Development of a Secure Web-Based Medical Imaging Analysis Platform: The AWESOMME Project.

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.

Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions.

Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.

Towards tumour hypoxia imaging: Incorporating relative oxygen extraction fraction mapping of prostate with multi-parametric quantitative MRI on a 1.5T MR-linac.

Hypoxia plays a central role in tumour radioresistance. Reliable tumour hypoxia imaging would allow the monitoring of tumour response and a more personalized adaptation of radiotherapy planning. Here, we showed a proof of concept of the feasibility and repeatability of relative oxygen extraction fraction (rOEF) mapping of prostate using multi-parametric quantitative MRI (qMRI) achieved for the first time on a 1.5T MR-linac. T2, T2* relaxation times maps, and intra-voxel incoherent motion (IVIM) parametric maps mapping were computed on a 29 years old healthy volunteer. R2' and rOEF maps were calculated based on a multi-parametric model. Long-term repeatability and repeatability coefficient (RC) were determined for each parameter according to QIBA recommendations. Mean values for the entire healthy prostate were 0.99 ± 0.14 × 10-3 mm/s2, 81 ± 2.1 × 10-3 mm/s2, 21.6 ± 3.6%, 92.7 ± 19.7 ms and 62.4 ± 17.3 ms for Dslow, Dfast, f, T2 and T2*, respectively. R2' and rOEF in the prostate were 6.1 ± 3.4 s-1 and 18.2 ± 10.1% respectively. The RC of rOEF was 4.43%. Long-term repeatability of quantitative parameters based on a test-retest ranged from 2 to 18%. qMRI parameters are measurable and repeatable on 1.5T MR LINAC. From T2, T2* and IVIM parameters maps, we were able to obtain a rOEF mapping of the prostate. These results are the first step to a non-invasive imaging of tumour hypoxia during radiotherapy leading to a biological image-guided adaptive radiotherapy.

Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different Fitting Approaches and Diffusion Models.

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.

Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches.

Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER-, PR+ vs. PR-, HER2+ vs. HER2-, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.

Radiomics combined with transcriptomics to predict response to immunotherapy from patients treated with PD-1/PD-L1 inhibitors for advanced NSCLC.

Introduction: In this study, we aim to build radiomics and multiomics models based on transcriptomics and radiomics to predict the response from patients treated with the PD-L1 inhibitor. Materials and methods: One hundred and ninety-five patients treated with PD-1/PD-L1 inhibitors were included. For all patients, 342 radiomic features were extracted from pretreatment computed tomography scans. The training set was built with 110 patients treated at the Léon Bérard Cancer Center. An independent validation cohort was built with the 85 patients treated in Dijon. The two sets were dichotomized into two classes, patients with disease control and those considered non-responders, in order to predict the disease control at 3 months. Various models were trained with different feature selection methods, and different classifiers were evaluated to build the models. In a second exploratory step, we used transcriptomics to enrich the database and develop a multiomic signature of response to immunotherapy in a 54-patient subgroup. Finally, we considered the HOT/COLD status. We first trained a radiomic model to predict the HOT/COLD status and then prototyped a hybrid model integrating radiomics and the HOT/COLD status to predict the response to immunotherapy. Results: Radiomic signature for 3 months' progression-free survival (PFS) classification: The most predictive model had an area under the receiver operating characteristic curve (AUROC) of 0.94 on the training set and 0.65 on the external validation set. This model was obtained with the t-test selection method and with a support vector machine (SVM) classifier. Multiomic signature for PFS classification: The most predictive model had an AUROC of 0.95 on the training set and 0.99 on the validation set. Radiomic model to predict the HOT/COLD status: the most predictive model had an AUROC of 0.93 on the training set and 0.86 on the validation set. HOT/COLD radiomic hybrid model for PFS classification: the most predictive model had an AUROC of 0.93 on the training set and 0.90 on the validation set. Conclusion: In conclusion, radiomics could be used to predict the response to immunotherapy in non-small-cell lung cancer patients. The use of transcriptomics or the HOT/COLD status, together with radiomics, may improve the working of the prediction models.

Optical Link as an Alternative for MRI Receive Coils: Toward a Passive Approach.

OBJECTIVE: The objective of this work was to propose an alternative solution to NMR signal transmission by replacing the coaxial cables of the receiver radiofrequency (RF) coil in the context of MRI so as to improve safety. Starting from the analysis of previous studies and reports on the topic, the difficulty of supplying power wirelessly to an RF coil was identified. To avoid this difficult task, the development of a passive analog optical link was studied. METHODS: In order to quantify the requirements for achieving an analog conversion, the performance of the link was evaluated based on the input NMR signal amplitude and the optical power and compared with that of a galvanic link. Acquisitions were performed on a 7-T preclinical MRI system with a doped saline solution as phantom. A passive and MRI-compatible polarization-state custom-made modulator was tested as well as a commercial Mach-Zehnder interferometer. RESULTS: The conversion was not sensitive enough to keep similar SNRs, but the main source of noise was identified along with parameters for improvement. Optical power emitted by the laser, insertion loss, and full-phase inversion voltage of the modulators were found to be crucial characteristics for the application. These data indicate that custom application devices are required since the frequency, bandwidth, and amplitude of NMR signals are quite different to usual telecommunication signals. CONCLUSION: An electro-optic modulation and a transmission channel were successfully conceived and tested. Images were reconstructed with some significant SNR drawbacks that are expected to be compensated with an appropriate modulator. SIGNIFICANCE: While technical challenges remain, our approach to a two-decades-long problem could solve a major issue of MRI safety by removing the need for supplying on-coil electrical current.

Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.

OBJECTIVES: Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep learning (DL) to predict malignancy in patients with lipomas oratypical lipomatous tumours. METHODS: Cohort include 145 patients affected by lipomatous soft tissue tumours with histology and fat-suppressed gadolinium contrast-enhanced T1-weighted MRI pulse sequence. Images were collected between 2010 and 2019 over 78 centres with non-uniform protocols (three different magnetic field strengths (1.0, 1.5 and 3.0 T) on 16 MR systems commercialised by four vendors (General Electric, Siemens, Philips, Toshiba)). Two approaches have been compared: (i) ML from radiomic features with and without batch correction; and (ii) DL from images. Performances were assessed using 10 cross-validation folds from a test set and next in external validation data. RESULTS: The best DL model was obtained using ResNet50 (resulting into an area under the curve (AUC) of 0.87 ± 0.11 (95% CI 0.65-1). For ML/radiomics, performances reached AUCs equal to 0.83 ± 0.12 (95% CI 0.59-1) and 0.99 ± 0.02 (95% CI 0.95-1) on test cohort using gradient boosting without and with batch effect correction, respectively. On the external cohort, the AUC of the gradient boosting model was equal to 0.80 and for an optimised decision threshold sensitivity and specificity were equal to 100% and 32% respectively. CONCLUSIONS: In this context of limited observations, batch-effect corrected ML/radiomics approaches outperformed DL-based models.

Prediction of Histologic Neoadjuvant Chemotherapy Response in Osteosarcoma Using Pretherapeutic MRI Radiomics.

Histologic response to chemotherapy for osteosarcoma is one of the most important prognostic factors for survival, but assessment occurs after surgery. Although tumor imaging is used for surgical planning and follow-up, it lacks predictive value. Therefore, a radiomics model was developed to predict the response to neoadjuvant chemotherapy based on pretreatment T1-weighted contrast-enhanced MRI. A total of 176 patients (median age, 20 years [range, 5-71 years]; 107 male patients) with osteosarcoma treated with neoadjuvant chemotherapy and surgery between January 2007 and December 2018 in three different centers in France (Centre Léon Bérard in Lyon, Centre Hospitalier Universitaire de Nantes in Nantes, and Hôpital Cochin in Paris) were retrospectively analyzed. Various models were trained from different configurations of the data sets. Two different methods of feature selection were tested with and without ComBat harmonization (ReliefF and t test) to select the most relevant features, and two different classifiers were used to build the models (an artificial neural network and a support vector machine). Sixteen radiomics models were built using the different combinations of feature selection and classifier applied on the various data sets. The most predictive model had an area under the receiver operating characteristic curve of 0.95, a sensitivity of 91%, and a specificity 92% in the training set; respective values in the validation set were 0.97, 91%, and 92%. In conclusion, MRI-based radiomics may be useful to stratify patients receiving neoadjuvant chemotherapy for osteosarcomas. Keywords: MRI, Skeletal-Axial, Oncology, Radiomics, Osteosarcoma, Pediatrics Supplemental material is available for this article. © RSNA, 2022.

Polyphenol Supplementation Did Not Affect Insulin Sensitivity and Fat Deposition During One-Month Overfeeding in Randomized Placebo-Controlled Trials in Men and in Women.

Two randomized placebo-controlled double-blind paralleled trials (42 men in Lyon, 19 women in Lausanne) were designed to test 2 g/day of a grape polyphenol extract during 31 days of high calorie-high fructose overfeeding. Hyperinsulinemic-euglycemic clamps and test meals with [1,1,1-13C3]-triolein were performed before and at the end of the intervention. Changes in body composition were assessed by dual-energy X-ray absorptiometry (DEXA). Fat volumes of the abdominal region and liver fat content were determined in men only, using 3D-magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) at 3T. Adipocyte's size was measured in subcutaneous fat biopsies. Bodyweight and fat mass increased during overfeeding, in men and in women. While whole body insulin sensitivity did not change, homeostasis model assessment of insulin resistance (HOMA-IR) and the hepatic insulin resistance index (HIR) increased during overfeeding. Liver fat increased in men. However, grape polyphenol supplementation did not modify the metabolic and anthropometric parameters or counteract the changes during overfeeding, neither in men nor in women. Polyphenol intake was associated with a reduction in adipocyte size in women femoral fat. Grape polyphenol supplementation did not counteract the moderated metabolic alterations induced by one month of high calorie-high fructose overfeeding in men and women. The clinical trials are registered under the numbers NCT02145780 and NCT02225457 at ClinicalTrials.gov and available at https://clinicaltrials.gov/ct2/show/NCT02145780 and https://clinicaltrials.gov/ct2/show/NCT02225457.

Short echo time dual-frequency MR Elastography with Optimal Control RF pulses.

Magnetic Resonance Elastography (MRE) quantifies the mechanical properties of tissues, typically applying motion encoding gradients (MEG). Multifrequency results allow better characterizations of tissues using data usually acquired through sequential monofrequency experiments. High frequencies are difficult to reach due to slew rate limitations and low frequencies induce long TEs, yielding magnitude images with low SNR. We propose a novel strategy to perform simultaneous multifrequency MRE in the absence of MEGs: using RF pulses designed via the Optimal Control (OC) theory. Such pulses control the spatial distribution of the MRI magnetization phase so that the resulting transverse magnetization reproduces the phase pattern of an MRE acquisition. The pulse is applied with a constant gradient during the multifrequency mechanical excitation to simultaneously achieve slice selection and motion encoding. The phase offset sampling strategy can be adapted according to the excitation frequencies to reduce the acquisition time. Phantom experiments were run to compare the classical monofrequency MRE to the OC based dual-frequency MRE method and showed excellent agreement between the reconstructed shear storage modulus G'. Our method could be applied to simultaneously acquire low and high frequency components, which are difficult to encode with the classical MEG MRE strategy.

Comparison of high-resolution magnetic resonance imaging and micro-computed tomography arthrography for in-vivo assessment of cartilage in non-human primate models.

BACKGROUND: Non-human primate (NHP) could be an interesting model for osteoarthritis (OA) longitudinal studies but standard medical imaging protocols are not able to acquire sufficiently high-resolution images to depict the thinner cartilage (compared to human) in an in vivo context. The aim of this study was thus to develop and validate the acquisition protocols for knee joint examination of NHP using magnetic resonance imaging (MRI) at 1.5 T and X-ray micro-computed tomography arthrography (µCTA). METHODS: The first phase of the study focused on developing dedicated in vivo HR-MRI and µCTA protocols for simultaneous acquisitions of both knee joints on NHP. For MR, a dedicated two-channel receiver array coil and acquisition sequence were developed on a 1.5 T Siemens Sonata system and tuned to respect safety issues and reasonable examination time. For µCTA, an experimental setup was devised so as to fulfill similar requirements. The two imaging protocols were used during a longitudinal study so as to confirm that repeated injections of loxaglic acid (contrast agent used for µCTA) didn't induce any bias in cartilage assessment and to compare segmentation results from the two modalities. Lateral and medial cartilage tibial plateaus were assessed using a common image processing protocol leading to a 3D estimation of the cartilage thickness. RESULTS: From HR-MRI and µCTA images, thickness distributions were extracted allowing for proper evaluation of knee cartilage thickness of the primates. Results obtained in vivo indicated that the µCTA protocol did not induce any bias in the measured cartilage parameters and moreover, segmentation results obtained from the two imaging modalities were consistent. CONCLUSIONS: MR and µCTA are valuable imaging tools for the morphological evaluation of cartilage in NHP models which in turn can be used for OA studies.

Multicontrast MRI-based radiomics for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with early triple negative breast cancer.

INTRODUCTION: To assess pre-therapeutic MRI-based radiomic analysis to predict the pathological complete response to neoadjuvant chemotherapy (NAC) in women with early triple negative breast cancer (TN). MATERIALS AND METHODS: This monocentric retrospective study included 75 TN female patients with MRI (T1-weighted, T2-weighted, diffusion-weighted and dynamic contrast enhancement images) performed before NAC. For each patient, the tumor(s) and the parenchyma were independently segmented and analyzed with radiomic analysis to extract shape, size, and texture features. Several sets of features were realized based on the 4 different sequence images. Performances of 4 classifiers (random forest, multilayer perceptron, support vector machine (SVM) with linear or quadratic kernel) were compared based on pathological complete response (defined on the excised tissues), on 100 draws with 75% as training set and 25% as test. RESULTS: The combination of features extracted from different MR images improved the classifier performance (more precisely, the features from T1W, T2W and DWI). The SVM with quadratic kernel showed the best performance with a mean AUC of 0.83, a sensitivity of 0.85 and a specificity of 0.75 in the test set. CONCLUSION: MRI-based radiomics may be relevant to predict NAC response in TN cancer. Our results promote the use of multi-contrast MRI sources for radiomics, providing enrich source of information to enhance model generalization.

Quantitative Magnetic Resonance Imaging Assessment of the Quadriceps Changes during an Extreme Mountain Ultramarathon.

INTRODUCTION/PURPOSE: Extreme ultra-endurance races are growing in popularity, but their effects on skeletal muscles remain mostly unexplored. This longitudinal study explores physiological changes in mountain ultramarathon athletes' quadriceps using quantitative magnetic resonance imaging (MRI) coupled with serological biomarkers. The study aimed to monitor the longitudinal effect of the race and recovery and to identify local inflammatory and metabolic muscle responses by codetection of biological markers. METHODS: An automatic image processing framework was designed to extract imaging-based biomarkers from quantitative MRI acquisitions of the upper legs of 20 finishers at three time points. The longitudinal effect of the race was demonstrated by analyzing the image markers with dedicated biostatistical analysis. RESULTS: Our framework allows for a reliable calculation of statistical data not only inside the whole quadriceps volume but also within each individual muscle head. Local changes in MRI parameters extracted from quantitative maps were described and found to be significantly correlated with principal serological biomarkers of interest. A decrease in the PDFF after the race and a stable paramagnetic susceptibility value were found. Pairwise post hoc tests suggested that the recovery process differs among the muscle heads. CONCLUSIONS: This longitudinal study conducted during a prolonged and extreme mechanical stress showed that quantitative MRI-based markers of inflammation and metabolic response can detect local changes related to the prolonged exercise, with differentiated involvement of each head of the quadriceps muscle as expected in such eccentric load. Consistent and efficient extraction of the local biomarkers enables to highlight the interplay/interactions between blood and MRI biomarkers. This work indeed proposes an automatized analytic framework to tackle the time-consuming and mentally exhausting segmentation task of muscle heads in large multi-time-point cohorts.

Harmonic wideband simultaneous dual-frequency MR Elastography.

Magnetic resonance elastography (MRE) is used to non-invasively quantify viscoelastic properties of tissues based on the measurement of propagation characteristics of shear waves. Because some of these viscoelastic parameters show a frequency dependence, multifrequency analysis allows us to measure the wave propagation dispersion, leading to a better characterization of tissue properties. Conventionally, motion encoding gradients (MEGs) oscillating at the same frequency as the mechanical excitation encode motion. Hence, multifrequency data is usually obtained by sequentially repeating monochromatic wave excitations experiments at different frequencies. The result is that the total acquisition time is multiplied by a factor corresponding to the number of repetitions of monofrequency experiments, which is a major limitation of multifrequency MRE. In order to make it more accessible, a novel single-shot harmonic wideband dual-frequency MRE method is proposed. Two superposed shear waves of different frequencies are simultaneously generated and propagate in a sample. Trapezoidal oscillating MEGs are used to encode mechanical vibrations having frequencies that are an odd multiple of the MEG frequency. The number of phase offsets is optimized to reduce the acquisition time. For this purpose, a sampling method not respecting the Shannon theorem is used to produce a controlled temporal aliasing that allows us to encode both frequencies without any additional examination time. Phantom experiments were run to compare conventional monofrequency MRE with the single-shot dual-frequency MRE method and showed excellent agreement between the reconstructed shear storage moduli G'. In addition, dual-frequency MRE yielded an increased signal-to-noise ratio compared with conventional monofrequency MRE acquisitions when encoding the high frequency component. The novel proposed multifrequency MRE method could be applied to simultaneously acquire more than two frequency components, reducing examination time. Further studies are needed to confirm its applicability in preclinical and clinical models.

MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study.

OBJECTIVES: To develop and validate a MRI-based radiomic method to predict malignancies in lipomatous soft tissue tumors. METHODS: This retrospective study searched in the database of our pathology department, data from patients with lipomatous soft tissue tumors, with histology and gadolinium-contrast enhanced T1w MR images, obtained from 56 centers with non-uniform protocols. For each tumor, 87 radiomic features were extracted by two independent observers to evaluate the inter-observer reproducibility. A reduction of learning base dimension was performed from reproducibility and relevancy criteria. A model was subsequently prototyped using a linear support vector machine to predict malignant lesions. RESULTS: Eighty-one subjects with lipomatous soft tissue tumors including 40 lipomas and 41 atypical lipomatous tumors or well-differentiated liposarcomas with fat-suppressed T1w contrast enhanced MR images available were retrospectively enrolled. Based on a Pearson's correlation coefficient threshold at 0.8, 55 out of 87 (63.2%) radiomic features were considered reproducible. Further introduction of relevancy finally selected 35 radiomic features to be integrated in the model. To predict malignant tumors, model diagnostic performances were as follow: AUROC = 0.96; sensitivity = 100%; specificity = 90%; positive predictive value = 90.9%; negative predictive value = 100% and overall accuracy = 95.0%. CONCLUSION: This work demonstrates that radiomics allows to predict malignancy in soft tissue lipomatous tumors with routinely used MR acquisition in clinical oncology. These encouraging results need to be further confirmed in an external validation population.

Feasibility and characterization of a safe susceptibility-matched endorectal coil for MR spectroscopy.

When using endorectal coils, local radiofrequency (RF) heating may occur in the surrounding tissue. Furthermore, most endorectal coils create a susceptibility artifact detrimental to both anatomical magnetic resonance imaging (MRI) and spectroscopy (MRS) acquisitions. We aimed at assessing the safety and MRS performance of a susceptibility-matched endorectal coil for further rectal wall analysis. Experiments were performed on a General Electric MR750 3 T scanner. A variable number of miniaturized passive RF traps were incorporated in the reception cable. The assessment of RF heating and coil sensitivity was conducted on a 1.5% agar-agar phantom doped with NaCl. Several susceptibility-matched materials such as Ultem, perfluorocarbon and barium sulfate were then compared with an external coil. Finally, Ultem was used as a solid support for an endorectal coil and compared with a reference coil. Phantom experiments exhibited a complete suppression of both the RF heating phenomenon and the coil sensitivity artifact. Ultem was the material that produced the smallest image distortion. The full width at half maximum of MR spectra acquired using the susceptibility-matched endorectal coil showed at least 30% narrowing compared with a reference endorectal coil. A susceptibility-matched endorectal coil with RF traps incorporated was validated on phantoms. This coil appears to be a promising device for future in vivo experiments.

On the identification of the blood vessel confounding effect in intravoxel incoherent motion (IVIM) Diffusion-Weighted (DW)-MRI in liver: An efficient sparsity based algorithm.

IntraVoxel Incoherent Motion (IVIM) Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is of great interest for evaluating tissue diffusion and perfusion and producing parametric maps in clinical applications for liver pathologies. However, the presence of macroscopic blood vessels (not capillaries) in a given Region of Interest (ROI) results in a confounding effect that bias the quantification of tissue perfusion. Therefore, it is necessary to identify those voxels affected by blood vessels. In this paper, an efficient algorithm for an automatic identification of blood vessels in a given ROI is proposed. It relies on the sparsity of the spatial distribution of blood vessels. This sparsity prior can be easily incorporated using the all-voxel IVIM-MRI model introduced in this paper. In addition to the identification of blood vessels, the proposed algorithm provides a quantification of blood vessels, tissue diffusion and tissue perfusion of all voxels in a given ROI, in one single step. Besides, two strategies are proposed in this paper to deal with the nonnegativity of the model parameters. The efficiency of the proposed algorithm compared to the Non-Negative Least Square (NNLS)-based method, recently introduced to deal with the confounding blood vessel effect in the IVIM-MRI model, is confirmed using both realistic and real DW-MR images.

The apparent mechanical effect of isolated amyloid-ß and α-synuclein aggregates revealed by multi-frequency MRE.

Several biological processes are involved in dementia, and fibrillar aggregation of misshaped endogenous proteins appears to be an early hallmark of neurodegenerative disease. A recently developed means of studying neurodegenerative diseases is magnetic resonance elastography (MRE), an imaging technique investigating the mechanical properties of tissues. Although mechanical changes associated with these diseases have been detected, the specific signal of fibrils has not yet been isolated in clinical or preclinical studies. The current study aims to exploit the fractal-like properties of fibrils to separate them from nonaggregated proteins using a multi-frequency MRE power law exponent in a phantom study. Two types of fibril, α-synuclein (α-Syn) and amyloid-ß (Aß), and a nonaggregated protein, bovine serum albumin, used as control, were incorporated in a dedicated nondispersive agarose phantom. Elastography was performed at multiple frequencies between 400 and 1200 Hz. After 3D-direct inversion, storage modulus (G'), phase angle (ϕ), wave speed and the power law exponent (y) were computed. No significant changes in G' and ϕ were detected. Both α-Syn and Aß inclusions showed significantly higher y values than control inclusions (P = 0.005) but did not differ between each other. The current phantom study highlighted a specific biomechanical effect of α-Syn and Aß aggregates, which was better captured with the power law exponent derived from multi-frequency MRE than with single frequency-derived parameters.

[Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data]. / Prédiction de la réponse à la chimiothérapie des ostéosarcomes à partir des données radiomiques issues des IRM diagnostiques.

INTRODUCTION: Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response to neoadjuvant chemotherapy determines continuation of treatment and is also a powerful prognostic factor. There are currently no reliable ways to evaluate it early. The aim is to develop a method to predict the chemotherapy response using radiomics from pre-treatment MRI. METHODS: Clinical characteristics and MRI of patients treated for local or metastatic osteosarcoma were collected retrospectively in the Rhône-Alpes region, from 2007 to 2016. On initial MRI exams, each tumor was segmented by expert radiologist and 87 radiomic features were extracted automatically. Univariate analysis was performed to assess each feature's association with histological response following neoadjuvante chemotherapy. To distinguish good histological responder from poor, we built predictive models based on support vector machines. Their classification performance was assessed with the area under operating characteristic curve receiver (AUROC) from test data. RESULTS: The analysis focused on the MRIs of 69 patients, 55.1% (38/69) of whom were good histological responders. The model obtained by support vector machines from initial MRI radiomic data had an AUROC of 0.98, a sensitivity of 100% (IC 95% [100%-100%]) and specificity of 86% (IC 95% [59.7%-111%]). DISCUSSION: Radiomic based on MRI data would predict the chemotherapy response before treatment initiation, in patients treated for osteosarcoma.