A General Bayesian Functional Spatial Partitioning Method for Multiple Region Discovery Applied to Prostate Cancer MRI.

Current protocols to estimate the number, size, and location of cancerous lesions in the prostate using multiparametric magnetic resonance imaging (mpMRI) are highly dependent on reader experience and expertise. Automatic voxel-wise cancer classifiers do not directly provide estimates of number, location, and size of cancerous lesions that are clinically important. Existing spatial partitioning methods estimate linear or piecewise-linear boundaries separating regions of local stationarity in spatially registered data and are inadequate for the application of lesion detection. Frequentist segmentation and clustering methods often require pre-specification of the number of clusters and do not quantify uncertainty. Previously, we developed a novel Bayesian functional spatial partitioning method to estimate the boundary surrounding a single cancerous lesion using data derived from mpMRI. We propose a Bayesian functional spatial partitioning method for multiple lesion detection with an unknown number of lesions. Our method utilizes functional estimation to model the smooth boundary curves surrounding each cancerous lesion. In a Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) framework, we develop novel jump steps to jointly estimate and quantify uncertainty in the number of lesions, their boundaries, and the spatial parameters in each lesion. Through simulation we show that our method is robust to the shape of the lesions, number of lesions, and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using MRI.

Improved quantitative parameter estimation for prostate T2 relaxometry using convolutional neural networks.

OBJECTIVE: Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate. MATERIALS AND METHODS: Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness. RESULTS: In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels. DISCUSSION: This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.

Use of a Commercial 7-T MRI Scanner for Clinical Brain Imaging: Indications, Protocols, Challenges, and Solutions-A Single-Center Experience.

The first commercially available 7-T MRI scanner (Magnetom Terra) was approved by the FDA in 2017 for clinical imaging of the brain and knee. After initial protocol development and sequence optimization efforts in volunteers, the 7-T system, in combination with an FDA-approved 1-channel transmit/32-channel receive array head coil, can now be routinely used for clinical brain MRI examinations. The ultrahigh field strength of 7-T MRI has the advantages of improved spatial resolution, increased SNR, and increased CNR but also introduces an array of new technical challenges. The purpose of this article is to describe an institutional experience with the use of the commercially available 7-T MRI scanner for routine clinical brain imaging. Specific clinical indications for which 7-T MRI may be useful for brain imaging include brain tumor evaluation with possible perfusion imaging and/or spectroscopy, radiotherapy planning; evaluation of multiple sclerosis and other demyelinating diseases, evaluation of Parkinson disease and guidance of deep brain stimulator placement, high-detail intracranial MRA and vessel wall imaging, evaluation of pituitary pathology, and evaluation of epilepsy. Detailed protocols, including sequence parameters, for these various indications are presented, and implementation challenges (including artifacts, safety, and side effects) and potential solutions are explored.

Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI.

Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.

A novel Bayesian functional spatial partitioning method with application to prostate cancer lesion detection using MRI.

Spatial partitioning methods correct for nonstationarity in spatially related data by partitioning the space into regions of local stationarity. Existing spatial partitioning methods can only estimate linear partitioning boundaries. This is inadequate for detecting an arbitrarily shaped anomalous spatial region within a larger area. We propose a novel Bayesian functional spatial partitioning (BFSP) algorithm, which estimates closed curves that act as partitioning boundaries around anomalous regions of data with a distinct distribution or spatial process. Our method utilizes transitions between a fixed Cartesian and moving polar coordinate system to model the smooth boundary curves using functional estimation tools. Using adaptive Metropolis-Hastings, the BFSP algorithm simultaneously estimates the partitioning boundary and the parameters of the spatial distributions within each region. Through simulation we show that our method is robust to shape of the target zone and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using magnetic resonance imaging.

Tier-based formalism for safety assessment of custom-built radio-frequency transmit coils.

The purpose of this work is to propose a tier-based formalism for safety assessment of custom-built radio-frequency (RF) coils that balances validation effort with the effort put in determinating the safety factor. The formalism has three tier levels. Higher tiers require increased effort when validating electromagnetic simulation results but allow for less conservative safety factors. In addition, we propose a new method to calculate modeling uncertainty between simulations and measurements and a new method to propagate uncertainties in the simulation into a safety factor that minimizes the risk of underestimating the peak specific absorption rate (SAR). The new safety assessment procedure was completed for all tier levels for an eight-channel dipole array for prostate imaging at 7 T and an eight-channel dipole array for head imaging at 10.5 T, using data from two different research sites. For the 7 T body array, the validation procedure resulted in a modeling uncertainty of 77% between measured and simulated local SAR distributions. For a situation where RF shimming is performed on the prostate, average power limits of 2.4 and 4.5 W/channel were found for tiers 2 and 3, respectively. When the worst-case peak SAR among all phase settings was calculated, power limits of 1.4 and 2.7 W/channel were found for tiers 2 and 3, respectively. For the 10.5 T head array, a modeling uncertainty of 21% was found based on B1 + mapping. For the tier 2 validation, a power limit of 2.6 W/channel was calculated. The demonstrated tier system provides a strategy for evaluating modeling inaccuracy, allowing for the rapid translation of novel coil designs with conservative safety factors and the implementation of less conservative safety factors for frequently used coil arrays at the expense of increased validation effort.

Evaluation of lesion and overlying articular cartilage in patients with juvenile osteochondritis dissecans of the knee using quantitative diffusion MRI.

Current clinical MRI of patients with juvenile osteochondritis dissecans (JOCD) is limited by the low reproducibility of lesion instability evaluation and inability to predict which lesions will heal after nonoperative treatment and which will later require surgery. The aim of this study is to verify the ability of apparent diffusion coefficient (ADC) to detect differences in lesion microstructure between different JOCD stages, treatment groups, and healthy, unaffected contralateral knees. Pediatric patients with JOCD received quantitative diffusion MRI between January 2016 and September 2020 in this prospective research study. A disease stage (I-IV) and stability of each JOCD lesion was evaluated. ADCs were calculated in progeny lesion, interface, parent bone, cartilage overlying lesion, control bone, and control cartilage regions. ADC differences were evaluated using linear mixed models with Bonferroni correction. Evaluated were 30 patients (mean age, 13 years; 21 males), with 40 JOCD-affected and 12 healthy knees. Nine patients received surgical treatment after MRI. Negative Spearman rank correlations were found between ADCs and JOCD stage in the progeny lesion (ρ = -0.572; p < 0.001), interface (ρ = -0.324; p = 0.041), and parent bone (ρ = -0.610; p < 0.001), demonstrating the sensitivity of ADC to microstructural differences in lesions at different JOCD stages. We observed a significant increase in the interface ADCs (p = 0.007) between operative (mean [95% CI] = 1.79 [1.56-2.01] × 10-3 mm2 /s) and nonoperative group (1.27 [0.98-1.57] × 10-3 mm2 /s). Quantitative diffusion MRI detects microstructural differences in lesions at different stages of JOCD progression towards healing and reveals differences between patients assigned for operative versus nonoperative treatment.

The impact of respiratory motion on electromagnetic fields and specific absorption rate in cardiac imaging at 7T.

PURPOSE: To present electromagnetic simulation setups for detailed analyses of respiration's impact on B 1 + $$ {B}_1^{+} $$ and E-fields, local specific absorption rate (SAR) and associated safety-limits for 7T cardiac imaging. METHODS: Finite-difference time-domain electromagnetic field simulations were performed at five respiratory states using a breathing body model and a 16-element 7T body transceiver RF-coil array. B 1 + $$ {B}_1^{+} $$ and SAR are analyzed for fixed and moving coil configurations. SAR variations are investigated using phase/amplitude shimming considering (i) a local SAR-controlled mode (here SAR calculations consider RF amplitudes and phases) and (ii) a channel-wise power-controlled mode (SAR boundary calculation is independent of the channels' phases, only dependent on the channels' maximum amplitude). RESULTS: Respiration-induced variations of both B 1 + $$ {B}_1^{+} $$ amplitude and phase are observed. The flip angle homogeneity depends on the respiratory state used for B 1 + $$ {B}_1^{+} $$ shimming; best results were achieved for shimming on inhale and exhale simultaneously ( | Δ C V | < 35 % $$ \mid \Delta CV\mid <35\% $$ ). The results reflect that respiration impacts position and amplitude of the local SAR maximum. With the local-SAR-control mode, a safety factor of up to 1.4 is needed to accommodate for respiratory variations while the power control mode appears respiration-robust when the coil moves with respiration (SAR peak decrease: 9% exhale→inhale). Instead, a spatially fixed coil setup yields higher SAR variations with respiration. CONCLUSION: Respiratory motion does not only affect the B 1 + $$ {B}_1^{+} $$ distribution and hence the image contrast, but also location and magnitude of the peak spatial SAR. Therefore, respiration effects may need to be included in safety analyses of RF coils applied to the human thorax.

Ultra-high-field MR in Prostate cancer: Feasibility and Potential.

Multiparametric MRI of the prostate at clinical magnetic field strengths (1.5/3 Tesla) has emerged as a reliable noninvasive imaging modality for identifying clinically significant cancer, enabling selective sampling of high-risk regions with MRI-targeted biopsies, and enabling minimally invasive focal treatment options. With increased sensitivity and spectral resolution, ultra-high-field (UHF) MRI (≥ 7 Tesla) holds the promise of imaging and spectroscopy of the prostate with unprecedented detail. However, exploiting the advantages of ultra-high magnetic field is challenging due to inhomogeneity of the radiofrequency field and high local specific absorption rates, raising local heating in the body as a safety concern. In this work, we review various coil designs and acquisition strategies to overcome these challenges and demonstrate the potential of UHF MRI in anatomical, functional and metabolic imaging of the prostate and pelvic lymph nodes. When difficulties with power deposition of many refocusing pulses are overcome and the full potential of metabolic spectroscopic imaging is used, UHF MR(S)I may aid in a better understanding of the development and progression of local prostate cancer. Together with large field-of-view and low-flip-angle anatomical 3D imaging, 7 T MRI can be used in its full strength to characterize different tumor stages and help explain the onset and spatial distribution of metastatic spread.

Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data.

Multi-parametric magnetic resonance imaging (mpMRI) has been playing an increasingly important role in the detection of prostate cancer (PCa). Various computer-aided detection algorithms were proposed for automated PCa detection by combining information in multiple mpMRI parameters. However, there are specific features of mpMRI, including between-voxel correlation within each prostate and heterogeneity across patients, that have not been fully explored but could potentially improve PCa detection if leveraged appropriately. This article proposes novel Bayesian approaches for voxel-wise PCa classification that accounts for spatial correlation and between-patient heterogeneity in the mpMRI data. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we propose three scalable approaches based on Nearest Neighbor Gaussian Process (NNGP), reduced-rank approximation, and a conditional autoregressive (CAR) model that approximates a Gaussian Process with the Matérn covariance, respectively. Our simulation study shows that properly modeling the spatial correlation and between-patient heterogeneity can substantially improve PCa classification. Application to in vivo data illustrates that classification is improved by all three spatial modeling approaches considered, while modeling the between-patient heterogeneity does not further improve our classifiers. Among the proposed models, the NNGP-based model is recommended given its high classification accuracy and computational efficiency.

Training Convolutional Networks for Prostate Segmentation With Limited Data.

Multi-zonal segmentation is a critical component of computer-aided diagnostic systems for detecting and staging prostate cancer. Previously, convolutional neural networks such as the U-Net have been used to produce fully automatic multi-zonal prostate segmentation on magnetic resonance images (MRIs) with performance comparable to human experts, but these often require large amounts of manually segmented training data to produce acceptable results. For institutions that have limited amounts of labeled MRI exams, it is not clear how much data is needed to train a segmentation model, and which training strategy should be used to maximize the value of the available data. This work compares how the strategies of transfer learning and aggregated training using publicly available external data can improve segmentation performance on internal, site-specific prostate MR images, and evaluates how the performance varies with the amount of internal data used for training. Cross training experiments were performed to show that differences between internal and external data were impactful. Using a standard U-Net architecture, optimizations were performed to select between 2D and 3D variants, and to determine the depth of fine-tuning required for optimal transfer learning. With the optimized architecture, the performance of transfer learning and aggregated training were compared for a range of 5-40 internal datasets. The results show that both strategies consistently improve performance and produced segmentation results that are comparable to that of human experts with approximately 20 site-specific MRI datasets. These findings can help guide the development of site-specific prostate segmentation models for both clinical and research applications.

Impact of 18-French Rectal Tube Placement on Image Quality of Multiparametric Prostate MRI.

This study compared prostate multiparametric MRI (mpMRI) performed using an 18-French rectal tube in place throughout the examination after initial placement by a technologist (n = 97) with mpMRI performed without rectal tube placement (n = 99). Acquisition parameters were otherwise identical. Two radiologists scored subjective image quality and measured rectal diameter. For both readers, rectal tube placement was associated (p < .001) with improved ADC map quality, decreased DWI distortion, decreased rectal gas, and decreased rectal diameter. Findings support routine rectal tube placement for prostate mpMRI.

Signature maps for automatic identification of prostate cancer from colorimetric analysis of H&E- and IHC-stained histopathological specimens.

Prostate cancer (PCa) is a major cause of cancer death among men. The histopathological examination of post-surgical prostate specimens and manual annotation of PCa not only allow for detailed assessment of disease characteristics and extent, but also supply the ground truth for developing of computer-aided diagnosis (CAD) systems for PCa detection before definitive treatment. As manual cancer annotation is tedious and subjective, there have been a number of publications describing methods for automating the procedure via the analysis of digitized whole-slide images (WSIs). However, these studies have focused only on the analysis of WSIs stained with hematoxylin and eosin (H&E), even though there is additional information that could be obtained from immunohistochemical (IHC) staining. In this work, we propose a framework for automating the annotation of PCa that is based on automated colorimetric analysis of both H&E and IHC WSIs stained with a triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and α-methylacyl CoA racemase (AMACR). The analysis outputs were then used to train a regression model to estimate the distribution of cancerous epithelium within slides. The approach yielded an AUC of 0.951, sensitivity of 87.1%, and specificity of 90.7% as compared to slide-level annotations, and generalized well to cancers of all grades.

Difference in MRI-guided biopsy cancer detection rates between individual clinicians.

OBJECTIVE: A number of studies have described the overall institutional learning curve for magnetic resonance imaging-guided biopsy but none have evaluated differences and interactions between clinicians. Therefore, we aim to measure and compare the cancer detection rates between individual radiologists and urologists at a single academic institution. METHODS: A consecutive sample of patients undergoing magnetic resonance imaging-guided biopsy at a single institution were included for analysis. The detection of any and clinically significant (Gleason score ≥3+4) prostate cancer was compared between radiologists and urologists after adjusting for relevant demographic and clinical characteristics. Analysis was conducted on a perlesion basis and only the results of the targeted cores were considered in the primary analysis. RESULTS: Two hundred eighty-one patients with 418 lesions were included in the study. Prostate cancer of any grade was detected in 43.7% (183/418) of targeted lesions. There was no difference in the distribution of Prostate Imaging Reporting and Data System (PIRADS) scores attributed by each radiologist (p = 0.43). The individual radiologist cancer detection rate for both overall and clinically significant cancer was similar across each PIRADS score except for the detection of any cancer in PIRADS 3 lesions (p = 0.03). There was no difference in the detection rates of any grade or clinically significant cancer between urologists. CONCLUSION: This single institutional analysis found that the performance of radiologists and urologists was largely comparable. Theonly variation observed was among radiologists for PIRADS 3 lesions.

A clinical prediction tool to determine the need for concurrent systematic sampling at the time of magnetic resonance imaging-guided biopsy.

OBJECTIVE: To develop a clinical prediction tool that characterises the risk of missing significant prostate cancer by omitting systematic biopsy in men undergoing transrectal ultrasonography/magnetic resonance imaging (TRUS/MRI)-fusion-guided biopsy. PATIENTS AND METHODS: A consecutive sample of men undergoing TRUS/MRI-fusion-guided biopsy with the UroNav® system (Invivo International, Best, The Netherlands) who also underwent concurrent systematic biopsy was included. By comparing the grade of cancer diagnosed on targeted and systematic biopsy cores, we identified cases where clinically significant disease (Gleason score ≥3+4) was only found on systematic and not targeted cores. Multivariable logistic regression analyses were used to identify predictive factors for finding significant cancer on systematic cores only. We then used these data to develop a nomogram and evaluated its utility using decision curve analysis. RESULTS: Of the 398 men undergoing TRUS/MRI-fusion-guided biopsy in our study, there were 46 (11.6%) cases in which clinically significant cancer was missed on targeted biopsy and detected on systematic biopsy. The clinical setting, number of MRI lesions identified, and the highest Prostate Imaging-Reporting and Data System (PI-RADS) score of the lesions, were all found to be predictors of this. Our model had a good discriminative ability (concordance index = 0.70). The results from our decision curve analysis show that this model provides a higher net clinical benefit than either biopsying all men or omitting biopsy in all patients when the threshold probability is <30%. CONCLUSION: We found that omitting concurrent systematic biopsy in men undergoing TRUS/MRI-fusion-guided biopsy would miss significant disease in more than one in 10 patients. We propose a prediction model with good discriminative ability that can be used to improve patient selection for performing concurrent systematic biopsy in order to minimise the number of missed significant cancers. It is important that our model is validated in external cohorts before being employed in routine clinical practice.

Which scores need a core? An evaluation of MR-targeted biopsy yield by PIRADS score across different biopsy indications.

INTRODUCTION: Magnetic resonance imaging is being widely adopted in the clinical management of prostate cancer. The correlation of the Prostate Imaging Reporting and Data System (PIRADS) to the presence of cancer has been established but studies have primarily evaluated this in a single clinical setting. This study aims to characterize the correlation of PIRADS score to the diagnosis of cancer on fusion biopsy among men who are undergoing primary biopsy, those who have had a previous negative biopsy or men on active surveillance. MATERIALS & METHODS: A consecutive sample of men undergoing US-MR biopsy at a single academic institution from 2014 to 2017 were included in this retrospective study. Men were stratified into groups according to their clinical history: biopsy-naïve, previous negative transrectal ultrasound (TRUS) biopsy or on active surveillance. The correlation of PIRADS score to the diagnosis of any and clinically significant cancer (Gleason score ≥ 3 + 4) was determined. RESULTS: A total of 255 patients with 365 discrete lesions were analyzed. PIRADS score 1-2, 3, 4 and 5 yielded any prostate cancer in 7.7, 29.7, 42.3 and 82.4% of the cases, respectively, across all indications while clinically significant cancer was found in 0, 8.9, 21.4 and 62.7%, respectively. The area under the receiver operative curves for the diagnosis of any and significant cancer was 0.69 (95%CI: 0.64-0.74) and 0.74 (95%CI: 0.69-0.79) respectively. Men who have had a previous negative biopsy had lower detection rates for any prostate cancer for PIRADS 3 and 4 lesions compared to those that were biopsy-naïve or on active surveillance. CONCLUSION: Cancer detection rates are significantly associated with PIRADS score. Biopsy yields differ across biopsy indications which should be considered when selecting a PIRADS score threshold for biopsy. Biopsy of PIRADS 3 lesions could potentially be avoided in men who have previously undergone a negative TRUS biopsy.

Detection of prostate cancer with multiparametric MRI utilizing the anatomic structure of the prostate.

Multiparametric magnetic resonance imaging (mpMRI), which combines traditional anatomic and newer quantitative MRI methods, has been shown to result in improved voxel-wise classification of prostate cancer as compared with any single MRI parameter. While these results are promising, substantial heterogeneity in the mpMRI parameter values and voxel-wise prostate cancer risk has been observed both between and within regions of the prostate. This suggests that classification of prostate cancer can potentially be improved by incorporating structural information into the classifier. In this paper, we propose a novel voxel-wise classifier of prostate cancer that accounts for the anatomic structure of the prostate by Bayesian hierarchical modeling, which can be combined with post hoc spatial Gaussian kernel smoothing to account for residual spatial correlation. Our proposed classifier results in significantly improved area under the ROC curve (0.822 vs 0.729, P < .001) and sensitivity corresponding to 90% specificity (0.599 vs 0.429, P < .001), compared with a baseline model that does not account for the anatomic structure of the prostate. Furthermore, the classifier can also be applied on voxels with missing mpMRI parameters, resulting in similar performance, which is an important practical consideration that cannot be easily accommodated using regression-based classifiers. In addition, our classifier achieved high computational efficiency with a closed-form solution for the posterior predictive cancer probability.

Development of a measure for evaluating lesion-wise performance of CAD algorithms in the context of mpMRI detection of prostate cancer.

PURPOSE: Computer-aided detection/diagnosis (CAD) of prostate cancer (PCa) on multiparametric MRI (mpMRI) is an active area of research. In the literature, the performance of predictive models trained to detect PCa on mpMRI has typically been reported in terms of voxel-wise measures such as sensitivity and specificity and/or area under the receiver operating curve (AUC). However, it is unclear whether models that score higher by these measures are actually superior. Here, we propose a novel method for lesion identification as well as novel measures that assess the quality of the detected lesions. METHODS: A total of 46 axial MRI slices of interest from 34 patients and the associated histopathologic ground truths were used to develop and to characterize the proposed measures. The proposed lesion-wise score sℓ is based on the Jaccard similarity index with modifications that emphasize the overlap and colocalization of predicted lesions with ground truth lesions. Thresholding of sℓ allowed for the sensitivity and specificity of lesion detection to be assessed, while the proposed lesion-summary score sσ is a weighted average of sℓ s that provides a single summary statistic of lesion detection performance. The proposed measures were used to compare the lesion detection performance of a predictive model vs that of a radiologist on the same data set. The measures were also used to evaluate the degree to which viewing the cancer prediction improved diagnostic accuracy. RESULTS: The lesion-wise score qualitatively reflected the goodness of predicted lesions over a wide range of values (sℓ = 0.1 to sℓ = 0.8) and was found to encompass a larger range of values than the Dice coefficient did over the same range of prediction qualities (0-0.9 vs 0-0.75). The lesion-summary score was shown to vary linearly with voxel-wise sensitivity and quadratically with voxel-wise specificity and correlated well with voxel-wise AUC (ρ = 0.68) and the Dice coefficient (ρ = 0.88). Radiologist performance was found to be significantly improved after viewing the model-generated cancer prediction maps as quantified by both sσ (P = 0.01) and DSC (P = 0.04), with improvements in both lesion detection sensitivity and specificity. CONCLUSION: The proposed measures allow for the assessment of lesion detection performance, which is most relevant in a clinical setting and would not be possible to do with voxel-wise measures alone.

Radiofrequency heating studies on anesthetized swine using fractionated dipole antennas at 10.5 T.

PURPOSE: To validate electromagnetic and thermal simulations with in vivo temperature measurements, and to demonstrate a framework that can be used to predict temperature increase caused by radiofrequency (RF) excitation with dipole transmitter arrays. METHODS: Dipole arrays were used to deliver RF energy in the back/neck region of the swine using different RF excitation patterns (n = 2-4 per swine) for heating. The temperature in anesthetized swine (n = 3) was measured using fluoroscopic probes (n = 12) and compared against thermal modeling from animal-specific electromagnetic simulations. RESULTS: Simulated temperature curves were in agreement with the measured data. The root mean square error between simulated and measured temperature rise at all locations (at the end of each RF excitation) is calculated as 0.37°C. The mean experimental temperature rise at the maximum temperature rise locations (averaged over all experiments) is calculated as 2.89°C. The root mean square error between simulated and measured temperature at the maximum temperature rise location is calculated as 0.57°C. (Error values are averaged over all experiments.) CONCLUSIONS: Electromagnetic and thermal simulations were validated with experiments. Thermal effects of RF excitation at 10.5 Tesla with dipoles were investigated. Magn Reson Med 79:479-488, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

MR spectroscopy of breast cancer for assessing early treatment response: Results from the ACRIN 6657 MRS trial.

PURPOSE: To estimate the accuracy of predicting response to neoadjuvant chemotherapy (NACT) in patients with locally advanced breast cancer using MR spectroscopy (MRS) measurements made very early in treatment. MATERIALS AND METHODS: This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant protocol was approved by the American College of Radiology and local-site institutional review boards. One hundred nineteen women with invasive breast cancer of ≥3 cm undergoing NACT were enrolled between September 2007 and April 2010. MRS measurements of the concentration of choline-containing compounds ([tCho]) were performed before the first chemotherapy regimen (time point 1, TP1) and 20-96 h after the first cycle of treatment (TP2). The change in [tCho] was assessed for its ability to predict pathologic complete response (pCR) and radiologic response using the area under the receiver operating characteristic curve (AUC) and logistic regression models. RESULTS: Of the 119 subjects enrolled, only 29 cases (24%) with eight pCRs provided usable data for the primary analysis. Technical challenges in acquiring quantitative MRS data in a multi-site trial setting limited the capture of usable data. In this limited data set, the decrease in tCho from TP1 to TP2 had poor ability to predict either pCR (AUC = 0.53, 95% confidence interval [CI]: 0.27-0.79) or radiologic response (AUC = 0.51, 95% CI: 0.27-0.75). CONCLUSION: The technical difficulty of acquiring quantitative MRS data in a multi-site clinical trial setting led to a low yield of analyzable data, which was insufficient to accurately measure the ability of early MRS measurements to predict response to NACT. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:290-302.