BACKGROUND:Magnetic resonance imaging (MRI) is commonly used for head-and-neck (H&N) delineation in radiotherapy (RT) owing to the lack of contrast in computed tomography (CT) images. MRI-based dosimetry is not yet fully developed for the H&N area due to the inter-patient variability and the difficulty of accurately quantifying cortical bone based on MRI measure. PURPOSE:Despite the difficulty of cortical bone segmentation with nearby air area, most papers in the literature show dosimetry results very close to the reference CT scan dosimetry. This study evaluated the impact of bone segmentation on dosimetry. METHODS:A home-made phantom was used to evaluate the Hounsfield unit (HU) values of different tissue at diverse scanner energies. Different dosimetry plans were considered and compared with the reference CT-based RT plan used for the treatment: CT images with cortical bone voxels assigned to the density of water and three based on MR images using a density assignment (DA) method for synthetic CT (sCT) generation with various bone attribution. Images from 24 patients were compared using mean absolute error (MAE). The generated dosimetry parameters were compared on the basis of dose-volume histogram values and 3%/3 mm gamma pass rates. RESULTS:Among the sCT, the best MAE were found for the DA sCT without bone. The mean of the median dose difference for the planning tumor volume (PTV) was less than 2% for all sCT. Mean gamma pass rate for the MR-based sCTs were comparable. CONCLUSIONS:The DA of the bone can be of interest if correctly segmented, but in the absence of certainty, an overestimation will lead to significant errors while an underestimation gives results close to the reference. The relevance of very precise sCT can be questioned if it requires too many resources and overlooks patients with artifacts.

BACKGROUND:Partially as a result of hypoxia-induced radioresistance, rates of treatment failure for head-and-neck cancer patients receiving radiotherapy can be considerable. Clinical trials utilizing positron emission tomography (PET) to image tumor hypoxia and escalate the prescription dose in hypoxic sub-volumes are being pursued in response, with current clinical prescription doses of 70 Gy generally escalated to 77-78 Gy. Instead utilizing magnetic resonance imaging (MRI) for hypoxia-based prescription dose escalation would be associated with a variety of advantages, including not requiring an additional imaging-related radiation dose to be delivered to the patient and allowing for a variety of other functional maps to be extracted from the same patient imaging session, in addition to tumor hypoxia information. PURPOSE:The purpose of this study is to investigate the benefits of MRI-informed hypoxia-based radiotherapy dose escalation for head-and-neck cancer patients treated with proton radiotherapy. METHODS:Ten patients with head-and-neck cancer scheduled to undergo photon therapy underwent a multi-parametric MRI protocol based on which tumor hypoxia maps were computed for every patient using a quantitative blood oxygenation level dependent (BOLD) approach. Four proton therapy treatment plans were then created for each patient, consisting of intensity-modulated proton therapy (IMPT) and proton arc therapy (PAT) treatment planning performed according to current clinical standards (IMPTConv and PATConv) or with a 10% prescription dose escalation to the hypoxic sub-volumes of the low- and high-risk target structures (IMPTEsc and PATEsc). The generated treatment plans were then analyzed with respect to target and organ-at-risk (OAR) doses and normal tissue complication probabilities (NTCPs) as well as tumor control probabilities (TCPs) calculated according to conventional models (TCPConv) or with consideration of hypoxia-induced radioresistance (TCPHyp). Statistical significance (p < 0.05) of different TCP or mean OAR dose distributions was determined using the Wilcoxon signed-rank test. RESULTS:During IMPT, radiotherapy prescription dose escalation increased TCPConv in the nominal scenario by (5.9 ± 6.3) percentage points (pp) in the normoxic (p < 0.001) and (5.2 ± 9.0) pp in the hypoxic target volumes (p = 0.006). In the worst-case scenario, TCPConv was increased by (5.6 ± 4.5) pp (p < 0.001) and (5.3 ± 6.4) pp (p = 0.003). Dose escalation during PAT improved TCPConv by (3.1 ± 3.3) pp (p < 0.001) and (2.1 ± 5.4) pp (p = 0.015) in the nominal scenario and (3.3 ± 3.1) pp (p < 0.001) and (3.3 ± 4.4) pp (p < 0.001) in the worst-case scenario. When hypoxia-induced radioresistance was considered, dose escalation elevated TCPHyp in the nominal scenario by (7.6 ± 4.5) pp (p < 0.001) during IMPT and (6.4 ± 4.1) pp (p < 0.001) during PAT and TCPHyp in the worst-case scenario by (6.3 ± 3.8) pp (p < 0.001) during IMPT and (6.3 ± 3.1) pp (p < 0.001) during PAT. Compared to the patients' clinical photon therapy treatment plans in the nominal scenario, mean OAR doses were reduced by (13.5 ± 9.3)Gy RBE by IMPTConv, (14.3 ± 10.5)Gy RBE by PATConv, (9.8 ± 10.5)Gy RBE by IMPTEsc, and (10.4 ± 12.8)Gy RBE by PATEsc (all p = 0.002). CONCLUSIONS:MRI-based hypoxia-informed radiotherapy prescription dose escalation during both IMPT and PAT significantly increased calculated TCPs while significantly reducing doses delivered to nearby healthy organs compared to the patients' clinical photon therapy treatment plans. MRI-based hypoxia-informed prescription dose escalation is therefore considered feasible and may help partially address hypoxia-induced radioresistance.

Objectives: The role of advanced diffusion-weighted imaging (DWI) in chronic liver disease (CLD) has not been fully studied. Chronic liver disease (CLD) is a progressive deterioration of liver functions, caused by one or more etiology. This study was aimed to investigate whether radiomics features extracted from individual or combined magnetic resonance imaging sequences, such as T1-weighted, T2-weighted images, or quantitative maps from chemical shift encoded, diffusion-weighted imaging, can effectively classify inflammation and fibrosis in CLD. Method: Seventy-seven patients with CLD were enrolled in this study. Each participant underwent both MRI examinations and liver biopsy. The biopsy procedure was applied to quantitatively or semi-qualitatively analyze several histology features, steatosis, inflammation, and fibrosis. Radiomic features were extracted, selected, reduced, and used to train the inflammation and fibrosis classification based on random forest models. The performances of classifiers were evaluated by the receiver operating characteristic curve (ROC), accuracy, precision, sensitivity, specificity, and DeLong tests. Result: The random forest model achieved the area under the curve (AUC) of 0.85 and 0.86 for inflammation and fibrosis classification, respectively. Conclusion: This study demonstrated that the MRI-based radiomics features hold potential in the inflammation and fibrosis classification.

BACKGROUND:Radiotherapy treatments are usually planned on computed tomography (CT) images. For head and neck localizations, magnetic resonance imaging (MRI) is also increasingly used for delineation as it provides better soft-tissue contrast. PURPOSE:Treatment planning exclusively based on MRI is currently not straightforward, as there is no direct link between MRI signal intensity and electron density. This study aims to generate a treatment planning using a UTE sequence, for regions with high anatomical variability. METHODS:An ultra-short echo time pulse sequence (1H MRI UTE) was performed on 25 patients with head and neck cancers, treatable by radiotherapy (protocol number R201-004-314), without exclusion due to dental induced artifacts. The hydrogen tissue content, achievable with this sequence can be linked to the electron density of tissues. Generated synthetic CT (sCT) images were compared with reference CT using mean absolute error (MAE) computation. Patient dose calculations were performed on CT and sCT and compared using dose differences, Bland-Altman analysis and global gamma pass rate computation. RESULTS:The mean MAE was 210.9 HU for all patients. The mean 3D global gamma pass rates were 93.1 %, 89.2 % and 80.9 %, for 3 %/3mm, 2 %/2mm and 1 %/1mm criteria respectively. The mean of the median dose difference for the planning tumor volume (PTV) was 0.87 % of 70 Gray. CONCLUSIONS:The UTE sequence enables a direct physical-based method suitable for radiotherapy planning. The proposed method, based on a dedicated acquisition sequence compatible with clinical duration, provided dosimetry results similar to the reference CT, in a region with high anatomical variability.

Contrast methods based on dipolar coupling are of great interest for imaging tissues containing large macromolecules, such as myelin. Most of these conventional methods deal with various "relaxation" phenomena influenced by dipolar coupling such as inhomogeneous magnetization transfer. In this work we propose to investigate the benefit of using another method, called magic sandwich echo (MSE), which allows direct modulation of the dipolar coupling (Hd) as described by the work of Matsui and the Redfield theory. To verify the potential of this method in biological tissue, we first proposed an experimental model for dipolar coupling modulation in an ex vivo tendon (as a highly anisotropic tissue) and used it to prove Hd modulation by varying the amplitude of the spin-lock radiofrequency pulse of this sequence. We then proposed a potential in vivo usable metric, directly related to the residual amount of Hd, which we called MaSteR for Magic sandwich echo to Stimulated echo ratio, as it is based on the ratio of the signal acquired with the MSE sequence and a stimulated echo sequence. First, we show that the higher Hd, the more effective the spin-lock radiofrequency amplitude. We measured with MaSteR that the change in radiofrequency amplitude allowed us to distinguish between different Hd intensities, with a greater MaSteR when Hd is higher.

BACKGROUND:Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management. PURPOSE:To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment). STUDY TYPE:Retrospective. SUBJECTS:Eighty-five patients with CLD (age: 47.9 ± 15.5, 42.4% females). FIELD STRENGTH/SEQUENCE:3-T, spin echo-echo planar imaging (SE-EPI) with 12 b-values (0-800 s/mm2 ). ASSESSMENT:Several models statistical models, stretched exponential model, and intravoxel incoherent motion were simulated. The corresponding parameters (Ds , σ, DDC, α, f, D, D*) were estimated on simulation and in vivo data using the nonlinear least squares (NLS), segmented NLS, and Bayesian methods. The fitting accuracy was analyzed on simulated Rician noised DWI. In vivo, the parameters were averaged from five central slices entire liver to compare correlations with histological features (inflammation, fibrosis, and steatosis). Then, the differences between mild (F0-F2) or severe (F3-F6) groups were compared respecting to statistics and classification. A total of 75.3% of patients used to build various classifiers (stratified split strategy and 10-folders cross-validation) and the remaining for testing. STATISTICAL TESTS:Mean squared error, mean average percentage error, spearman correlation, Mann-Whitney U-test, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, precision. A P-value <0.05 was considered statistically significant. RESULTS:In simulation, the Bayesian method provided the most accurate parameters. In vivo, the highest negative significant correlation (Ds , steatosis: r = -0.46, D*, fibrosis: r = -0.24) and significant differences (Ds , σ, D*, f) were observed for Bayesian fitted parameters. Fibrosis classification was performed with an AUC of 0.92 (0.91 sensitivity and 0.70 specificity) with the aforementioned diffusion parameters based on the decision tree method. DATA CONCLUSION:These results indicate that Bayesian fitted parameters may provide a noninvasive evaluation of fibrosis with decision tree. EVIDENCE LEVEL:1 TECHNICAL EFFICACY: Stage 1.

Chapter 1 MRI Principles, Hardware Components and Quantification Hervé SAINT-JALMES, Hervé SAINT-JALMES LTSI, Inserm, Université de Rennes, FranceSearch for more papers by this authorHélène RATINEY, Hélène RATINEY CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this authorOlivier BEUF, Olivier BEUF CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this author Hervé SAINT-JALMES, Hervé SAINT-JALMES LTSI, Inserm, Université de Rennes, FranceSearch for more papers by this authorHélène RATINEY, Hélène RATINEY CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this authorOlivier BEUF, Olivier BEUF CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this author Hélène Ratiney, Hélène RatineySearch for more papers by this authorOlivier Beuf, Olivier BeufSearch for more papers by this author Book Author(s):Hélène Ratiney, Hélène RatineySearch for more papers by this authorOlivier Beuf, Olivier BeufSearch for more papers by this author First published: 19 April 2024 https://doi.org/10.1002/9781394284030.ch1 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary This chapter describes magnetic resonance imaging (MRI) principles and the main components of an MRI scanner, in particular those responsible for the generation of magnetic fields and signal acquisition. The static magnetic field is employed both for polarizing the sample and imposing the frequency of excitation and signal acquisition. These two aspects determine the specifications and constraints of the magnet. The chapter explains how the magnetization relaxation to equilibrium can be modulated to obtain information on spatial localization or tissue composition. It discusses the principles of encoding an nuclear magnetic resonance (NMR) image performed using gradients of magnetic fields in a rather succinct way. In order to perform the spatial encoding of information, additional magnetic fields have to be superposed to the main static magnetic field. The NMR signal measured by a magnetic flux detector is obtained after the steps of amplification, demodulation, sampling and digitization via analog or digital converters. References Abragam , A. ( 1961 ). The Principles of Nuclear Magnetism . 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Access to state-of-art algorithms in the field of medical imaging is often difficult in hospital environments with a limited adaptation capability. Addressing these challenges, the AWESOMME platform has been developed as a secure compre-hensive web-based toolbox for analyzing medical images, serving clinical, and research purposes while allowing action traceability. New developments have been made to offer improvements for administrators, through a new deployment system, and users with three additional application cases. Each case has a dedicated and independent viewer mode with tailored functionalities: the initial Awesomme Workflow (from segmentation to treatment prediction for osteosarcoma analysis), Prepanext for education on annotations, Score for post-stroke assessment, and HERESP for MRI reconstruction and heterogeneity assessment. It stands as a unique open-source web platform, providing several complete analysis pipelines and enabling a comparison feature, unprece-dented in existing solutions.

Chapter 7 Quantitative Biomechanical Imaging via Magnetic Resonance Elastography Olivier BEUF, Olivier BEUF CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this authorPhilippe GARTEISER, Philippe GARTEISER Centre de recherche sur l'inflammation, Inserm, Paris, FranceSearch for more papers by this authorKevin TSE VE KOON, Kevin TSE VE KOON CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this authorJonathan VAPPOU, Jonathan VAPPOU ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this author Olivier BEUF, Olivier BEUF CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this authorPhilippe GARTEISER, Philippe GARTEISER Centre de recherche sur l'inflammation, Inserm, Paris, FranceSearch for more papers by this authorKevin TSE VE KOON, Kevin TSE VE KOON CREATIS, CNRS, Inserm, INSA Lyon, Université Claude Bernard Lyon 1, FranceSearch for more papers by this authorJonathan VAPPOU, Jonathan VAPPOU ICUBE, CNRS, Université de Strasbourg, FranceSearch for more papers by this author Hélène Ratiney, Hélène RatineySearch for more papers by this authorOlivier Beuf, Olivier BeufSearch for more papers by this author Book Author(s):Hélène Ratiney, Hélène RatineySearch for more papers by this authorOlivier Beuf, Olivier BeufSearch for more papers by this author First published: 19 April 2024 https://doi.org/10.1002/9781394284030.ch7 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary Many pathologies affect the tissue structure and composition at different levels, including cellular modifications and the interactions between cells and their close surroundings, from the extracellular matrix up to the entire organ. Magnetic resonance imaging was adopted as an imaging tool to develop magnetic resonance elastography (MRE). One of the characteristic MRE limitations is related to the duration of the motion-encoding gradients. The goal of fractional encoding is to remove this undesired coupling between the applied mechanical frequency and the available signal at the price of reduced sensitivity to motion. The liver is a preferential MRE target. MRE of the brain is particularly interesting in the sense that assessing this organ via ultrasound elastography methods is very difficult due to the attenuation of the ultrasound in the skull. The chapter illustrates two particularly novel applications: MRE as a biomechanical characterization method and MRE as a guidance method for thermal ablations in interventional MRI. References Asbach , P. , Klatt , D. , Schlosser , B. , Biermer , M. , Muche , M. , Rieger , A. , Loddenkemper , C. , Somasundaram , R. , Berg , T. , Hamm , B. et al. ( 2010 ). 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The excellent contrast between soft tissues in MRI is of great interest for head and neck radiotherapy planning. This work presents an in vivo study aiming to evaluate a multimodal acquisition protocol for tumors and organs at risks delineation and synthetic CT reconstruction based on a density assignment method. Our results showed better contrast between the tumor and healthy tissues with injected T1 MRI compared to injected CT (44,7 +/- 30,5 vs 29,9 +/- 27,2) and acceptable geometric distortions (less than 2mm). Fat content mean absolute error between CT and synthetic CT is about 0,27.

SAR assessment is a major concern in MRI. The energy absorbed by tissues increases quadratically with the static magnetic field; therefore, ultra-high field (>= 7 T) systems require careful dosimetry to exploit their potential. The objectives are to validate the use of electric-field probe for SAR assessment for high-field MRI, and to study the advantages and drawbacks of E-field measurements. The experiments were performed at 7 and 11.7 T on preclinical systems in a phantom with calibrated dielectric properties. Absolute values of the E-field were measured according to position inside a birdcage coil and electrical conductivity, local temperature increase were simultaneously evaluated with operating RF frequency, as well as the re-positioning precision through five repetitive measurements. Results yielded a 14.8 +/- 0.36 W/kg SAR near the coil's capacitors compared to 6.8 +/- 0.17 W/kg estimated at the center of the coil. The temperature rise was nevertheless higher in the center likely due to heat transfer effects. The SAR measured in similar conditions was 5.2 times higher at 11.7 T than at 7 T. The probes induced no visible artefact, and the test to estimate the reproducibility of positioning the sensors granted a low 2.3 % coefficient of variation. Measuring both the cause (E-field) and the effect (temperature rise) yielded different information, both useful in the context of EM simulation validation.

Molecular imaging, also referred to as biological imaging or functional imaging, is the use of non-invasive imaging techniques that enable the visualization of various biological pathways and physiological characteristics of tumours and/or normal tissues. In short, it mainly refers (but not only) to positron emission tomography and magnetic resonance imaging. In clinical oncology, molecular imaging offers the unique opportunity to allow an earlier diagnosis and staging of the disease, to contribute to the selection and delineation of the optimal target volumes before and during (i.e. adaptive treatment) radiotherapy and to a lesser extent before surgery, to monitor the response early on during the treatment or after its completion, and to help in the early detection of recurrence. From the viewpoint of experimental radiation oncology, molecular imaging may bridge radiobiological concepts such as tumour hypoxia, tumour proliferation, tumour stem cell density and tumour radiosensitivity by integrating tumour biological heterogeneity into the treatment planning equation. From the viewpoint of experimental oncology, molecular imaging may also facilitate and speed up the process of drug development by allowing faster and cheaper pharmacokinetic and biodistribution studies.

Precision medicine research benefits from machine learning in the creation of robust models adapted to the processing of patient data. This applies both to pathology identification in images, i.e., annotation or segmentation, and to computer-aided diagnostic for classification or prediction. It comes with the strong need to exploit and visualize large volumes of images and associated medical data. The work carried out in this paper follows on from a main case study piloted in a cancer center. It proposes an analysis pipeline for patients with osteosarcoma through segmentation, feature extraction and application of a deep learning model to predict response to treatment. The main aim of the AWESOMME project is to leverage this work and implement the pipeline on an easy-to-access, secure web platform. The proposed WEB application is based on a three-component architecture: a data server, a heavy computation and authentication server and a medical imaging web-framework with a user interface. These existing components have been enhanced to meet the needs of security and traceability for the continuous production of expert data. It innovates by covering all steps of medical imaging processing (visualization and segmentation, feature extraction and aided diagnostic) and enables the test and use of machine learning models. The infrastructure is operational, deployed in internal production and is currently being installed in the hospital environment. The extension of the case study and user feedback enabled us to fine-tune functionalities and proved that AWESOMME is a modular solution capable to analyze medical data and share research algorithms with in-house clinicians.