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Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.
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Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time-consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three-dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water-only, fat-only, in-phase, and out-of-phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross-validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis.
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Purpose: To develop multichannel transmit and receive arrays towards capturing the ultimate-intrinsic-SNR (uiSNR) at 10.5 Tesla (T) and to demonstrate the feasibility and potential of whole-brain, high-resolution human brain imaging at this high field strength. Methods: A dual row 16-channel self-decoupled transmit (Tx) array was converted to a 16Tx/Rx transceiver using custom transmit/receive switches. A 64-channel receive-only (64Rx) array was built to fit into the 16Tx/Rx array. Electromagnetic modeling and experiments were employed to define safe operation limits of the resulting 16Tx/80Rx array and obtain FDA approval for human use. Results: The 64Rx array alone captured approximately 50% of the central uiSNR at 10.5T while the identical 7T 64Rx array captured â¼76% of uiSNR at this lower field strength. The 16Tx/80Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the performance of the 64Rx array at 7T. SNR data obtained at the two field strengths with these arrays displayed dependent increases over a large central region. Whole-brain high resolution T 2 * and T 1 weighted anatomical and gradient-recalled echo EPI BOLD fMRI images were obtained at 10.5T for the first time with such an advanced array, illustrating the promise of >10T fields in studying the human brain. Conclusion: We demonstrated the ability to approach the uiSNR at 10.5T over the human brain with a novel, high channel count array, achieving large SNR gains over 7T, currently the most commonly employed ultrahigh field platform, and demonstrate high resolution and high contrast anatomical and functional imaging at 10.5T.
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PURPOSE: To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities. METHODS: Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water. This information is then utilized to generate water-only composite images. The principle is demonstrated in free-breathing abdominal and neck examinations using stack-of-stars 3D balanced SSFP (bSSFP) and gradient-recalled echo (GRE) sequences at 0.55 and 3T. Moreover, a potential extension toward quantitative fat/water separation is described. RESULTS: Experiments with a proton density fat fraction (PDFF) phantom validated the reliability of fat/water separation using signal-response curves. As demonstrated for abdominal imaging at 0.55T, the approach resulted in more uniform fat suppression without loss of water signal and in improved CSF-to-fat signal ratio. Moreover, the approach provided consistent fat suppression in 3T neck exams where conventional spectrally-selective fat saturation failed due to strong local B0 inhomogeneities. The feasibility of simultaneous fat/water quantification has been demonstrated in a PDFF phantom. CONCLUSION: The proposed principle achieves reliable fat suppression in low-field applications and adapts to high-field applications with strong B0 inhomogeneity. Moreover, the principle potentially provides a basis for developing an alternative approach for PDFF quantification.
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Tecido Adiposo , Algoritmos , Imageamento por Ressonância Magnética , Imagens de Fantasmas , Humanos , Tecido Adiposo/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Reprodutibilidade dos Testes , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/métodos , Ondas de Rádio , Sensibilidade e Especificidade , Abdome/diagnóstico por imagem , Imageamento Tridimensional/métodosRESUMO
We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions. We first implemented the Feature VarNet, which propagates information throughout the cascades of the network in an N-channel feature-space instead of a 2-channel feature-space. Then, we add an attention layer that utilizes the spatial locations of Cartesian undersampling artifacts to further improve performance. Lastly, we combined the Feature and E2E VarNets into the Feature-Image (FI) VarNet, to facilitate cross-domain learning and boost accuracy. Reconstructions were evaluated on the fastMRI dataset using standard metrics and clinical scoring by three neuroradiologists. Feature and FI VarNets outperformed the E2E VarNet for 4 × , 5 × and 8 × Cartesian undersampling in all studied metrics. FI VarNet secured second place in the public fastMRI leaderboard for 4 × Cartesian undersampling, outperforming all open-source models in the leaderboard. Radiologists rated FI VarNet brain reconstructions with higher quality and sharpness than the E2E VarNet reconstructions. FI VarNet excelled in preserving anatomical details, including blood vessels, whereas E2E VarNet discarded or blurred them in some cases. The proposed FI VarNet enhances the reconstruction quality of undersampled MRI and could enable clinically acceptable reconstructions at higher acceleration factors than currently possible.
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Encéfalo , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Imageamento por Ressonância Magnética/métodos , Humanos , Processamento de Imagem Assistida por Computador/métodos , Encéfalo/diagnóstico por imagem , Redes Neurais de Computação , AlgoritmosRESUMO
PURPOSE: We examined magnetic field dependent SNR gains and ability to capture them with multichannel receive arrays for human head imaging in going from 7 T, the most commonly used ultrahigh magnetic field (UHF) platform at the present, to 10.5 T, which represents the emerging new frontier of >10 T in UHFs. METHODS: Electromagnetic (EM) models of 31-channel and 63-channel multichannel arrays built for 10.5 T were developed for 10.5 T and 7 T simulations. A 7 T version of the 63-channel array with an identical coil layout was also built. Array performance was evaluated in the EM model using a phantom mimicking the size and electrical properties of the human head and a digital human head model. Experimental data was obtained at 7 T and 10.5 T with the 63-channel array. Ultimate intrinsic SNR (uiSNR) was calculated for the two field strengths using a voxelized cloud of dipoles enclosing the phantom or the digital human head model as a reference to assess the performance of the two arrays and field depended SNR gains. RESULTS: uiSNR calculations in both the phantom and the digital human head model demonstrated SNR gains at 10.5 T relative to 7 T of 2.6 centrally, Ë2 at the location corresponding to the edge of the brain, Ë1.4 at the periphery. The EM models demonstrated that, centrally, both arrays captured Ë90% of the uiSNR at 7 T, but only Ë65% at 10.5 T, leading only to Ë2-fold gain in array SNR in going from 7 to 10.5 T. This trend was also observed experimentally with the 63-channel array capturing a larger fraction of the uiSNR at 7 T compared to 10.5 T, although the percentage of uiSNR captured were slightly lower at both field strengths compared to EM simulation results. CONCLUSIONS: Major uiSNR gains are predicted for human head imaging in going from 7 T to 10.5 T, ranging from Ë2-fold at locations corresponding to the edge of the brain to 2.6-fold at the center, corresponding to approximately quadratic increase with the magnetic field. Realistic 31- and 63-channel receive arrays, however, approach the central uiSNR at 7 T, but fail to do so at 10.5 T, suggesting that more coils and/or different type of coils will be needed at 10.5 T and higher magnetic fields.
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Cabeça , Imageamento por Ressonância Magnética , Imagens de Fantasmas , Razão Sinal-Ruído , Humanos , Cabeça/diagnóstico por imagem , Imageamento por Ressonância Magnética/instrumentação , Encéfalo/diagnóstico por imagem , Desenho de Equipamento , Simulação por Computador , Processamento de Imagem Assistida por Computador/métodosRESUMO
Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.
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Imageamento por Ressonância Magnética , Próstata , Neoplasias da Próstata , Humanos , Masculino , Inteligência Artificial , Aprendizado de Máquina , Imageamento por Ressonância Magnética/métodos , Próstata/diagnóstico por imagem , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/patologiaRESUMO
PURPOSE: Recent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy-mimicking cylindrical sample. METHODS: The study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples. The total field in each medium is decomposed in terms of inward and outward electromagnetic waves, and the fields are expressed as series of cylindrical harmonics, whose coefficients can be interpreted as classical reflection and transmission coefficients. RESULTS: Our new formulation allows a quantitative evaluation of the effect of the HPM layer for varying permittivity and thickness, and it provides an intuitive understanding of such effect in terms of propagation and scattering of the RF field. CONCLUSION: We show how HPM can filter out the modes that only contribute to the noise or RF power deposition, resulting in higher SNR or lower SAR, respectively. Our proposed framework provides physical insight on how to properly design HPM for MRI applications.
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Imageamento por Ressonância Magnética , Imagens de Fantasmas , Imageamento por Ressonância Magnética/métodos , Humanos , Simulação por Computador , Espalhamento de Radiação , Razão Sinal-Ruído , AlgoritmosRESUMO
Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation (AF), in partnership with the Hospital for Special Surgery (HSS), convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations gleaned from 5 presentations given in the "early hip osteoarthritis" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City. It also summarizes the workgroup recommendations from a small-group discussion on clinical research gaps.
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Introduction: Femoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images. Material and methods: 17 patients diagnosed with monolateral FAI underwent pre-surgical MR imaging, including a 3D Dixon sequence of the pelvis. An expert radiologist drew regions of interest on the water-only Dixon images outlining femur and acetabulum in both impingement (IJ) and healthy joints (HJ). 182 radiomic features were extracted for each hip. The dataset numerosity was increased by 60 times with an ad-hoc data augmentation tool. Features were subdivided by type and region in 24 subsets. For each, a univariate ANOVA F-value analysis was applied to find the 5 features most correlated with IJ based on p-value, for a total of 48 subsets. For each subset, a K-nearest neighbor model was trained to differentiate between IJ and HJ using the values of the radiomic features in the subset as input. The training was repeated 100 times, randomly subdividing the data with 75%/25% training/testing. Results: The texture-based gray level features yielded the highest prediction max accuracy (0.972) with the smallest subset of features. This suggests that the gray image values are more homogeneously distributed in the HJ in comparison to IJ, which could be due to stress-related inflammation resulting from impingement. Conclusions: We showed that radiomics can automatically distinguish IJ from HJ using water-only Dixon MRI. To our knowledge, this is the first application of radiomics for FAI diagnosis. We reported an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Our proposed radiomic analysis could be combined with methods for automated joint segmentation to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology.
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OBJECTIVE: We developed a hybrid volume surface integral equation (VSIE) method based on domain decomposition to perform fast and accurate magnetic resonance imaging (MRI) simulations that include both remote and local conductive elements. METHODS: We separated the conductive surfaces present in MRI setups into two domains and optimized electromagnetic (EM) modeling for each case. Specifically, interactions between the body and EM waves originating from local radiofrequency (RF) coils were modeled with the precorrected fast Fourier transform, whereas the interactions with remote conductive surfaces (RF shield, scanner bore) were modeled with a novel cross tensor train-based algorithm. We compared the hybrid-VSIE with other VSIE methods for realistic MRI simulation setups. RESULTS: The hybrid-VSIE was the only practical method for simulation using 1 mm voxel isotropic resolution (VIR). For 2 mm VIR, our method could be solved at least 23 times faster and required 760 times lower memory than traditional VSIE methods. CONCLUSION: The hybrid-VSIE demonstrated a marked improvement in terms of convergence times of the numerical EM simulation compared to traditional approaches in multiple realistic MRI scenarios. SIGNIFICANCE: The efficiency of the novel hybrid-VSIE method could enable rapid simulations of complex and comprehensive MRI setups.
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Radiação Eletromagnética , Ondas de Rádio , Simulação por Computador , Algoritmos , Imageamento por Ressonância Magnética/métodos , Imagens de Fantasmas , Campos EletromagnéticosRESUMO
BACKGROUND: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. PURPOSE: To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations. STUDY TYPE: Retrospective. POPULATION: Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available). FIELD STRENGTH AND SEQUENCE: A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS). ASSESSMENT: Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. STATISTICAL TESTS: Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. RESULTS: The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss. DATA CONCLUSION: Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.
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Aprendizado Profundo , Humanos , Estudos Retrospectivos , Imageamento Tridimensional/métodos , Articulação do Joelho/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética , Processamento de Imagem Assistida por Computador/métodosRESUMO
High static field MR scanners can produce human tissue images of astounding clarity, but rely on high frequency electromagnetic radiation that generates complicated in-tissue field patterns that are patient-specific and potentially harmful. Many such scanners use parallel transmitters to better control field patterns, but then adjust the transmitters based on general guidelines rather than optimizing for the specific patient, mostly because computing patient-specific fields was presumed far too slow. It was recently demonstrated that the combination of fast low-resolution tissue mapping and fast voxel-based field simulation can be used to perform a patient-specific MR safety check in minutes. However, the field simulation required several of those minutes, making it too slow to perform the dozens of simulations that would be needed for patient-specific optimization. In this paper we describe a compressed-perturbation-matrix technique that nearly eliminates the computational cost of including complex coils (or coils and shields) in voxel-based field simulation of tissue, thereby reducing simulation time from minutes to seconds. The approach is demonstrated on a wide variety of head+coil and head+coil+shield configurations, using the implementation in MARIE 2.0, the latest version of the open-source MR field simulator MARIE.
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Imageamento por Ressonância Magnética , Humanos , Imageamento por Ressonância Magnética/métodos , Simulação por Computador , Imagens de FantasmasRESUMO
OBJECTIVE: We propose a framework to interpret the effects of High Permittivity Materials (HPM) on the performance of radiofrequency coils in Magnetic Resonance Imaging (MRI). METHODS: Based on a recent formulation of the scattering and propagation properties of spheres, we expanded the field in a layered sphere as a superposition of ingoing and outgoing travelling waves, which allowed us to study the field components with a non-homogeneous transmission line model. We investigated the effects of a layer of HPM surrounding a head-mimicking uniform sphere at 7 tesla. RESULTS: Through the analysis of impedance and reflection coefficients, we show that by adjusting the properties of the HPM, it is possible to selectively amplify individual modes, or combination of them, modifying the overall field distribution in the sample and increasing signal-to-noise ratio at specific locations. Our results demonstrate that the observed enhanced MRI performance is not merely due to secondary fields generated by displacement currents in the HPM. CONCLUSIONS: Our formulation explains the effect of HPM in terms of matching, enabling the optimization of the electrical properties of the HPM with a simple mode-matching formula. SIGNIFICANCE: The proposed method could guide the design of novel radiofrequency coils with integrated HPM.
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Imageamento por Ressonância Magnética , Ondas de Rádio , Imagens de Fantasmas , Desenho de Equipamento , Imageamento por Ressonância Magnética/métodos , Razão Sinal-RuídoRESUMO
In this work, we propose a method for the compression of the coupling matrix in volume-surface integral equation (VSIE) formulations. VSIE methods are used for electromagnetic analysis in magnetic resonance imaging (MRI) applications, for which the coupling matrix models the interactions between the coil and the body. We showed that these effects can be represented as independent interactions between remote elements in 3D tensor formats, and subsequently decomposed with the Tucker model. Our method can work in tandem with the adaptive cross approximation technique to provide fast solutions of VSIE problems. We demonstrated that our compression approaches can enable the use of VSIE matrices of prohibitive memory requirements, by allowing the effective use of modern graphical processing units (GPUs) to accelerate the arising matrix-vector products. This is critical to enable numerical MRI simulations at clinical voxel resolutions in a feasible computation time. In this paper, we demonstrate that the VSIE matrix-vector products needed to calculate the electromagnetic field produced by an MRI coil inside a numerical body model with 1 mm3 voxel resolution, could be performed in ~ 33 seconds in a GPU, after compressing the associated coupling matrix from ~ 80 TB to ~ 43 MB.
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PURPOSE: To present a novel 3T 24-channel glove array that enables hand and wrist imaging in varying postures. METHODS: The glove array consists of an inner glove holding the electronics and an outer glove protecting the components. The inner glove consists of four main structures: palm, fingers, wrist, and a flap that rolls over on top. Each structure was constructed out of three layers: a layer of electrostatic discharge flame-resistant fabric, a layer of scuba neoprene, and a layer of mesh fabric. Lightweight and flexible high impedance coil (HIC) elements were inserted into dedicated tubes sewn into the fabric. Coil elements were deliberately shortened to minimize the matching interface. Siemens Tim 4G technology was used to connect all 24 HIC elements to the scanner with only one plug. RESULTS: The 24-channel glove array allows large motion of both wrist and hand while maintaining the SNR needed for high-resolution imaging. CONCLUSION: In this work, a purpose-built 3T glove array that embeds 24 HIC elements is demonstrated for both hand and wrist imaging. The 24-channel glove array allows a great range of motion of both the wrist and hand while maintaining a high SNR and providing good theoretical acceleration performance, thus enabling hand and wrist imaging at different postures to extract kinematic information.
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Imageamento por Ressonância Magnética , Punho , Impedância Elétrica , Desenho de Equipamento , Mãos/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Imagens de Fantasmas , Punho/diagnóstico por imagemRESUMO
We investigated how to construct low-order subspace basis sets to accurately represent electromagnetic fields generated within inhomogeneous arbitrary objects by radio-frequency sources external to a Huygen's surface. The basis generation relies on the singular value decomposition of Green's functions integro-differential operators which makes it feasible to derive a reduced-order yet stable model. We present a detailed study of the theoretical and numerical requisites for generating such basis, and show how it can be used to calculate performance limits in magnetic resonance imaging applications. Finally, we propose a novel numerical framework for the computation of characteristic modes of arbitrary inhomogeneous objects. We validated accuracy and convergence properties of the numerical basis against a complete analytical basis in the case of a uniform spherical object. We showed that the discretization of the Huygens's surface has a minimal effect on the accuracy of the calculations, which mainly depended on the electromagnetic solver resolution and order of approximation.
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BACKGROUND: Signal-to-noise ratio (SNR) is used to evaluate the performance of magnetic resonance (MR) imaging systems. Accurate and consistent estimations are needed to routinely use SNR to assess coils and image reconstruction techniques. PURPOSE: To identify a reliable and practical method for SNR estimation in multiple-coil reconstructions. STUDY TYPE: Technical evaluation and comparison. SUBJECTS/PHANTOM: A uniform phantom and four healthy volunteers: 35, 38, 39 y/o males, 25 y/o female. FIELD STRENGTH/SEQUENCE: Two-dimensional multislice gradient-echo pulse sequence at 3 T and 7 T. ASSESSMENT: Reference-standard SNR was calculated from 100 multiple replicas. Six SNR methods were compared against it: difference image (DI), analytic array combination (AC), pseudo-multiple-replica (PMR), generalized pseudo-replica (GPR), smoothed image subtraction (SIS), and DI with temporal instability correction (TIC). The assessment was repeated for different multiple-coil reconstructions. STATISTICAL TESTS: SNR methods were evaluated in terms of relative deviation (RD) and normalized mutual information (NMI) with respect to the reference-standard, using a linear regression (0.05 significance level) to assess how different factors affect accuracy. RESULTS: Average RD (phantom) for DI, AC, PMR, GPR, SIS, and TIC was 7.9%, 6%, 6.7%, 10.1%, 40%, and 14.6%, respectively. RD increased with acceleration. SNR maps with AC were the most similar to the reference standard (NMI = 0.358). Considering all brain regions of interest, average RD for all SNR methods varied 96% among volunteers but remained approximately 10% for AC, PMR, and GPR, whereas it was more than 30% for DI, SIS, and TIC. RD was mainly affected by image reconstruction (beta = 12) for AC and SNR entropy for SIS (beta = 19). DATA CONCLUSION: AC provided accurate and robust SNR estimation. PMR and GPR are more generally applicable than AC. DI and TIC should be used only at low acceleration factors, when an additional noise-only scan cannot be acquired. SIS is a single-acquisition alternative to DI for generalized autocalibrating partial parallel acquisition (GRAPPA) reconstructions. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.
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Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Encéfalo/diagnóstico por imagem , Feminino , Humanos , Masculino , Imagens de Fantasmas , Razão Sinal-RuídoRESUMO
PURPOSE: Investigating the designs and effects of high dielectric constant (HDC) materials in the shape of a conformal helmet on the enhancement of RF field and reduction of specific absorption rate at 10.5 T for human brain studies. METHODS: A continuous and a segmented four-piece HDC helmet fit to a human head inside an eight-channel fractionated-dipole array were constructed and studied with a phantom and a human head model using computer electromagnetic simulations. The simulated transmit efficiency and receive sensitivity were experimentally validated using a phantom with identical electric properties and helmet-coil configurations of the computer model. The temporal and spatial distributions of displacement currents on the HDC helmets were analyzed. RESULTS: Using the continuous HDC helmet, simulation results in the human head model demonstrated an average transmit efficiency enhancement of 66%. A propagating displacement current was induced on the continuous helmet, leading to an inhomogeneous RF field enhancement in the brain. Using the segmented four-piece helmet design to reduce this effect, an average 55% and 57% enhancement in the transmit efficiency and SNR was achieved in human head, respectively, along with 8% and 28% reductions in average and maximum local specific absorption rate. CONCLUSION: The HDC helmets enhanced the transmit efficiency and SNR of the dipole array coil in the human head at 10.5 T. The segmentation of the helmet to disrupt the continuity of circumscribing displacement currents in the helmet produced a more uniform distribution of the transmit field and lower specific absorption rate in the human head compared with the continuous helmet design.