Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning.

Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 weighted, T1 post-contrast, T2 weighted, T2-weighted FLAIR, proton-density weighted. Prior studies have investigated using imaging-based methods and DICOM metadata-based methods with success on cohorts of patients acquired as part of a clinical trial. This study compares the performance of these methods on heterogeneous clinical datasets acquired with many different scanners from many institutions. RF and CNN models were trained on metadata and pixel data, respectively. A combined RF model incorporated CNN logits from the pixel-based model together with metadata. Four cohorts were used for model development and evaluation: MS research (n = 11,106 series), MS clinical (n = 3244 series), glioma research (n = 612 series, test/validation only), and ADNI PTSD (n = 477 series, training only). Together, these cohorts represent a broad range of acquisition contexts (scanners, sequences, institutions) and subject pathologies. Pixel-based CNN and combined models achieved accuracies between 97 and 98% on the clinical MS cohort. Validation/test accuracies with the glioma cohort were 99.7% (metadata only) and 98.4 (CNN). Accurate and generalizable classification of MRI acquisition contrast types was demonstrated. Such methods are important for enabling automated data selection in high-throughput and big-data image analysis applications.

Hyperpolarized 13 C MRI data acquisition and analysis in prostate and brain at University of California, San Francisco.

Based on the expanding set of applications for hyperpolarized carbon-13 (HP-13 C) MRI, this work aims to communicate standardized methodology implemented at the University of California, San Francisco, as a primer for conducting reproducible metabolic imaging studies of the prostate and brain. Current state-of-the-art HP-13 C acquisition, data processing/reconstruction and kinetic modeling approaches utilized in patient studies are presented together with the rationale underpinning their usage. Organized around spectroscopic and imaging-based methods, this guide provides an extensible framework for handling a variety of HP-13 C applications, which derives from two examples with dynamic acquisitions: 3D echo-planar spectroscopic imaging of the human prostate and frequency-specific 2D multislice echo-planar imaging of the human brain. Details of sequence-specific parameters and processing techniques contained in these examples should enable investigators to effectively tailor studies around individual-use cases. Given the importance of clinical integration in improving the utility of HP exams, practical aspects of standardizing data formats for reconstruction, analysis and visualization are also addressed alongside open-source software packages that enhance institutional interoperability and validation of methodology. To facilitate the adoption and further development of this methodology, example datasets and analysis pipelines have been made available in the supporting information.

Fully automated atlas-based method for prescribing 3D PRESS MR spectroscopic imaging: Toward robust and reproducible metabolite measurements in human brain.

PURPOSE: To implement a fully automated atlas-based method for prescribing 3D PRESS MR spectroscopic imaging (MRSI). METHODS: The PRESS selected volume and outer-volume suppression bands were predefined on the MNI152 standard template image. The template image was aligned to the subject T1 -weighted image during a scan, and the resulting transformation was then applied to the predefined prescription. To evaluate the method, H-1 MRSI data were obtained in repeat scan sessions from 20 healthy volunteers. In each session, datasets were acquired twice without repositioning. The overlap ratio of the prescribed volume in the two sessions was calculated and the reproducibility of inter- and intrasession metabolite peak height and area ratios was measured by the coefficient of variation (CoV). The CoVs from intra- and intersession were compared by a paired t-test. RESULTS: The average overlap ratio of the automatically prescribed selection volumes between two sessions was 97.8%. The average voxel-based intersession CoVs were less than 0.124 and 0.163 for peak height and area ratios, respectively. Paired t-test showed no significant difference between the intra- and intersession CoVs. CONCLUSION: The proposed method provides a time efficient method to prescribe 3D PRESS MRSI with reproducible imaging positioning and metabolite measurements. Magn Reson Med 79:636-642, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Detection of localized changes in the metabolism of hyperpolarized gluconeogenic precursors 13 C-lactate and 13 C-pyruvate in kidney and liver.

PURPOSE: The purpose of this study was to characterize tissue-specific alterations in metabolism of hyperpolarized (HP) gluconeogenic precursors 13 C-lactate and 13 C-pyruvate by rat liver and kidneys under conditions of fasting or insulin-deprived diabetes. METHODS: Seven normal rats were studied by MR spectroscopic imaging of both HP 13 C-lactate and 13 C-pyruvate in both normal fed and 24 h fasting states, and seven additional rats were scanned after induction of diabetes by streptozotocin (STZ) with insulin withdrawal. Phosphoenolpyruvate carboxykinase (PEPCK) expression levels were also measured in liver and kidney tissues of the STZ-treated rats. RESULTS: Multiple sets of significant signal modulations were detected, with graded intensity in general between fasting and diabetic states. An approximate two-fold reduction in the ratio of 13 C-bicarbonate to total 13 C signal was observed in both organs in fasting. The ratio of HP lactate-to-alanine was markedly altered, ranging from a liver-specific 54% increase in fasting, to increases of 69% and 92% in liver and kidney, respectively, in diabetes. Diabetes resulted in a 40% increase in renal lactate signal. STZ resulted in 5.86-fold and 2.73-fold increases in PEPCK expression in liver and kidney, respectively. CONCLUSION: MRI of HP 13 C gluconeogenic precursors may advance diabetes research by clarifying organ-specific roles in abnormal diabetic metabolism. Magn Reson Med 77:1429-1437, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Power estimation for non-standardized multisite studies.

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

Serial analysis of 3D H-1 MRSI for patients with newly diagnosed GBM treated with combination therapy that includes bevacizumab.

Interpretation of changes in the T1- and T2-weighted MR images from patients with newly diagnosed glioblastoma (GBM) treated with standard of care in conjunction with anti-angiogenic agents is complicated by pseudoprogression and pseudoresponse. The hypothesis being tested in this study was that 3D H-1 magnetic resonance spectroscopic imaging (MRSI) provides estimates of levels of choline, creatine, N-acetylaspartate (NAA), lactate and lipid that change in response to treatment and that metrics describing these characteristics are associated with survival. Thirty-one patients with newly diagnosed GBM and being treated with radiation therapy (RT), temozolomide, erlotinib and bevacizumab were recruited to receive serial MR scans that included 3-D lactate edited MRSI at baseline, mid-RT, post-RT and at specific follow-up time points. The data were processed to provide estimates of metrics representing changes in metabolite levels relative to normal appearing brain. Cox proportional hazards analysis was applied to examine the relationship of these parameters with progression free survival (PFS) and overall survival (OS). There were significant reductions in parameters that describe relative levels of choline to NAA and creatine, indicating that the treatment caused a decrease in tumor cellularity. Changes in the levels of lactate and lipid relative to the NAA from contralateral brain were consistent with vascular normalization. Metabolic parameters from the first serial follow-up scan were associated with PFS and OS, when accounting for age and extent of resection. Integrating metabolic parameters into the assessment of patients with newly diagnosed GBM receiving therapies that include anti-angiogenic agents may be helpful for tracking changes in tumor burden, resolving ambiguities in anatomic images caused by non-specific treatment effects and for predicting outcome.

Precision medicine in chronic disease management: The multiple sclerosis BioScreen.

We present a precision medicine application developed for multiple sclerosis (MS): the MS BioScreen. This new tool addresses the challenges of dynamic management of a complex chronic disease; the interaction of clinicians and patients with such a tool illustrates the extent to which translational digital medicine-that is, the application of information technology to medicine-has the potential to radically transform medical practice. We introduce 3 key evolutionary phases in displaying data to health care providers, patients, and researchers: visualization (accessing data), contextualization (understanding the data), and actionable interpretation (real-time use of the data to assist decision making). Together, these form the stepping stones that are expected to accelerate standardization of data across platforms, promote evidence-based medicine, support shared decision making, and ultimately lead to improved outcomes.

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI.

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth, it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper, we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies, and technical developments of this imaging technique.

Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology.

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF. Engineering and usability challenges and results from 3 use cases are presented. A generalized validation system based on free, open-source software (OSS) was implemented, connecting clinical imaging modalities, the Picture Archiving and Communication System (PACS), and an ML inference server. ML pipelines were implemented in NVIDIA's Clara Deploy framework with results and clinician feedback stored in a customized XNAT instance, separate from the clinical record but linked from within PACS. Prospective clinical validation studies of 3 ML models were conducted, with data routed from multiple clinical imaging modalities and PACS. Completed validation studies provided expert clinical feedback on model performance and usability, plus system reliability and performance metrics. Clinical validation of ML models entails assessing model performance, impact on clinical infrastructure, robustness, and usability. Study results must be easily accessible to participating clinicians but remain outside the clinical record. Building a system that generalizes and scales across multiple ML models takes the concerted effort of software engineers, clinicians, data scientists, and system administrators, and benefits from the use of modular OSS. The present work provides a template for institutions looking to translate and clinically validate ML models in the clinic, together with required resources and expected challenges.

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Reliable and Reproducible GABA Measurements Using Automated Spectral Prescription at Ultra-High Field.

Purpose: To evaluate spectral acquisition processes important for obtaining reliable and reproducible γ-aminobutyric acid (GABA) signals from volunteers in brain regions that are frequently used for neuroimaging studies [anterior cingulate cortex (ACC), superior temporal gyrus, and caudate] at ultra-high field. Methods: Ten healthy volunteers were studied using a single-voxel Point-RESolved Spectrosocpy (PRESS) sequence with band selective inversion with gradient dephasing pulses (BASING). The editing pulse was designed to be symmetrically placed at 2.0 and 1.4 ppm in the two cycles to reduce the co-editing of macro-molecules (MM). Spectral data were obtained with phase encoding matrix 8 × 8 × 1 and two editing cycles or 1 × 1 × 1 and 64 editing/64 non-editing. The total acquisition time was approximately 4.5 min for each acquisition. An automated MRS prescription method was utilized for the placement of the GABA scan location in 5/10 subjects. Three regions of interest were predefined in the MNI152 space and then registered and transformed to subject space. These volunteers also had repeat scans to examine between-session reproducibility. Results: The placement of editing pulses symmetrically at 1.7 ppm reduced the effect of MM contributions and provided more accurate GABA estimation. Chemical shift misregistration errors caused by classic PRESS localization sequence are more significant at ultra-high field strength. Therefore, a large over-excitation factor was needed to reduce this error. Furthermore, the inefficiency of saturation bands and unspoiled coherence could also interfere with the quality of the data. Reliable recovery of metabolite signals resulted from the implementation of 8 × 8 × 1 phase encoding that successfully removed artifacts and errors, without compromising the total acquisition time. Between successive scans on the same subject, dice overlap ratios of the excited spectral volume between the two scans were in the range of 92-95%. Within subject variability of metabolites between two repeat scans was smaller in the ACC and left superior temporal gyrus when compared to that in the right caudate, with averaged coefficients of variation being 3.6, 6.0, and 16.9%, respectively. Conclusion: This study demonstrated the feasibility of obtaining reliable and reproducible GABA measurements at ultra-high field.

Metabolic Profiling of IDH Mutation and Malignant Progression in Infiltrating Glioma.

Infiltrating low grade gliomas (LGGs) are heterogeneous in their behavior and the strategies used for clinical management are highly variable. A key factor in clinical decision-making is that patients with mutations in the isocitrate dehydrogenase 1 and 2 (IDH1/2) oncogenes are more likely to have a favorable outcome and be sensitive to treatment. Because of their relatively long overall median survival, more aggressive treatments are typically reserved for patients that have undergone malignant progression (MP) to an anaplastic glioma or secondary glioblastoma (GBM). In the current study, ex vivo metabolic profiles of image-guided tissue samples obtained from patients with newly diagnosed and recurrent LGG were investigated using proton high-resolution magic angle spinning spectroscopy (1H HR-MAS). Distinct spectral profiles were observed for lesions with IDH-mutated genotypes, between astrocytoma and oligodendroglioma histologies, as well as for tumors that had undergone MP. Levels of 2-hydroxyglutarate (2HG) were correlated with increased mitotic activity, axonal disruption, vascular neoplasia, and with several brain metabolites including the choline species, glutamate, glutathione, and GABA. The information obtained in this study may be used to develop strategies for in vivo characterization of infiltrative glioma, in order to improve disease stratification and to assist in monitoring response to therapy.

A feasibility study of in vivo T1rho imaging of the intervertebral disc.

PURPOSE: Recent studies have proposed that magnetic resonance (MR) T1rho relaxation time is associated with loss of macromolecules. The depletion of macromolecules in the matrix of the intervertebral disc may be an initiating factor in degenerative disc disease. The purpose of this study was to test the feasibility of quantifying T1rho relaxation time in phantoms and intervertebral discs of healthy volunteers using in vivo MR imaging at 3 T. MATERIALS AND METHODS: A multislice T1rho spiral sequence was used to quantify T1rho relaxation time in phantoms with different agarose concentrations and in the intervertebral discs of 11 healthy volunteers (mean age=31.3 years; age range=23-60 years; gender: 5 females, 6 males). RESULTS: The phantom studies demonstrated the feasibility of using spiral imaging at 3 T. The in vivo results indicate that the median T1rho value of the nucleus (116.6+/-21.4 ms) is significantly greater (P<0.05) than that of the annulus (84.1+/-11.7 ms). The correlations between the age of the volunteers and T1rho relaxation time in the nucleus (r2=-0.82; P=0.0001) and the annulus (r2=-0.37; P=0.04) were significant. A trend of decreasing T1rho values from L3-4 to L4-5 to L5-S1 was evident. CONCLUSION: The results of this study suggest that in vivo T1rho quantification is feasible and may potentially be a clinical tool in identifying early degenerative changes in the intervertebral disc.

Considerations in applying 3D PRESS H-1 brain MRSI with an eight-channel phased-array coil at 3 T.

The purpose of this study was to assess the benefits of a 3 T scanner and an eight-channel phased-array head coil for acquiring three-dimensional PRESS (Point REsolved Spectral Selection) proton (H-1) magnetic resonance spectroscopic imaging (MRSI) data from the brains of volunteers and patients with brain tumors relative to previous studies that used a 1.5 T scanner and a quadrature head coil. Issues that were of concern included differences in chemical shift artifacts, line broadening due to increased susceptibility at higher field strengths, changes in relaxation times and the increased complexity of the postprocessing software due to the need for combining signals from the multichannel data. Simulated and phantom spectra showed that very selective suppression pulses with a thickness of 40 mm and an overpress factor of at least 1.2 are needed to reduce chemical shift artifact and lipid contamination at higher field strengths. Spectral data from a phantom and those from six volunteers demonstrated that the signal-to-noise ratio (SNR) in the eight-channel coil was more than 50% higher than that in the quadrature head coil. For healthy volunteers and eight patients with brain tumors, the SNR at 3 T with the eight-channel coil was on average 1.5 times higher relative to the eight-channel coil at 1.5 T in voxels from normal-appearing brains. In combination with the effect of a higher field strength, the use of the eight-channel coil was able to provide an increase in the SNR of more than 2.33 times the corresponding acquisition at 1.5 T with a quadrature head coil. This is expected to be critical for clinical applications of MRSI in patients with brain tumors because it can be used to either decrease acquisition time or improve spatial resolution.

SIVIC: Open-Source, Standards-Based Software for DICOM MR Spectroscopy Workflows.

Quantitative analysis of magnetic resonance spectroscopic imaging (MRSI) data provides maps of metabolic parameters that show promise for improving medical diagnosis and therapeutic monitoring. While anatomical images are routinely reconstructed on the scanner, formatted using the DICOM standard, and interpreted using PACS workstations, this is not the case for MRSI data. The evaluation of MRSI data is made more complex because files are typically encoded with vendor-specific file formats and there is a lack of standardized tools for reconstruction, processing, and visualization. SIVIC is a flexible open-source software framework and application suite that enables a complete scanner-to-PACS workflow for evaluation and interpretation of MRSI data. It supports conversion of vendor-specific formats into the DICOM MR spectroscopy (MRS) standard, provides modular and extensible reconstruction and analysis pipelines, and provides tools to support the unique visualization requirements associated with such data. Workflows are presented which demonstrate the routine use of SIVIC to support the acquisition, analysis, and delivery to PACS of clinical (1)H MRSI datasets at UCSF.

Prospective image registration for automated scan prescription of follow-up knee images in quantitative studies.

Consistent scan prescription for MRI of the knee is very important for accurate comparison of images in a longitudinal study. However, consistent scan region selection is difficult due to the complexity of the knee joint. We propose a novel method for registering knee images using a mutual information registration algorithm to align images in a baseline and follow-up exam. The output of the registration algorithm, three translations and three Euler angles, is then used to redefine the region to be imaged and acquire an identical oblique imaging volume in the follow-up exam as in the baseline. This algorithm is robust to articulation of the knee and anatomical abnormalities due to disease (e.g., osteophytes). The registration method is performed only on the distal femur and is not affected by the proximal tibia or soft tissues. We have incorporated this approach in a clinical MR system and have demonstrated its utility in automatically obtaining consistent scan regions between baseline and follow-up examinations, thus improving the precision of quantitative evaluation of cartilage. Results show an improvement with prospective registration in the coefficient of variation for cartilage thickness, cartilage volume and T2 relaxation measurements.

Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme.

The objective of this study was to test the predictive value of serial MRI data in relation to clinical outcome for patients with glioblastoma multiforme (GBM). Sixty-four patients with newly diagnosed GBM underwent conventional MRI and diffusion-weighted and perfusion-weighted imaging postsurgery and prior to radiation/chemotherapy (pre-RT), immediately after RT (post-RT), and every 1-2 months thereafter until tumor progression, up to a maximum of 1 year. Tumor volumes and perfusion and diffusion parameters were calculated and subject to time-independent and time-dependent Cox proportional hazards models that were adjusted for age and MR scanner field strength. Larger volumes of the T2 hyperintensity lesion (T2ALL) and nonenhancing lesion (NEL) at pre-RT, as well as increased anatomic volumes at post-RT, were associated with worse overall survival (OS). Higher normalized cerebral blood volumes (nCBVs), normalized peak height (nPH) and normalized recirculation factors (nRF) at pre-RT, and nCBV at post-RT, in the T2ALL and NEL, were associated with shorter progression-free survival (PFS). From pre- to post-RT, there was a reduction in nCBV and nPH and an increase in apparent diffusion coefficient (ADC). Patients with lower nRF values at pre-RT, or a larger increase in nRF from pre-RT to post-RT, had significantly longer PFS. Time-dependent analysis showed that patterns of changes in ADC and anatomic volumes were associated with OS, while changes in nCBV, nPH, and the contrast-enhancing volume were associated with PFS. Our studies suggest that quantitative MRI variables derived from anatomic and physiological MRI provide useful information for predicting outcome in patients with GBM.

3D sensitivity encoded ellipsoidal MR spectroscopic imaging of gliomas at 3T.

PURPOSE: The goal of this study was to implement time efficient data acquisition and reconstruction methods for 3D magnetic resonance spectroscopic imaging (MRSI) of gliomas at a field strength of 3T using parallel imaging techniques. METHODS: The point spread functions, signal to noise ratio (SNR), spatial resolution, metabolite intensity distributions and Cho:NAA ratio of 3D ellipsoidal, 3D sensitivity encoding (SENSE) and 3D combined ellipsoidal and SENSE (e-SENSE) k-space sampling schemes were compared with conventional k-space data acquisition methods. RESULTS: The 3D SENSE and e-SENSE methods resulted in similar spectral patterns as the conventional MRSI methods. The Cho:NAA ratios were highly correlated (P<.05 for SENSE and P<.001 for e-SENSE) with the ellipsoidal method and all methods exhibited significantly different spectral patterns in tumor regions compared to normal appearing white matter. The geometry factors ranged between 1.2 and 1.3 for both the SENSE and e-SENSE spectra. When corrected for these factors and for differences in data acquisition times, the empirical SNRs were similar to values expected based upon theoretical grounds. The effective spatial resolution of the SENSE spectra was estimated to be same as the corresponding fully sampled k-space data, while the spectra acquired with ellipsoidal and e-SENSE k-space samplings were estimated to have a 2.36-2.47-fold loss in spatial resolution due to the differences in their point spread functions. CONCLUSION: The 3D SENSE method retained the same spatial resolution as full k-space sampling but with a 4-fold reduction in scan time and an acquisition time of 9.28 min. The 3D e-SENSE method had a similar spatial resolution as the corresponding ellipsoidal sampling with a scan time of 4:36 min. Both parallel imaging methods provided clinically interpretable spectra with volumetric coverage and adequate SNR for evaluating Cho, Cr and NAA.

A clinical comparison of rigid and inflatable endorectal-coil probes for MRI and 3D MR spectroscopic imaging (MRSI) of the prostate.

PURPOSE: To compare the data quality and ease of use of four endorectal-coil probe setups for prostate MRI. MATERIALS AND METHODS: Four endorectal-coil probe setups were compared: 1) air-inflated probe; 2) perfluorocarbon (PFC)-inflated probe; 3) rigid, smaller prototype coil; and 4) rigid, smaller coil designed for biopsying the prostate. Signal-to-noise ratio (SNR), positioning, shimming, MRI motion artifact, and MR spectroscopic imaging (MRSI) spectral quality were assessed. RESULTS: Rigid coils provided approximately 2.5-fold higher SNR than inflatable coils near the peripheral zone midline. The biopsy probe sensitivity decreased dramatically by the apex. The rigid probes, as compared to the inflatable probes, took longer to place (10 +/- 2 vs. 7 +/- 2 minutes, P < 0.0002), tended to be placed too superiorly, required repositioning more often (73% vs. 20%, P < 0.003), and had higher motion artifacts (P < 0.001). Shimming time was least for the PFC-inflated probe (2 +/- 0.5 minutes, P < 0.05). The air-inflated probe produced larger linewidths (P < 0.01) and tended to have longer shim times (7 +/- 4 minutes) and poorer spectral quality. CONCLUSION: The inflatable coil is a good clinical choice due to ease of use, good coverage, and low motion artifacts. PFC-inflation is recommended as it provided higher quality data than air-inflation. The rigid, smaller probes have higher SNR and produce less tissue distortion and may be preferred for certain applications.

Three-dimensional J-resolved H-1 magnetic resonance spectroscopic imaging of volunteers and patients with brain tumors at 3T.

A method that combines two-dimensional (2D) J-resolved spectroscopy with three spatial dimension magnetic resonance spectroscopic imaging (MRSI) is introduced to measure J-coupled metabolites of glutamate (Glu), glutamine (Gln), myo-Inositol (mI), and lactate (Lac) in the brain and to simultaneously obtain T(2) values of choline (Cho), creatine (Cr), and N-acetyl aspartate (NAA). Relatively few points in the t(1) dimension (six echo times) and a flyback echo-planar trajectory were incorporated in the acquisition to speed up the total acquisition time so that it was within a clinically feasible range (23 min). Data obtained using GAMMA software simulations and from phantoms have shown that the (4)CH(2) resonances of Glu can be separated from Gln at 2.35 ppm in TE-averaged spectra. Results from phantoms, six normal volunteers, and four patients demonstrated good signal-to-noise ratio (SNR). The J cross-peaks from the methyl group of Lac were visualized in the 2D spectra from the phantom and the glioma patient, and could be quantified from the spectra at J = +/-4.17 Hz. This technique also enables the evaluation of the changes in metabolite T(2). Compared with the values in normal white matter, the T(2) values of Cho and Cr were statistically significantly increased in regions of glioma.