Clinical Utility of Quantitative Imaging

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

P-glycoprotein is implicated in clinical drug resistance; thus, rapid quantitative analysis of its expression and activity is of paramout importance to the design and success of novel therapeutics. The scope for the application of quantitative imaging and image analysis tools in this field is reported here at "proof of concept" level. P-glycoprotein expression was utilized as a model for quantitative immunofluorescence and subsequent spatial intensity distribution analysis (SpIDA). Following expression studies, p-glycoprotein inhibition as a function of verapamil concentration was assessed in two cell lines using live cell imaging of intracellular Calcein retention and a routine monolayer fluorescence assay. Intercellular and sub-cellular distributions in the expression of the p-glycoprotein transporter between parent and MDR1-transfected Madin-Derby Canine Kidney cell lines were examined. We have demonstrated that quantitative imaging can provide dose-response parameters while permitting direct microscopic analysis of intracellular fluorophore distributions in live and fixed samples. Analysis with SpIDA offers the ability to detect heterogeniety in the distribution of labeled species, and in conjunction with live cell imaging and immunofluorescence staining may be applied to the determination of pharmacological parameters or analysis of biopsies providing a rapid prognostic tool.

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: 1. Introduction: • Importance of tissue elasticity measurement • Strain vs. shear wave elastography (SWE), beneficial features of SWE • The link between shear wave speed and material properties, influence of viscosity 2. Generation of shear waves • External vibration (Fibroscan) • ultrasound radiation force • Point push • Supersonic push (Aixplorer) • Comb push (GE Logiq E9) 3. Detection of shear waves • Motion detection from pulse-echo ultrasound • Importance of frame rate for shear wave imaging • Plane wave imaging detection • How to achieve high effective frame rate using line-by-line scanners 4. Shear wave speed calculation • Time to peak • Random sample consensus (RANSAC) • Cross correlation 5. Sources of bias and variation in SWE • Tissue viscosity • Transducer compression or internal pressure of organ • Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: 1. Goals • Review the need for quantitative medical imaging • Provide examples of quantitative imaging biomarkers • Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization • Review the QIBA process for creating a quantitative biomarker • Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile 2. Underlying Premise and Assumptions • Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice • Reasons for quantification i. Evidence based medicine requires objective, not subjective observer data ii. Computerized decision support tools (eg CAD) generally require quantitative input. iii. Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems 3. What is quantitative imaging? • Definition from Imaging Metrology Workshop 4. The Quantitative Imaging Biomarker Alliance • Formation 2008 • Mission • Structure • Example Imaging Biomarkers Being Explored • Biomarker Selection • Groundwork • Draft Protocol for imaging and data evaluation • QIBA Profile Drafting • Equipment and Site Validation i. Technical ii. Clinical • Site and Equipment QA and Compliance Checking 5. Ultrasound Elasticity Estimation Biomarker • US Elasticity Estimation Background • Current Status and Problems • Biomarker Selection-process and outcome 6. US SWS for Liver Fibrosis Biomarker Work • Groundwork i. Literature search and analysis results ii. Phase I phantom testing-Elastic phantoms iii. Phase II phantom testing-Viscoelastic phantoms iv. Digital Simulated Data • Protocol and Profile Drafting i Protocol: based on UPICT and existing literature and standards bodies protocols ii. Profile-Current claims, Manufacturer specific appendices 7. What comes after the profile • Profile Validation i Technical validation ii. Clinical validation • QA and Compliance i. Possible approaches 1. Site a. Operator testing b. Site protocol re-evaluation 2. Imaging system a. Manufacturer testing and attestation b. User acceptance testing and periodic QA i. Phantom Tests ii. Digital Phantom Based Testing iii. Standard QA Testing iv. Remediation Schemes 8. Profile Evolution • Towards additional applications • Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

Imaging of tissue elastic properties is a relatively new and powerful approach to one of the oldest and most important diagnostic tools. Imaging of shear wave speed with ultrasound is has been added to most high-end ultrasound systems. Understanding this exciting imaging mode aiding its most effective use in medicine can be a rewarding effort for medical physicists and other medical imaging and treatment professionals. Assuring consistent, quantitative measurements across the many ultrasound systems in a typical imaging department will constitute a major step toward realizing the great potential of this technique and other quantitative imaging. This session will target these two goals with two presentations. A. Basics and Current Implementations of Ultrasound Imaging of Shear Wave Speed and Elasticity - Shigao Chen, Ph.D. Learning objectives-To understand: 1.Introduction: •Importance of tissue elasticity measurement •Strain vs. shear wave elastography (SWE), beneficial features of SWE •The link between shear wave speed and material properties, influence of viscosity 2.Generation of shear waves •External vibration (Fibroscan) •ultrasound radiation force •Point push •Supersonic push (Aixplorer) •Comb push (GE Logiq E9) 3.Detection of shear waves •Motion detection from pulse-echo ultrasound •Importance of frame rate for shear wave imaging •Plane wave imaging detection •How to achieve high effective frame rate using line-by-line scanners 4.Shear wave speed calculation •Time to peak •Random sample consensus (RANSAC) •Cross correlation 5.Sources of bias and variation in SWE •Tissue viscosity •Transducer compression or internal pressure of organ •Reflection of shear waves at boundaries B. Elasticity Imaging System Biomarker Qualification and User Testing of Systems – Brian Garra, M.D. Learning objectives-To understand: 1.Goals •Review the need for quantitative medical imaging •Provide examples of quantitative imaging biomarkers •Acquaint the participant with the purpose of the RSNA Quantitative Imaging Biomarker Alliance and the need for such an organization •Review the QIBA process for creating a quantitative biomarker •Summarize steps needed to verify adherence of site, operators, and imaging systems to a QIBA profile 2.Underlying Premise and Assumptions •Objective, quantifiable results are needed to enhance the value of diagnostic imaging in clinical practice •Reasons for quantification i.Evidence based medicine requires objective, not subjective observer data ii.Computerized decision support tools (eg CAD) generally require quantitative input. iii.Quantitative, reproducible measures are more easily used to develop personalized molecular medical diagnostic and treatment systems 3.What is quantitative imaging? •Definition from Imaging Metrology Workshop 4.The Quantitative Imaging Biomarker Alliance •Formation 2008 •Mission •Structure •Example Imaging Biomarkers Being Explored •Biomarker Selection •Groundwork •Draft Protocol for imaging and data evaluation •QIBA Profile Drafting •Equipment and Site Validation i.Technical ii.Clinical •Site and Equipment QA and Compliance Checking 5.Ultrasound Elasticity Estimation Biomarker •US Elasticity Estimation Background •Current Status and Problems •Biomarker Selection-process and outcome 6.US SWS for Liver Fibrosis Biomarker Work •Groundwork i.Literature search and analysis results ii.Phase I phantom testing-Elastic phantoms iii.Phase II phantom testing-Viscoelastic phantoms iv.Digital Simulated Data •Protocol and Profile Drafting iProtocol: based on UPICT and existing literature and standards bodies protocols ii.Profile-Current claims, Manufacturer specific appendices 7.What comes after the profile •Profile Validation iTechnical validation ii.Clinical validation •QA and Compliance i.Possible approaches 1.Site a.Operator testing b.Site protocol re-evaluation 2.Imaging system a.Manufacturer testing and attestation b.User acceptance testing and periodic QA i.Phantom Tests ii.Digital Phantom Based Testing iii.Standard QA Testing iv.Remediation Schemes 8.Profile Evolution •Towards additional applications •Towards higher accuracy and precision Supported in part by NIH contract HHSN268201300071C from NIBIB. Collaboration with GE Global Research, no personal support.; S. Chen, Some technologies described in this presentation have been licensed. Mayo Clinic and Dr. Chen have financial interests these technologies.

Objective. X-ray phase contrast imaging is a promising technique for future clinical diagnostic as it can provide enhanced contrast in soft tissues compared to traditional x-ray attenuation-contrast imaging. However, the strict requirements on the x-ray coherence and the precise alignment of optical elements limit its applications towards clinical use. To solve this problem, mesh-based x-ray phase contrast imaging method with one hexagonal mesh is proposed for easy alignment and better image visualization. Approach. The mesh produces structured illuminations and the detector captures its distortions to reconstruct the absorption, differential phase contrast (DPC) and dark-field (DF) images of the sample. In this work, we fabricated a hexagonal mesh to simultaneously retrieve DPC and DF signals in three different directions with single shot. A phase retrieval algorithm to obtain artifacts-free phase from DPC images with three different directions is put forward and false color dark-field image is also reconstructed with tri-directional images. Mesh-shifting method based on this hexagonal mesh modulator is also proposed to reconstruct images with better image quality at the expense of increased dose. Main results. In numerical simulations, the proposed hexagonal mesh outperforms the traditional square mesh in image evaluation metrics performance and false color visualization with the same radiation dose. The experimental results demonstrate its feasiblity in real imaging systems and its advantages in quantitive imaging and better visualization. The proposed hexagonal mesh is easy to fabricate and can be successfully applied to x-ray source with it spot size up to 300 μm. Significance. This work opens new possibilities for quantitative x-ray non-destructive imaging and may also be instructive for research fields such as x-ray structured illumination microscopy (SIM), x-ray spectral imaging and x-ray phase contrast and dark-field computed tomography (CT).

Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by progressive cognitive decline and is traditionally associated with grey matter pathology. Recent research highlights the significance of white matter and myelin damage in AD, presenting a paradigm shift in understanding the disease. The aim of this study was to summarize current advancements in magnetic resonance imaging (MRI) techniques and their applications in assessing myelin and brain pathology in AD with a special focus on ultrashort echo time (UTE) based techniques, alongside the role of artificial intelligence (AI) in enhancing diagnostic accuracy. Between April and May 2024, we conducted a literature search using Google Scholar, Web of Science, and PubMed, focusing on publications from 1990 to 2024. Search terms included "Quantitative imaging", "Alzheimer's MRI", "T1ρ Alzheimer's", "MT imaging Alzheimer's", and "myelin water fraction Alzheimer's". We included quantitative MRI studies involving AD brains and excluded volumetric analyses, non-quantitative studies, non-English reports, non-peer-reviewed studies, and animal research. Quantitative MRI techniques, including T1, T1ρ, magnetization transfer ratio (MTR), T2, T2*, susceptibility, myelin water fraction (MWF), and non-aqueous myelin proton density (PD) were described. These biomarkers represent different pathophysiological elements of brain damage and may have distinct functions at different phases of the disease. The role of AI in enhancing diagnostic accuracy is also discussed. In conclusion, integrating advanced MRI techniques and AI offers promising avenues for understanding and diagnosing AD. The focus on myelin damage and white matter integrity underscores the importance of comprehensive imaging approaches. Continued research and development are essential to address current challenges and improve clinical practice in AD diagnostics.

Quantitative SPECT and PET imaging have transformed nuclear medicine from qualitative assessment to precise quantitative analysis, significantly impacting both research and clinical applications. This chapter explores three fundamental aspects: standardisation initiatives, treatment response evaluation, and pharmacokinetic modelling advancements. Standardisation efforts encompass calibration protocols, harmonisation across imaging centres, reconstruction methods, and reporting standards, establishing a foundation for reliable quantitative metrics and multi-centre trials. In treatment response assessment, particularly in oncology, quantitative imaging has become instrumental through FDG-PET and novel tracers, with standardised criteria like PERCIST enabling objective outcome evaluation. Pharmacokinetic modelling has evolved to provide deeper insights into tracer behaviour by incorporating imagederived input functions, simplified kinetic models, and parametric imaging techniques, thereby advancing applications in drug development, neuroimaging, and personalised medicine. The chapter addresses ongoing challenges and future directions, including the need for enhanced standardisation, artificial intelligence integration, and novel radiotracer development. These developments in quantitative nuclear imaging are revolutionising personalised medicine, improving diagnostic accuracy, and enabling more precise treatment monitoring across various diseases. As the field continues to advance, quantitative SPECT and PET are positioned to play increasingly vital roles in understanding disease processes and guiding clinical decisions, ultimately improving patient outcomes through more accurate and personalised approaches to diagnosis and treatment.

Quantitative phase imaging (QPI) has emerged as a powerful tool in label-free bioimaging, in situ microstructure characterization for advanced manufacturing, and high-speed imaging of material property changes. Among various QPI methods, quadri-wave lateral shearing interferometry (QWLSI) stands out for its unique advantages in compactness, robustness, and high temporal resolution, making it an ideal choice for a wide range of applications. The compact design of QWLSI allows for easy integration with existing microscopy systems, while its robustness is manifested in the ability to maintain precise interferometric sensitivity even in high-vibration environments. Moreover, QWLSI also enables single-shot measurements that facilitate the capture of fast dynamic processes. This paper provides an in-depth exploration of the technical aspects of QWLSI, focusing on the evolution of its optical system and the primary algorithms used in wavefront reconstruction. The review also showcases significant applications of QWLSI, with a particular emphasis on its contributions to biomedical imaging. By discussing the advantages, limitations, and potential future developments of QWLSI, this paper aims to provide a comprehensive overview of this powerful QPI technique and its impact on various research fields.

Revealing the structural and functional organization of biological samples at submicrometer scales requires high-resolution imaging techniques that provide real-time, quantitative information. However, resolving and analyzing unlabeled biological structures at the subcellular level from a single-shot image remain challenging. We introduce a novel incoherent quantitative phase imaging technique based on lateral shearing digital holographic microscopy, with shearing distances exceeding the coherence area of the employed illumination—a regime not previously achieved and exploited. This strategy consequently ensures artifact-free, high-accuracy, and high-resolution quantitative phase reconstructions. This regime extends single-shot lateral shearing digital holographic microscopy toward incoherent illumination, thus increasing the space–time bandwidth product of the method. The practical common-path geometry also provides enhanced vibration resistance, which is essential for consistent time-lapse measurements. We verify the scalability and effectiveness of the method by investigating samples via multiple condenser–objective pairs, providing a wide range of lateral resolutions and fields of view, thereby ensuring its applicability for various microscope settings. We experimentally verify long-term phase stability and unprecedented phase-reconstruction accuracy. Finally, we use our method to investigate biological samples, including cheek cells, diatoms, and yeast cells, highlighting its potential for dynamic label-free analysis at the organelle level. These results establish our approach as a powerful tool for quantitative phase imaging, enabling high-precision investigations across a wide range of scientific applications.

Quantitative holographic phase contrast imaging enables high-resolution inspection of reflective surfaces and technical phase specimen as well as the minimally invasive analysis of living cells. However, a drawback of many experimental arrangements is the requirement for a separate reference wave which results in a phase stability decrease and the demand for a precise adjustment of the intensity ratio between object and reference wave. Thus, a self interference digital holographic microscopy (DHM) approach was explored which only requires a single object illumination wave. Due to the Michelson interferometer design of the proposed setup two wave fronts with an almost identical curvature are superimposed. This results in a nearly ideal pattern of spatial off-axis carrier fringes and a constant interferogram contrast in the hologram plane. Moreover, the hologram evaluation with spatial phase shifting reconstruction algorithms and Fourier transformation-based spatial filtering methods as well as the integration of DHM in common research microscopes is simplified. Furthermore, the use of laser light sources with a short coherence length is enabled. The applicability of the proposed self interference principle is illustrated by data from the analysis of technical and biological phase specimens. The obtained results demonstrate that the method prospects to be a versatile tool for quantitative phase contrast imaging.

This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P<0.001) and practice (β=0.412, P<0.001), and attitude had a direct effect on practice (β=0.135, P<0.001). Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

Coherent anti-Stokes Raman microscopy (CARS) is a quantitative, chemically specific, and label-free optical imaging technique for studying inhomogeneous systems. However, the complicating influence of the nonresonant response on the CARS signal severely limits its sensitivity and specificity and especially limits the extent to which CARS microscopy has been used as a fully quantitative imaging technique. On the basis of spectral focusing mechanism, we establish a dual-soliton Stokes based CARS microspectroscopy and microscopy scheme capable of quantifying the spatial information of densities and chemical composition within inhomogeneous samples, using a single fiber laser. Dual-soliton Stokes scheme not only removes the nonresonant background but also allows robust acquisition of multiple characteristic vibrational frequencies. This all-fiber based laser source can cover the entire fingerprint (800-2200 cm-1) region with a spectral resolution of 15 cm-1. We demonstrate that quantitative degree determination of lipid-chain unsaturation in the fatty acids mixture can be achieved by the characterization of C = C stretching and CH2 deformation vibrations. For microscopy purposes, we show that the spatially inhomogeneous distribution of lipid droplets can be further quantitatively visualized using this quantified degree of lipid unsaturation in the acyl chain for contrast in the hyperspectral CARS images. The combination of compact excitation source and background-free capability to facilitate extraction of quantitative composition information with multiplex spectral peaks will enable wider applications of quantitative chemical imaging in studying biological and material systems.

Quantitative MRI: Rationale and Challenges

Chapter 17 - Meaningful incorporation of artificial intelligence for personalized patient management during cancer: Quantitative imaging, risk assessment, and therapeutic outcomes

Despite more than 20 years of development, the MRI-based cartilage compositional biomarkers T2 and T1rho have not been routinely applied in clinical practice. This review examines these measures' historical development and frames the challenges in the application of these quantitative imaging tools to the care of patients with cartilage injury and osteoarthritis (OA) using the hierarchic model of efficacy proposed by Fryback and Thornbury. T2 and T1rho have been validated for the evaluation of early compositional and structural changes in the cartilage extracellular matrix. Yet, these biomarkers lack direct correlation with pain or function loss, lack standardization of methods for acquisition and analysis, and have a limited role in guiding therapeutic management given the absence of effective disease-modifying OA drugs. These issues present significant challenges in the path to the biomarkers' future implementation in clinical care. Nonetheless, these MRI-based cartilage compositional biomarkers provide an essential tool for musculoskeletal research and can provide important information about the biophysical properties of cartilage that will continue to contribute to our understanding of cartilage injury and OA pathogenesis.