (HIV) within the gastrointestinal tract is critical to development of rectal HIV microbicides. A hydroxyethylcellulose-based microbicide surrogate or viscosity-matched semen surrogate, labeled with gadolinium-DTPA (diethylene triamine pentaacetic acid) and 99mTechnetium-sulfur colloid, was administered to three subjects under varying experimental conditions to evaluate effects of enema, coital simulation, and microbicide or semen simulant over 5 h duration. Quantitative assessment used single photon emission computed tomography (SPECT)/computed tomography (CT) and magnetic resonance Imaging (MRI) Imaging, and sigmoidoscopic sampling. Over 4 h, radiolabel migrated cephalad in all studies by a median (interquartile range) of 50% (29–102%; P<0.001), as far as the splenic flexure (~60 cm) in 12% of studies. There was a correlation in concentration profile between endoscopic sampling and SPECT assessments. HIV-sized particles migrate retrograde, 60 cm in some studies, 4 h after simulated ejaculation in our model. SPECT/CT, MRI, and endoscopy can be used Quantitatively to facilitate rational development of microbicides for rectal use. Clinical Pharmacology & Therapeutics (2008) 83, 97–105; doi:10.1038/sj.clpt.6100236; published online 16 May 2007
Nancy A Obuchowski - One of the best experts on this subject based on the ideXlab platform.
-
Quantitative Imaging biomarkers alliance qiba recommendations for improved precision of dwi and dce mri derived biomarkers in multicenter oncology trials
Journal of Magnetic Resonance Imaging, 2019Co-Authors: Amita Shukladave, Nancy A Obuchowski, Lawrence H Schwartz, Thomas L Chenevert, Sachin Jambawalikar, Dariya I Malyarenko, Wei Huang, Susan M Noworolski, Robert J Young, Mark S ShiroishiAbstract:Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted Imaging and dynamic contrast-enhanced magnetic resonance Imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived Imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these Quantitative Imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the Imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical Imaging community. Some of QIBA's development of Quantitative diffusion-weighted Imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.
-
Quantitative Imaging biomarkers effect of sample size and bias on confidence interval coverage
Statistical Methods in Medical Research, 2018Co-Authors: Nancy A Obuchowski, Jennifer BullenAbstract:Introduction Quantitative Imaging biomarkers (QIBs) are being increasingly used in medical practice and clinical trials. An essential first step in the adoption of a Quantitative Imaging biomarker is the characterization of its technical performance, i.e. precision and bias, through one or more performance studies. Then, given the technical performance, a confidence interval for a new patient's true biomarker value can be constructed. Estimating bias and precision can be problematic because rarely are both estimated in the same study, precision studies are usually quite small, and bias cannot be measured when there is no reference standard. Methods A Monte Carlo simulation study was conducted to assess factors affecting nominal coverage of confidence intervals for a new patient's Quantitative Imaging biomarker measurement and for change in the Quantitative Imaging biomarker over time. Factors considered include sample size for estimating bias and precision, effect of fixed and non-proportional bias, clustered data, and absence of a reference standard. Results Technical performance studies of a Quantitative Imaging biomarker should include at least 35 test-retest subjects to estimate precision and 65 cases to estimate bias. Confidence intervals for a new patient's Quantitative Imaging biomarker measurement constructed under the no-bias assumption provide nominal coverage as long as the fixed bias is <12%. For confidence intervals of the true change over time, linearity must hold and the slope of the regression of the measurements vs. true values should be between 0.95 and 1.05. The regression slope can be assessed adequately as long as fixed multiples of the measurand can be generated. Even small non-proportional bias greatly reduces confidence interval coverage. Multiple lesions in the same subject can be treated as independent when estimating precision. Conclusion Technical performance studies of Quantitative Imaging biomarkers require moderate sample sizes in order to provide robust estimates of bias and precision for constructing confidence intervals for new patients. Assumptions of linearity and non-proportional bias should be assessed thoroughly.
-
metrology standards for Quantitative Imaging biomarkers
Radiology, 2015Co-Authors: Daniel C Sullivan, Nancy A Obuchowski, Larry Kessler, David Raunig, Constantine Gatsonis, Erich Huang, Marina V Kondratovich, Lisa M Mcshane, Anthony P Reeves, Daniel P BarboriakAbstract:The information and recommendations presented in this article are derived from the deliberations and publications of the Quantitative Imaging Biomarkers Alliance Metrology Working Group.
-
metrology standards for Quantitative Imaging biomarkers
Radiology, 2015Co-Authors: Daniel C Sullivan, Nancy A Obuchowski, Larry Kessler, David Raunig, Constantine Gatsonis, Lisa M Mcshane, Anthony P Reeves, Marina Kondratovich, Erich P Huang, Daniel P BarboriakAbstract:Although investigators in the Imaging community have been active in developing and evaluating Quantitative Imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to Imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo Imaging studies.
-
statistical issues in the comparison of Quantitative Imaging biomarker algorithms using pulmonary nodule volume as an example
Statistical Methods in Medical Research, 2015Co-Authors: Nancy A Obuchowski, Jayashree Kalpathycramer, Gene Pennello, Andrew J Buckler, Huiman X Barnhart, Xiaofeng Wang, Hyun J Kim, Anthony P ReevesAbstract:Quantitative Imaging biomarkers are being used increasingly in medicine to diagnose and monitor patients' disease. The computer algorithms that measure Quantitative Imaging biomarkers have different technical performance characteristics. In this paper we illustrate the appropriate statistical methods for assessing and comparing the bias, precision, and agreement of computer algorithms. We use data from three studies of pulmonary nodules. The first study is a small phantom study used to illustrate metrics for assessing repeatability. The second study is a large phantom study allowing assessment of four algorithms' bias and reproducibility for measuring tumor volume and the change in tumor volume. The third study is a small clinical study of patients whose tumors were measured on two occasions. This study allows a direct assessment of six algorithms' performance for measuring tumor change. With these three examples we compare and contrast study designs and performance metrics, and we illustrate the advantages and limitations of various common statistical methods for Quantitative Imaging biomarker studies.
Andriy Fedorov - One of the best experts on this subject based on the ideXlab platform.
-
multisite concordance of apparent diffusion coefficient measurements across the nci Quantitative Imaging network
Journal of medical imaging, 2017Co-Authors: David C Newitt, Andriy Fedorov, Thomas L Chenevert, Dariya I Malyarenko, Chad C Quarles, Laura C Bell, M Fiona M D Fennessy, Michael A Jacobs, Meiyappan Solaiyappan, Stefanie J HectorsAbstract:Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as Quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two (ADC2) and four (ADC4) b-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo ADC2, with relative biases <0.1 % (ADC2) and <0.5 % (phantom ADC4) but with higher deviations in ADC at the lowest phantom ADC values. In vivo ADC4 concordance was good, with typical biases of ±2 % to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for ADC4in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.
-
multisite concordance of apparent diffusion coefficient measurements across the nci Quantitative Imaging network
Journal of medical imaging, 2017Co-Authors: David C Newitt, Andriy Fedorov, Thomas L Chenevert, Dariya I Malyarenko, Chad C Quarles, Laura C Bell, M Fiona M D Fennessy, Michael A Jacobs, Meiyappan Solaiyappan, Stefanie J HectorsAbstract:Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as Quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.
-
dicom for Quantitative Imaging biomarker development a standards based approach to sharing clinical data and structured pet ct analysis results in head and neck cancer research
PeerJ, 2016Co-Authors: Andriy Fedorov, David A Clunie, Jorg Riesmeier, Christian Bauer, Ethan J Ulrich, Andreas Wahle, Bartley Brown, Michael Onken, Steve Pieper, Ron KikinisAbstract:Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and Imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) Quantitative Imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM(®)) international standard and free open-source software. Methods. Quantitative analysis of PET/CT Imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.
-
informatics methods to enable sharing of Quantitative Imaging research data
Magnetic Resonance Imaging, 2012Co-Authors: Mia A Levy, Fiona M Fennessy, John Freymann, Justin Kirby, Andriy Fedorov, Steven A Eschrich, Anders Berglund, David Fenstermacher, Yongqiang Tan, Xiaotao GuoAbstract:Abstract Introduction The National Cancer Institute Quantitative Research Network (QIN) is a collaborative research network whose goal is to share data, algorithms and research tools to accelerate Quantitative Imaging research. A challenge is the variability in tools and analysis platforms used in Quantitative Imaging. Our goal was to understand the extent of this variation and to develop an approach to enable sharing data and to promote reuse of Quantitative Imaging data in the community. Methods We performed a survey of the current tools in use by the QIN member sites for representation and storage of their QIN research data including images, image meta-data and clinical data. We identified existing systems and standards for data sharing and their gaps for the QIN use case. We then proposed a system architecture to enable data sharing and collaborative experimentation within the QIN. Results There are a variety of tools currently used by each QIN institution. We developed a general information system architecture to support the QIN goals. We also describe the remaining architecture gaps we are developing to enable members to share research images and image meta-data across the network. Conclusions As a research network, the QIN will stimulate Quantitative Imaging research by pooling data, algorithms and research tools. However, there are gaps in current functional requirements that will need to be met by future informatics development. Special attention must be given to the technical requirements needed to translate these methods into the clinical research workflow to enable validation and qualification of these novel Imaging biomarkers.
-
3d slicer as an image computing platform for the Quantitative Imaging network
Magnetic Resonance Imaging, 2012Co-Authors: Andriy Fedorov, Jayashree Kalpathycramer, Fiona M Fennessy, Christian Bauer, Jeanchristophe Fillionrobin, Reinhard R Beichel, Julien Finet, Sonia Pujol, Dominique Jennings, Milan SonkaAbstract:Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical Imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing Quantitative Imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the Quantitative Imaging techniques. The success of Quantitative Imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new Quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of Imaging biomarkers using 3D Slicer.
Alexandre Aubry - One of the best experts on this subject based on the ideXlab platform.
-
Reflection matrix approach for Quantitative Imaging of scattering media
Physical Review X, 2020Co-Authors: William Lambert, Laura Cobus, Mathieu Couade, Mathias Fink, Alexandre AubryAbstract:We present a physically intuitive matrix approach for wave Imaging and characterization in scattering media. The experimental proof-of-concept is performed with ultrasonic waves, but this approach can be applied to any field of wave physics for which multi-element technology is available. The concept is that focused beamforming enables the synthesis, in transmit and receive, of an array of virtual transducers which map the entire medium to be imaged. The inter-element responses of this virtual array form a focused reflection matrix from which spatial maps of various characteristics of the propagating wave can be retrieved. Here we demonstrate: (i) a local focusing criterion that enables the image quality and the wave velocity to be evaluated everywhere inside the medium, including in random speckle, and (ii) an highly resolved spatial mapping of the prevalence of multiple scattering, which constitutes a new and unique contrast for ultrasonic Imaging. The approach is demonstrated for a controllable phantom system, and for in vivo Imaging of the human abdomen. More generally, this matrix approach opens an original and powerful route for Quantitative Imaging in wave physics.
-
Reflection matrix approach for Quantitative Imaging of scattering media
Physical Review X, 2020Co-Authors: William Lambert, Laura Cobus, Mathieu Couade, Mathias Fink, Alexandre AubryAbstract:We present a physically intuitive matrix approach for wave Imaging and characterization in scattering media. The experimental proof-of-concept is performed with ultrasonic waves, but this approach can be applied to any field of wave physics for which multi-element technology is available. The concept is that focused beamforming enables the synthesis, in transmit and receive, of an array of virtual transducers which map the entire medium to be imaged. The inter-element responses of this virtual array form a focused reflection matrix from which spatial maps of various characteristics of the propagating wave can be retrieved. Here we demonstrate: (i) a local focusing criterion that enables the Imaging quality to be evaluated everywhere inside the medium, including in random speckle; (ii) a tomographic measurement of wave velocity, which allows for aberration corrections in the original image; (iii) an highly resolved spatial mapping of the prevalence of multiple scattering, which constitutes a new and unique contrast for ultrasonic Imaging. More generally, this matrix approach opens an original and powerful route for Quantitative Imaging in wave physics.
Gene Pennello - One of the best experts on this subject based on the ideXlab platform.
-
Quantitative Imaging biomarkers a review of statistical methods for technical performance assessment
Statistical Methods in Medical Research, 2015Co-Authors: David Raunig, Constantine Gatsonis, Lisa M Mcshane, Brenda F Kurland, Gene Pennello, Paul L Carson, James T Voyvodic, Richard L Wahl, Adam J Schwarz, Mithat GonenAbstract:Technological developments and greater rigor in the Quantitative measurement of biological features in medical images have given rise to an increased interest in using Quantitative Imaging biomarkers to measure changes in these features. Critical to the performance of a Quantitative Imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a Quantitative Imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of Quantitative Imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined.
-
Quantitative Imaging biomarkers a review of statistical methods for technical performance assessment
Statistical Methods in Medical Research, 2015Co-Authors: David Raunig, Constantine Gatsonis, Lisa M Mcshane, Brenda F Kurland, Gene Pennello, Paul L Carson, James T Voyvodic, Richard L Wahl, Adam J Schwarz, Mithat GonenAbstract:Technological developments and greater rigor in the Quantitative measurement of biological features in medical images have given rise to an increased interest in using Quantitative Imaging biomarkers to measure changes in these features. Critical to the performance of a Quantitative Imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a Quantitative Imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of...
-
statistical issues in the comparison of Quantitative Imaging biomarker algorithms using pulmonary nodule volume as an example
Statistical Methods in Medical Research, 2015Co-Authors: Nancy A Obuchowski, Jayashree Kalpathycramer, Gene Pennello, Andrew J Buckler, Huiman X Barnhart, Xiaofeng Wang, Hyun J Kim, Anthony P ReevesAbstract:Quantitative Imaging biomarkers are being used increasingly in medicine to diagnose and monitor patients' disease. The computer algorithms that measure Quantitative Imaging biomarkers have different technical performance characteristics. In this paper we illustrate the appropriate statistical methods for assessing and comparing the bias, precision, and agreement of computer algorithms. We use data from three studies of pulmonary nodules. The first study is a small phantom study used to illustrate metrics for assessing repeatability. The second study is a large phantom study allowing assessment of four algorithms' bias and reproducibility for measuring tumor volume and the change in tumor volume. The third study is a small clinical study of patients whose tumors were measured on two occasions. This study allows a direct assessment of six algorithms' performance for measuring tumor change. With these three examples we compare and contrast study designs and performance metrics, and we illustrate the advantages and limitations of various common statistical methods for Quantitative Imaging biomarker studies.
-
Quantitative Imaging biomarkers a review of statistical methods for computer algorithm comparisons
Statistical Methods in Medical Research, 2015Co-Authors: Nancy A Obuchowski, Anthony P Reeves, Andrew J Buckler, Huiman X Barnhart, Erich P Huang, Xiaofeng Wang, Hyun J Kim, Edward F Jackson, Maryellen L Giger, Gene PennelloAbstract:Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of Quantitative Imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the ...
