Development, evaluation and validation of machine learning models to predict hospitalizations of patients with coronary artery disease within the next 12 months.

BACKGROUND: Improved survival of patients after acute coronary syndromes, population growth, and overall life expectancy rise have led to a significant increase in the proportion of patients with stable coronary artery disease (CAD), creating a significant load on the entire healthcare system. The disease often progresses with the development of many complications while significantly increasing the likelihood of hospitalization. Developing and applying a machine learning model for predicting hospitalizations of patients with CAD to an inpatient medical facility will allow for close monitoring of high-risk patients, early preventive interventions, and optimized medical care. AIMS: Development and external validation of personalized models for predicting the preventable hospitalizations of patients with stable CAD and its complications using ML algorithms and data of real-world clinical practice. METHODS: 135,873 depersonalized electronic health records of 49,103 patients with stable CAD were included in the study. Anthropometric measurements, physical examination results, laboratory, instrumental, anamnestic, and socio-demographic data, widely used in routine medical practice, were considered as potential predictors, a total of 73 features. Logistic regression, decision tree-based methods including gradient boosting (AdaBoost, LightGBM, XGBoost, CatBoost) and bagging (RandomForest and ExtraTrees), discriminant analysis (LinearDiscriminant, QuadraticDiscriminant), and naive Bayes classifier were compared. External validation was performed on the data of a separate region. RESULTS: The best results and stability to external validation data were shown by the CatBoost model with an AUC of 0.875 (95% CI 0.865-0.885) for the internal testing and 0.872 (95% CI 0.856-0.886) for the external validation. The best model showed good performance evaluated through AUROC, Brier score and standardized net benefit (for the target NPV threshold) for the validation dataset that was only slightly similar to the train data. CONCLUSION: The metrics of the best model were superior to previously published studies. The results of external validation demonstrated the relative stability of the model to new data from another region that confirms the possibility of the model's application in real clinical practice.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Clinical application of radiological AI for pulmonary nodule evaluation: Replicability and susceptibility to the population shift caused by the COVID-19 pandemic.

PURPOSE: replicability and generalizability of medical AI are the recognized challenges that hinder a broad AI deployment in clinical practice. Pulmonary nodes detection and characterization based on chest CT images is one of the demanded use cases for automatization by means of AI, and multiple AI solutions addressing this task are becoming available. Here, we evaluated and compared the performance of several commercially available radiological AI with the same clinical task on the same external datasets acquired before and during the pandemic of COVID-19. APPROACH: 5 commercially available AI models for pulmonary nodule detection were tested on two external datasets labelled by experts according to the intended clinical task. Dataset1 was acquired before the pandemic and did not contain radiological signs of COVID-19; dataset2 was collected during the pandemic and did contain radiological signs of COVID-19. ROC-analysis was applied separately for the dataset1 and dataset2 to select probability thresholds for each dataset separately. AUROC, sensitivity and specificity metrics were used to assess and compare the results of AI performance. RESULTS: Statistically significant differences in AUROC values were observed between the AI models for the dataset1. Whereas for the dataset2 the differences of AUROC values became statistically insignificant. Sensitivity and specificity differed statistically significantly between the AI models for the dataset1. This difference was insignificant for the dataset2 when we applied the probability threshold initially selected for the dataset1. An update of the probability threshold based on the dataset2 created statistically significant differences of sensitivity and specificity between AI models for the dataset2. For 3 out of 5 AI models, the update of the probability threshold was valuable to compensate for the degradation of AI model performances with the population shift caused by the pandemic. CONCLUSIONS: Population shift in the data is able to deteriorate differences of AI models performance. Update of the probability threshold together with the population shift seems to be valuable to preserve AI models performance without retraining them.

An International Non-Inferiority Study for the Benchmarking of AI for Routine Radiology Cases: Chest X-ray, Fluorography and Mammography.

An international reader study was conducted to gauge an average diagnostic accuracy of radiologists interpreting chest X-ray images, including those from fluorography and mammography, and establish requirements for stand-alone radiological artificial intelligence (AI) models. The retrospective studies in the datasets were labelled as containing or not containing target pathological findings based on a consensus of two experienced radiologists, and the results of a laboratory test and follow-up examination, where applicable. A total of 204 radiologists from 11 countries with various experience performed an assessment of the dataset with a 5-point Likert scale via a web platform. Eight commercial radiological AI models analyzed the same dataset. The AI AUROC was 0.87 (95% CI:0.83-0.9) versus 0.96 (95% CI 0.94-0.97) for radiologists. The sensitivity and specificity of AI versus radiologists were 0.71 (95% CI 0.64-0.78) versus 0.91 (95% CI 0.86-0.95) and 0.93 (95% CI 0.89-0.96) versus 0.9 (95% CI 0.85-0.94) for AI. The overall diagnostic accuracy of radiologists was superior to AI for chest X-ray and mammography. However, the accuracy of AI was noninferior to the least experienced radiologists for mammography and fluorography, and to all radiologists for chest X-ray. Therefore, an AI-based first reading could be recommended to reduce the workload burden of radiologists for the most common radiological studies such as chest X-ray and mammography.

A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis.

In 2020, an experiment testing AI solutions for lung X-ray analysis on a multi-hospital network was conducted. The multi-hospital network linked 178 Moscow state healthcare centers, where all chest X-rays from the network were redirected to a research facility, analyzed with AI, and returned to the centers. The experiment was formulated as a public competition with monetary awards for participating industrial and research teams. The task was to perform the binary detection of abnormalities from chest X-rays. For the objective real-life evaluation, no training X-rays were provided to the participants. This paper presents one of the top-performing AI frameworks from this experiment. First, the framework used two EfficientNets, histograms of gradients, Haar feature ensembles, and local binary patterns to recognize whether an input image represents an acceptable lung X-ray sample, meaning the X-ray is not grayscale inverted, is a frontal chest X-ray, and completely captures both lung fields. Second, the framework extracted the region with lung fields and then passed them to a multi-head DenseNet, where the heads recognized the patient's gender, age and the potential presence of abnormalities, and generated the heatmap with the abnormality regions highlighted. During one month of the experiment from 11.23.2020 to 12.25.2020, 17,888 cases have been analyzed by the framework with 11,902 cases having radiological reports with the reference diagnoses that were unequivocally parsed by the experiment organizers. The performance measured in terms of the area under receiving operator curve (AUC) was 0.77. The AUC for individual diseases ranged from 0.55 for herniation to 0.90 for pneumothorax.

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative.

OBJECTIVE: The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)-based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. METHODS: The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0-100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. RESULTS: The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). CONCLUSIONS: Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. KEY POINTS: • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.

Quadrature transceive wireless coil: Design concept and application for bilateral breast MRI at 1.5 T.

PURPOSE: Development of a novel quadrature inductively driven transceive wireless coil for breast MRI at 1.5 T. METHODS: A quadrature wireless coil (HHMM-coil) design has been developed as a combination of two linearly polarized coils: a pair of 'metasolenoid' coils (MM-coil) and a pair of Helmholtz-type coils (HH-coil). The MM-coil consisted of an array of split-loop resonators. The HH-coil design included two electrically connected flat spirals. All the wireless coils were coupled to a whole-body birdcage coil. The HHMM-coil was studied and compared to the linear coils in terms of transmit and SAR efficiencies via numerical simulations. A prototype of HHMM-coil was built and tested on a 1.5 T scanner in a phantom and healthy volunteer. We also proposed an extended design of the HHMM-coil and compared its performance to a dedicated breast array. RESULTS: Numerical simulations of the HHMM-coil with a female voxel model have shown more than a 2.5-fold increase in transmit efficiency and a 1.7-fold enhancement of SAR efficiency compared to the linearly polarized coils. Phantom and in vivo imaging showed good agreement with the numerical simulations. Moreover, the HHMM-coil provided good image quality, visualizing all areas of interest similar to a multichannel breast array with a 32% reduction in signal-to-noise ratio. CONCLUSION: The proposed quadrature HHMM-coil allows the B 1 + $$ {\mathrm{B}}_1^{+} $$ -field to be significantly better focused in the region-of-interest compared to the linearly polarized coils. Thus, the HHMM-coil provides high-quality breast imaging on a 1.5 T scanner using a whole-body birdcage coil for transmit and receive.

Changes in software as a medical device based on artificial intelligence technologies.

PURPOSE: to develop a procedure for registering changes, notifying users about changes made, unifying software as a medical device based on artificial intelligence technologies (SaMD-AI) changes, as well as requirements for testing and inspections-quality control before and after making changes. METHODS: The main types of changes, divided into two groups-major and minor. Major changes imply a subsequent change of a SaMD-AI version to improve efficiency and safety, to change the functionality, and to ensure the processing of new data types. Minor changes imply those that SaMD-AI developers can make due to errors in the program code. Three types of SaMD-AI testings are proposed to use: functional testing, calibration testing or control, and technical testing. RESULTS: The presented approaches for validation SaMD-AI changes were introduced. The unified requirements for the request for changes and forms of their submission made this procedure understandable for SaMD-AI developers, and also adjusted the workload for the Experiment experts who checked all the changes made to SaMD-AI. CONCLUSION: This article discusses the need to control changes in the module of SaMD-AI, as innovative products influencing medical decision making. It justifies the need to control a module operation of SaMD-AI after making changes. To streamline and optimize the necessary and sufficient control procedures, a systematization of possible changes in SaMD-AI and testing methods was carried out.

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.

OBJECTIVE: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)-based classification in a multi-demographic setting. METHODS: This multi-institutional review boards-approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18-100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS-based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. RESULTS: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the "wavelet_(LH)_GLCM_Imc1" feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. CONCLUSION: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. KEYPOINTS: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92.

Volumetric wireless coil for wrist MRI at 1.5 T as a practical alternative to Tx/Rx extremity coil: a comparative study.

This work performs a detailed assessment of radiofrequency (RF) safety and imaging performance of a volumetric wireless coil based on periodically coupled split-loop resonators (SLRs) for 1.5 T wrist MRI versus a commercially available transceive extremity coil. In particular, we evaluated the transmit efficiency and RF safety for three setups: a whole-body birdcage coil, a transceive extremity birdcage coil, and a volumetric wireless coil inductively coupled to the whole-body birdcage coil. The imaging performance of the two latter setups was studied experimentally for nine subjects. The signal-to-noise ratio (SNR) of the images acquired with several standard pulse sequences for osteoarthritis wrist imaging was assessed. Application of the wireless coil significantly improved the specific absorption rate (SAR) efficiency of the whole-body birdcage coil, with at least 4.3-fold and 7.6-fold improvement of local and global SAR efficiencies, respectively. This setup also outperformed the transceive extremity coil in terms of SNR (up to 1.40-fold gain) with a moderate (11%) reduction of the local SAR efficiency.

Comparative analysis of SINC-shaped and SLR pulses performance for contiguous multi-slice fast spin-echo imaging using metamaterial-based MRI.

OBJECTIVE: To comparatively assess the performance of highly selective pulses computed with the SLR algorithm in fast-spin echo (FSE) within the current radiofrequency safety limits using a metamaterial-based coil for wrist magnetic resonance imaging. METHODS: Apodized SINC pulses commonly used for clinical FSE sequences were considered as a reference. Selective SLR pulses with a time-bandwidth product of four were constructed in the MATPULSE program. Slice selection profiles in conventional T1-weighted and PD-weighted FSE wrist imaging pulse sequences were modeled using a Bloch equations simulator. Signal evolution was assessed in three samples with relaxation times equivalent to those in musculoskeletal tissues at 1.5T. Regular and SLR-based FSE pulse sequences were tested in a phantom experiment in a multi-slice mode with different gaps between slices and the direct saturation effect was investigated. RESULTS: As compared to the regular FSEs with a conventional transmit coil, combining the utilization of the metadevice with SLR-based FSEs provided a 23 times lower energy deposition in a duty cycle. When the slice gap was decreased from 100 to 0%, the "slice cross-talk" effect reduced the signal intensity by 15.9-17.6% in the SLR-based and by 22.9-32.3% in the regular T1-weighted FSE; and by 0.0-6.4% in the SLR-based and by 0.3-9.3% in the regular PD-weighted FSE. DISCUSSION AND CONCLUSION: SLR-based FSE together with the metadevice allowed to increase the slice selectivity while still being within the safe SAR limits. The "slice cross-talk" effects were conditioned by the number of echoes in the echo train, the repetition time, and T1 relaxation times. The approach was more beneficial for T1-weighted SLR-based FSE as compared to PD-weighted. The combination of the metadevice and SLR-based FSE offers a promising alternative for MR investigations that require scanning in a "Low-SAR" regime such as those for children, pregnant women, and patients with implanted devices.

Metamaterial inspired wireless coil for clinical breast imaging.

In this work, we propose an application of a metamaterial inspired volumetric wireless coil (WLC) based on coupled split-loop resonators for targeted breast MRI at 1.5 T. Due to strong electromagnetic coupling with the body coil, the metamaterial inspired WLC locally focuses radiofrequency (RF) magnetic flux in the target region, thus improving both transmit and receive performance of the external body coil. This leads to substantial enhancement in local transmit efficiency and improvement of RF safety. Phantom images showed a tenfold increase of signal-to-noise ratio (SNR) in the region-of-interest (ROI) and, at the same time, an almost 50-fold reduction in transmit power relative to the same body coil used alone.

Ceramic resonators for targeted clinical magnetic resonance imaging of the breast.

Currently, human magnetic resonance (MR) examinations are becoming highly specialized with a pre-defined and often relatively small target in the body. Conventionally, clinical MR equipment is designed to be universal that compromises its efficiency for small targets. Here, we present a concept for targeted clinical magnetic resonance imaging (MRI), which can be directly integrated into the existing clinical MR systems, and demonstrate its feasibility for breast imaging. The concept comprises spatial redistribution and passive focusing of the radiofrequency magnetic flux with the aid of an artificial resonator to maximize the efficiency of a conventional MR system for the area of interest. The approach offers the prospect of a targeted MRI and brings novel opportunities for high quality specialized MR examinations within any existing MR system.

Deep learning-based fully automatic segmentation of wrist cartilage in MR images.

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in 20 multi-slice MRI datasets acquired with two different coils in 11 subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and PB-U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sørensen-Dice similarity coefficient [DSC] = 0.81) in the representative (central coronal) slices with a large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC = 0.78-0.88 and 0.9, respectively). The proposed deep learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy.

A Coaxial RF Applicator for Ultra-High Field Human MRI.

OBJECTIVE: To develop a novel radio-frequency (RF) concept for ultra-high field (UHF) human magnetic resonance imaging (MRI) based on a coaxial resonant cavity. METHODS: A two-channel slotted coaxial cavity RF applicator was designed for human head MRI at 9.4T. Physical dimensions made the proposed conducting structure resonant at the required frequency without tuning lumped elements. Numerical electromagnetic modeling was used to optimize the design. RF safety was assessed with two representative human body models. MR experiments on a 9.4T scanner included gradient echo images and mapping of a circularly polarized RF magnetic field in the human head phantom. RESULTS: The simulations and the phantom MR experiments agreed both qualitatively and quantitatively. The design was relatively simple, robust and required only a few additional reactive elements for the applicator's input impedance matching. The transmit efficiency and homogeneity of the excitation field were only 20% and 4% lower compared to a conventional 8-channel head array. CONCLUSION: The coaxial RF applicator was feasible for human MRI at UHF and required no lumped elements for its tuning. Imaging performance of the RF applicator was only moderately lower compared to the conventional transmit array, but would be sufficient to provide an anatomical reference for the heteronuclei MRI. SIGNIFICANCE: An alternative approach with the minimal involvement of lumped elements becomes feasible to design volume-type RF coils for UHF human MRI.

Prospective Respiration Detection in Magnetic Resonance Imaging by a Non-Interfering Noise Navigator.

Passive monitoring of the thermal noise variances of the channels of a receive array was shown to reveal respiratory motion of the underlying anatomy, a so called "noise navigator". There is, however, an inevitable trade off between the accuracy and temporal resolution of the noise navigator due to its passive nature. A temporal filter has to be added to the noise navigator to accurately reveal respiration and retain temporal resolution. For real-time applications of the noise navigator, e.g., prospective motion correction or motion tracking, the added filter must be prospective. Thus a prospective Kalman filter was designed to predict respiration from the noise navigator without a temporal delay. The performance of the noise navigator enhanced by this prospective Kalman filter was explored and the robustness of the proposed method was assessed on healthy volunteers. The respiratory signal could be measured by the noise navigator independent of magnetic resonance acquisition. The calculated respiratory signal was qualitatively compared with the respiratory bellows. In addition, a strong linear relationship was found between the prospective noise navigator and a quantitative 2-D image navigator for measurements, including free and tasked breathing.

The feasibility of semi-automatically generated red bone marrow segmentations based on MR-only for patients with gynecologic cancer.

PURPOSE: For patients with cervical cancer the delivery of chemotherapy with radiotherapy improves survival compared with radiotherapy alone. However, high rates of acute hematologic toxicity occur when combining both therapies due to the damage of the red bone marrow (RBM). This study aimed to reduce the radiation damage to the RBM. A tool has been developed for semi-automatic delineation of the red bone marrow based on MR-only. This delineation can be included into the treatment planning process to reduce the volume of RBM irradiated in patients receiving pelvic radiation therapy. METHODS: 13 patients with cervical cancer were enrolled. All the patients underwent MR, CT and FDG-PET imaging. A tool for RBM determination from water and fat MR images was developed. Our MR-based RBM tool was optimized and validated with the FDG-PET scans of the patients. RESULTS: Our tool identified RBM regions in the pelvic area. The mean total volume of these regions was 34% of the pelvic bone marrow. The corresponding SUV values based on the FDG-PET scans were above the reported threshold of active/red bone marrow. CONCLUSION: This study shows that delineations of the RBM for the radiotherapy with RBM sparing can be generated semi-automatically using MR scans only.

Dielectric waveguides for ultrahigh field magnetic resonance imaging.

PURPOSE: The design of RF coils for MRI transmit becomes increasingly challenging at high frequencies required for MRI at 7T and above. Our goal is to show a proof of principle of a new type of transmit coil for higher field strengths. METHOD: We demonstrate an alternative transmit coil design based on dielectric waveguide principles which transfers energy via evanescent wave coupling. The operating principles and conditions are explored by simulations. The waveguide is applied for in vivo imaging at 7T. RESULTS: The waveguide can be an efficient transmit coil when four conditions are fulfilled: (1) the waveguide should be operated just above the cutoff frequency of the lowest order transverse electric mode, (2) the waveguide should not operate at a frequency where the wavelength fits an integer number of times in the waveguide length and standing wave patterns become very prominent, (3) for homogeneous excitation, the waveguide should be bent around the object, and (4) there should be an air gap between the waveguide and the object. CONCLUSIONS: By choosing the dielectric and the dimensions adequately, the dielectric waveguide couples the magnetic field efficiently into the body. The waveguide can be redesigned for higher frequencies by simple adaptations and may be a promising transmit alternative. Magn Reson Med 76:1314-1324, 2016. © 2015 Wiley Periodicals, Inc.

An optimization framework to maximize signal-to-noise ratio in simultaneous multi-slice body imaging.

Parallel imaging is essential for the acceleration of abdominal and pelvic 2D multi-slice imaging, in order to reduce scan time and mitigate motion artifacts. Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) accelerated imaging has been shown to increase the signal-to-noise ratio (SNR) significantly compared with in-plane parallel imaging with similar acceleration. We hypothesize that for CAIPIRINHA-accelerated abdominal imaging the consistency of image quality and SNR is more difficult to achieve due to the subject-specific coil sensitivity profiles, caused by (1) flexible coil placement; (2) variations in anatomy; and (3) variations in scan coverage along the superior-inferior direction. To test this, a mathematical framework is introduced that calculates the (retained) SNR for in-plane and simultaneous multi-slice (SMS)-accelerated acquisitions. Moreover, this framework was used to optimize the sampling pattern by maximizing the local SNR within a region of interest (ROI) through non-linear, RF-induced CAIPIRINHA slice shifts. The framework was evaluated on 14 healthy subjects and the optimized sampling pattern was compared with in-plane acceleration and CAIPIRINHA acceleration with linear slice shifts, which are primarily used in brain imaging. We demonstrate that the field of view (FOV) in the superior-inferior direction, the coil positioning and the individual anatomy have a large impact on the image SNR (changes up to 50% for varying coil positions and 40% differences between subjects) and image artifacts for simultaneous multi-slice acceleration. Consequently, sampling patterns have to be optimized for acquisitions employing different FOVs and ideally on an individual basis. Optimization of the sampling pattern, which exploits non-linear shifts between slices, showed a considerable SNR increase (10-30%) for higher acceleration factors. The framework outlined in this article can be used to optimize sampling patterns for a broad range of accelerated body acquisitions on an individual basis. Copyright © 2015 John Wiley & Sons, Ltd.