Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.
Radiology. 2018 Dec 11;:180940
Authors: Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M, Poston KL, Sha SJ, Greicius MD, Mormino E, Pauly JM, Srinivas S, Zaharchuk G
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [18F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition. Convolutional neural networks were implemented with low-dose PET and multiple MR images (PET-plus-MR model) or with low-dose PET alone (PET-only) as inputs to predict full-dose PET images. Quality of the synthesized images was evaluated while Bland-Altman plots assessed the agreement of regional standard uptake value ratios (SUVRs) between image types. Two readers scored image quality on a five-point scale (5 = excellent) and determined amyloid status (positive or negative). Statistical analyses were carried out to assess the difference of image quality metrics and reader agreement and to determine confidence intervals (CIs) for reading results. Results The synthesized images (especially from the PET-plus-MR model) showed marked improvement on all quality metrics compared with the low-dose image. All PET-plus-MR images scored 3 or higher, with proportions of images rated greater than 3 similar to those for the full-dose images (-10% difference [eight of 80 readings], 95% CI: -15%, -5%). Accuracy for amyloid status was high (71 of 80 readings [89%]) and similar to intrareader reproducibility of full-dose images (73 of 80 [91%]). The PET-plus-MR model also had the smallest mean and variance for SUVR difference to full-dose images. Conclusion Simultaneously acquired MRI and ultra-low-dose PET data can be used to synthesize full-dose-like amyloid PET images. © RSNA, 2018 Online supplemental material is available for this article.
PMID: 30526350 [PubMed - as supplied by publisher]
Hyperpolarized 13C MRI: Path to Clinical Translation in Oncology.
Neoplasia. 2018 Nov 22;21(1):1-16
Authors: Kurhanewicz J, Vigneron DB, Ardenkjaer-Larsen JH, Bankson JA, Brindle K, Cunningham CH, Gallagher FA, Keshari KR, Kjaer A, Laustsen C, Mankoff DA, Merritt ME, Nelson SJ, Pauly JM, Lee P, Ronen S, Tyler DJ, Rajan SS, Spielman DM, Wald L, Zhang X, Malloy CR, Rizi R
This white paper discusses prospects for advancing hyperpolarization technology to better understand cancer metabolism, identify current obstacles to HP (hyperpolarized) 13C magnetic resonance imaging's (MRI's) widespread clinical use, and provide recommendations for overcoming them. Since the publication of the first NIH white paper on hyperpolarized 13C MRI in 2011, preclinical studies involving [1-13C]pyruvate as well a number of other 13C labeled metabolic substrates have demonstrated this technology's capacity to provide unique metabolic information. A dose-ranging study of HP [1-13C]pyruvate in patients with prostate cancer established safety and feasibility of this technique. Additional studies are ongoing in prostate, brain, breast, liver, cervical, and ovarian cancer. Technology for generating and delivering hyperpolarized agents has evolved, and new MR data acquisition sequences and improved MRI hardware have been developed. It will be important to continue investigation and development of existing and new probes in animal models. Improved polarization technology, efficient radiofrequency coils, and reliable pulse sequences are all important objectives to enable exploration of the technology in healthy control subjects and patient populations. It will be critical to determine how HP 13C MRI might fill existing needs in current clinical research and practice, and complement existing metabolic imaging modalities. Financial sponsorship and integration of academia, industry, and government efforts will be important factors in translating the technology for clinical research in oncology. This white paper is intended to provide recommendations with this goal in mind.
PMID: 30472500 [PubMed - as supplied by publisher]
Whole-heart coronary MR angiography using a 3D cones phyllotaxis trajectory.
Magn Reson Med. 2018 Oct 29;:
Authors: Malavé MO, Baron CA, Addy NO, Cheng JY, Yang PC, Hu BS, Nishimura DG
PURPOSE: To develop a 3D cones steady-state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free-breathing whole-heart coronary magnetic resonance angiography.
METHOD: The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k-space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade-off of an increased number of acquisition heartbeats. For verification, phyllotaxis-cones is compared to sequential-cones through simulations, phantom studies, and in vivo coronary scans with 8 subjects using 2D image-based navigators for retrospective motion correction.
RESULTS: Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis-cones compared to sequential-cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis-cones.
CONCLUSION: Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free-breathing whole-heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady-state free precession sequence.
PMID: 30370941 [PubMed - as supplied by publisher]
Advantages of Short Repetition Time Resting-State Functional MRI Enabled by Simultaneous Multi-slice Imaging.
J Neurosci Methods. 2018 Oct 06;:
Authors: Jahanian H, Holdsworth S, Christen T, Wu H, Zhu K, Kerr AB, Middione MJ, Dougherty RF, Moseley M, Zaharchuk G
BACKGROUND: Recent advancements in simultaneous multi-slice (SMS) imaging techniques have enabled whole-brain resting-state fMRI (rs-fMRI) scanning at sub-second temporal resolution, providing spectral ranges much wider than the typically used range of 0.01-0.1 Hz. However, the advantages of this accelerated acquisition for rs-fMRI have not been evaluated.
NEW METHOD: In this study, we used SMS Echo Planar Imaging (EPI) to probe whole-brain functional connectivity with a short repetition time (TR = 350 ms) and compared it with standard EPI with a longer TR of 2000 ms. We determined the effect of scan length and investigated the temporal filtration strategies that optimize results based on metrics of signal-noise separation and test-retest reliability using both seed-based and independent component analysis (ICA).
RESULTS: We found that use of either the entire frequency range of 0.01-1.4 Hz or the entire frequency range with the exclusion of typical cardiac and respiratory frequency values tended to provide the best functional connectivity maps.
COMPARISON WITH EXISTING METHODS: We found that the SMS-acquired rs-fMRI scans had improved the signal-noise separation, while preserving the same level of test-retest reliability compared to conventional EPI, and enabled the detection of reliable functional connectivity networks with scan times as short as 3 minutes.
CONCLUSIONS: Our findings suggest that whole-brain rs-fMRI studies may benefit from the increased temporal resolution enabled by the SMS-EPI acquisition, leading to drastic scan time reductions, which in turn should enable the more widespread use of rs-fMRI in clinical research protocols.
PMID: 30300699 [PubMed - as supplied by publisher]
ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI.
Front Neurol. 2018;9:679
Authors: Winzeck S, Hakim A, McKinley R, Pinto JAADSR, Alves V, Silva C, Pisov M, Krivov E, Belyaev M, Monteiro M, Oliveira A, Choi Y, Paik MC, Kwon Y, Lee H, Kim BJ, Won JH, Islam M, Ren H, Robben D, Suetens P, Gong E, Niu Y, Xu J, Pauly JM, Lucas C, Heinrich MP, Rivera LC, Castillo LS, Daza LA, Beers AL, Arbelaezs P, Maier O, Chang K, Brown JM, Kalpathy-Cramer J, Zaharchuk G, Wiest R, Reyes M
Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).
PMID: 30271370 [PubMed]
Automatically Determining the Confocal Parameters from OCT B-Scans for Quantification of the Attenuation Coefficients.
IEEE Trans Med Imaging. 2018 Jul 31;:
Authors: Dwork N, Smith GT, Leng T, Pauly JM, Bowden AK
The attenuation coefficient is a relevant biomarker for many diagnostic medical applications. Recently, the Depth- Resolved Confocal (DRC) technique was developed to automatically estimate the attenuation coefficients from Optical Coherence Tomography (OCT) data with pixel-level resolution. However, DRC requires that the confocal function parameters (i.e., focal plane location and apparent Rayleigh range) be known a priori. In this paper we present the autoConfocal algorithm: a simple, automatic method for estimating those parameters directly from OCT imagery when the focal plane is within the sample. We present autoConfocal+DRC results on phantom data, ex-vivo biological tissue data, and in-vivo clinical data.
PMID: 30072317 [PubMed - as supplied by publisher]
Deep Generative Adversarial Neural Networks for Compressive Sensing (GANCS) MRI.
IEEE Trans Med Imaging. 2018 Jul 23;:
Authors: Mardani M, Gong E, Cheng JY, Vasanawala SS, Zaharchuk G, Xing L, Pauly JM
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require trade offs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ1/ℓ2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the ℓ1/ℓ2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning based CS schemes as well as with deeplearning based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which is two orders of magnitude faster than current state-of-the-art CS-MRI schemes.
PMID: 30040634 [PubMed - as supplied by publisher]
Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.
Radiology. 2018 Jul 24;:180445
Authors: Chen F, Taviani V, Malkiel I, Cheng JY, Tamir JI, Shaikh J, Chang ST, Hardy CJ, Pauly JM, Vasanawala SS
Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness.
PMID: 30040039 [PubMed - as supplied by publisher]
Quantitative susceptibility mapping using deep neural network: QSMnet.
Neuroimage. 2018 Jun 09;:
Authors: Yoon J, Gong E, Chatnuntawech I, Bilgic B, Lee J, Jung W, Ko J, Jung H, Setsompop K, Zaharchuk G, Kim EY, Pauly J, Lee J
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.
PMID: 29894829 [PubMed - as supplied by publisher]
Technique development of 3D dynamic CS-EPSI for hyperpolarized 13 C pyruvate MR molecular imaging of human prostate cancer.
Magn Reson Med. 2018 Mar 25;:
Authors: Chen HY, Larson PEZ, Gordon JW, Bok RA, Ferrone M, van Criekinge M, Carvajal L, Cao P, Pauly JM, Kerr AB, Park I, Slater JB, Nelson SJ, Munster PN, Aggarwal R, Kurhanewicz J, Vigneron DB
PURPOSE: The purpose of this study was to develop a new 3D dynamic carbon-13 compressed sensing echoplanar spectroscopic imaging (EPSI) MR sequence and test it in phantoms, animal models, and then in prostate cancer patients to image the metabolic conversion of hyperpolarized [1-13 C]pyruvate to [1-13 C]lactate with whole gland coverage at high spatial and temporal resolution.
METHODS: A 3D dynamic compressed sensing (CS)-EPSI sequence with spectral-spatial excitation was designed to meet the required spatial coverage, time and spatial resolution, and RF limitations of the 3T MR scanner for its clinical translation for prostate cancer patient imaging. After phantom testing, animal studies were performed in rats and transgenic mice with prostate cancers. For patient studies, a GE SPINlab polarizer (GE Healthcare, Waukesha, WI) was used to produce hyperpolarized sterile GMP [1-13 C]pyruvate. 3D dynamic 13 C CS-EPSI data were acquired starting 5 s after injection throughout the gland with a spatial resolution of 0.5 cm3 , 18 time frames, 2-s temporal resolution, and 36 s total acquisition time.
RESULTS: Through preclinical testing, the 3D CS-EPSI sequence developed in this project was shown to provide the desired spectral, temporal, and spatial 5D HP 13 C MR data. In human studies, the 3D dynamic HP CS-EPSI approach provided first-ever simultaneously volumetric and dynamic images of the LDH-catalyzed conversion of [1-13 C]pyruvate to [1-13 C]lactate in a biopsy-proven prostate cancer patient with full gland coverage.
CONCLUSION: The results demonstrate the feasibility to characterize prostate cancer metabolism in animals, and now patients using this new 3D dynamic HP MR technique to measure kPL , the kinetic rate constant of [1-13 C]pyruvate to [1-13 C]lactate conversion.
PMID: 29575178 [PubMed - as supplied by publisher]
Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.
J Magn Reson Imaging. 2018 Feb 13;:
Authors: Gong E, Pauly JM, Wintermark M, Zaharchuk G
BACKGROUND: There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration.
PURPOSE: To reduce gadolinium dose in contrast-enhanced brain MRI using a deep learning method.
STUDY TYPE: Retrospective, crossover.
POPULATION: Sixty patients receiving clinically indicated contrast-enhanced brain MRI.
SEQUENCE: 3D T1 -weighted inversion-recovery prepped fast-spoiled-gradient-echo (IR-FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR-FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low-dose (0.01mmol/kg) and 100% full-dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full-dose images from the precontrast and low-dose images. Synthesized full-dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma.
ASSESSMENT: For both test sets, low-dose, true full-dose, and the synthesized full-dose postcontrast image sets were compared quantitatively using peak-signal-to-noise-ratios (PSNR) and structural-similarity-index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion-artifact suppression, and contrast enhancement compared with precontrast images.
STATISTICAL ANALYSIS: Results were assessed using paired t-tests and noninferiority tests.
RESULTS: The proposed deep learning method yielded significant (n = 50, P < 0.001) improvements over the low-dose images (>5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full-dose images, the synthesized full-dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion-artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full-dose images for image quality (95% CI: -14-9%), artifacts suppression (95% CI: -5-20%), and contrast enhancement (95% CI: -13-6%).
DATA CONCLUSION: With the proposed deep learning method, gadolinium dose can be reduced 10-fold while preserving contrast information and avoiding significant image quality degradation.
LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.
PMID: 29437269 [PubMed - as supplied by publisher]
Frequency shifting reduces but does not eliminate acoustic interference between echolocating bats: A theoretical analysis.
J Acoust Soc Am. 2017 Oct;142(4):2133
Authors: Perkins ML, Frank HK, Pauly JM, Hadly EA
Bats have been observed to shift the frequency of their echolocation calls in the presence of other echolocating bats, ostensibly as a way to reduce acoustic interference. Few studies, however, have examined the theoretical efficacy of such jamming avoidance responses. The present study uses the wideband ambiguity function to analyze the effects of acoustic interference from conspecifics and congeneric heterospecifics on the target acquisition ability of Myotis californicus and Myotis yumanensis, specifically whether unilateral or bilateral frequency shifts reduce the effects of such interference. Model results suggest that in conspecific interactions, M. yumanensis recovers its target acquisition ability more completely and with less absolute frequency shift than does M. californicus, but that alternative methods of jamming avoidance may be easier to implement. The optimal strategy for reducing heterospecific interference is for M. californicus to downshift its call and M. yumanensis to upshift its call, which exaggerates a preexisting difference in mean frequency between the calls of the two species. Further empirical research would elucidate whether these species do in practice actively employ frequency shifting or other means for jamming avoidance, as well as illuminate the role of acoustic interference in niche partitioning.
PMID: 29092549 [PubMed - in process]
Thermo-Acoustic Ultrasound for Detection of RF-Induced Device Lead Heating in MRI.
IEEE Trans Med Imaging. 2017 Oct 17;:1
Authors: Dixit N, Stang PP, Pauly JM, Scott GC
Patients who have implanted medical devices with long conductive leads are often restricted from receiving MRI scans due to the danger of RF-induced heating near the lead tips. Phantom studies have shown that this heating varies significantly on a case-by-case basis, indicating that many patients with implanted devices can receive clinically useful MRI scans without harm. However, the difficulty of predicting RF-induced lead tip heating prior to scanning prevents numerous implant recipients from being scanned. Here, we demonstrate that thermo-acoustic ultrasound (TAUS) has the potential to be utilized for a prescan procedure assessing the risk of RF-induced lead tip heating in MRI. A system was developed to detect TAUS signals by four different TAUS acquisition methods. We then integrated this system with an MRI scanner and detected a peak in RF power absorption near the tip of a model lead when transmitting from the scanner's body coil. We also developed and experimentally validated simulations to characterize the thermo-acoustic signal generated near lead tips. These results indicate that TAUS is a promising method for assessing RF implant safety, and with further development, a TAUS pre-scan could allow many more patients to have access to MRI scans of significant clinical value.Patients who have implanted medical devices with long conductive leads are often restricted from receiving MRI scans due to the danger of RF-induced heating near the lead tips. Phantom studies have shown that this heating varies significantly on a case-by-case basis, indicating that many patients with implanted devices can receive clinically useful MRI scans without harm. However, the difficulty of predicting RF-induced lead tip heating prior to scanning prevents numerous implant recipients from being scanned. Here, we demonstrate that thermo-acoustic ultrasound (TAUS) has the potential to be utilized for a prescan procedure assessing the risk of RF-induced lead tip heating in MRI. A system was developed to detect TAUS signals by four different TAUS acquisition methods. We then integrated this system with an MRI scanner and detected a peak in RF power absorption near the tip of a model lead when transmitting from the scanner's body coil. We also developed and experimentally validated simulations to characterize the thermo-acoustic signal generated near lead tips. These results indicate that TAUS is a promising method for assessing RF implant safety, and with further development, a TAUS pre-scan could allow many more patients to have access to MRI scans of significant clinical value.
PMID: 29053449 [PubMed - as supplied by publisher]
Body diffusion-weighted imaging using magnetization prepared single-shot fast spin echo and extended parallel imaging signal averaging.
Magn Reson Med. 2017 Oct 17;:
Authors: Gibbons EK, Vasanawala SS, Pauly JM, Kerr AB
PURPOSE: This work demonstrates a magnetization prepared diffusion-weighted single-shot fast spin echo (SS-FSE) pulse sequence for the application of body imaging to improve robustness to geometric distortion. This work also proposes a scan averaging technique that is superior to magnitude averaging and is not subject to artifacts due to object phase.
THEORY AND METHODS: This single-shot sequence is robust against violation of the Carr-Purcell-Meiboom-Gill (CPMG) condition. This is achieved by dephasing the signal after diffusion weighting and tipping the MG component of the signal onto the longitudinal axis while the non-MG component is spoiled. The MG signal component is then excited and captured using a traditional SS-FSE sequence, although the echo needs to be recalled prior to each echo. Extended Parallel Imaging (ExtPI) averaging is used where coil sensitivities from the multiple acquisitions are concatenated into one large parallel imaging (PI) problem. The size of the PI problem is reduced by SVD-based coil compression which also provides background noise suppression. This sequence and reconstruction are evaluated in simulation, phantom scans, and in vivo abdominal clinical cases.
RESULTS: Simulations show that the sequence generates a stable signal throughout the echo train which leads to good image quality. This sequence is inherently low-SNR, but much of the SNR can be regained through scan averaging and the proposed ExtPI reconstruction. In vivo results show that the proposed method is able to provide diffusion encoded images while mitigating geometric distortion artifacts compared to EPI.
CONCLUSION: This work presents a diffusion-prepared SS-FSE sequence that is robust against the violation of the CPMG condition while providing diffusion contrast in clinical cases. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
PMID: 29044721 [PubMed - as supplied by publisher]
Mitigation of near-band balanced steady-state free precession through-plane flow artifacts using partial dephasing.
Magn Reson Med. 2017 Oct 10;:
Authors: Datta A, Cheng JY, Hargreaves BA, Baron CA, Nishimura DG
PURPOSE: To mitigate artifacts from through-plane flow at the locations of steady-state stopbands in balanced steady-state free precession (SSFP) using partial dephasing.
METHODS: A 60° range in the phase accrual during a TR was created over the voxel by slightly unbalancing the slice-select dephaser. The spectral profiles of SSFP with partial dephasing for various constant flow rates and during pulsatile flow were simulated to determine if partial dephasing decreases through-plane flow artifacts originating near SSFP dark bands while maintaining on-resonant signal. Simulations were then validated in a flow phantom. Lastly, phase-cycled SSFP cardiac cine images were acquired with and without partial dephasing in six subjects.
RESULTS: Partial dephasing decreased the strength and non-linearity of the dependence of the signal at the stopbands on the through-plane flow rate. It thus mitigated hyper-enhancement from out-of-slice signal contributions and transient-related artifacts caused by variable flow both in the phantom and in vivo. In six volunteers, partial dephasing noticeably decreased artifacts in all of the phase-cycled cardiac cine datasets.
CONCLUSION: Partial dephasing can mitigate the flow artifacts seen at the stopbands in balanced SSFP while maintaining the sequence's desired signal. By mitigating hyper-enhancement and transient-related artifacts originating from the stopbands, partial dephasing facilitates robust multiple-acquisition phase-cycled SSFP in the heart. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
PMID: 28994486 [PubMed - as supplied by publisher]
Rapid compressed sensing reconstruction of 3D non-Cartesian MRI.
Magn Reson Med. 2017 Sep 23;:
Authors: Baron CA, Dwork N, Pauly JM, Nishimura DG
PURPOSE: Conventional non-Cartesian compressed sensing requires multiple nonuniform Fourier transforms every iteration, which is computationally expensive. Accordingly, time-consuming reconstructions have slowed the adoption of undersampled 3D non-Cartesian acquisitions into clinical protocols. In this work we investigate several approaches to minimize reconstruction times without sacrificing accuracy.
METHODS: The reconstruction problem can be reformatted to exploit the Toeplitz structure of matrices that are evaluated every iteration, but it requires larger oversampling than what is strictly required by nonuniform Fourier transforms. Accordingly, we investigate relative speeds of the two approaches for various nonuniform Fourier transform kernel sizes and oversampling for both GPU and CPU implementations. Second, we introduce a method to minimize matrix sizes by estimating the image support. Finally, density compensation weights have been used as a preconditioning matrix to improve convergence, but this increases noise. We propose a more general approach to preconditioning that allows a trade-off between accuracy and convergence speed.
RESULTS: When using a GPU, the Toeplitz approach was faster for all practical parameters. Second, it was found that properly accounting for image support can prevent aliasing errors with minimal impact on reconstruction time. Third, the proposed preconditioning scheme improved convergence rates by an order of magnitude with negligible impact on noise.
CONCLUSION: With the proposed methods, 3D non-Cartesian compressed sensing with clinically relevant reconstruction times (<2 min) is feasible using practical computer resources. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
PMID: 28940748 [PubMed - as supplied by publisher]
Self-Calibrating Wave-Encoded Variable-Density Single-Shot Fast Spin Echo Imaging.
J Magn Reson Imaging. 2017 Sep 14;:
Authors: Chen F, Taviani V, Tamir JI, Cheng JY, Zhang T, Song Q, Hargreaves BA, Pauly JM, Vasanawala SS
BACKGROUND: It is highly desirable in clinical abdominal MR scans to accelerate single-shot fast spin echo (SSFSE) imaging and reduce blurring due to T2 decay and partial-Fourier acquisition.
PURPOSE: To develop and investigate the clinical feasibility of wave-encoded variable-density SSFSE imaging for improved image quality and scan time reduction.
STUDY TYPE: Prospective controlled clinical trial.
SUBJECTS: With Institutional Review Board approval and informed consent, the proposed method was assessed on 20 consecutive adult patients (10 male, 10 female, range, 24-84 years).
FIELD STRENGTH/SEQUENCE: A wave-encoded variable-density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable high acceleration (3.5×) with full-Fourier acquisitions by: 1) introducing wave encoding with self-refocusing gradient waveforms to improve acquisition efficiency; 2) developing self-calibrated estimation of wave-encoding point-spread function and coil sensitivity to improve motion robustness; and 3) incorporating a parallel imaging and compressed sensing reconstruction to reconstruct highly accelerated datasets.
ASSESSMENT: Image quality was compared pairwise with standard Cartesian acquisition independently and blindly by two radiologists on a scale from -2 to 2 for noise, contrast, confidence, sharpness, and artifacts. The average ratio of scan time between these two approaches was also compared.
STATISTICAL TESTS: A Wilcoxon signed-rank tests with a P value under 0.05 considered statistically significant.
RESULTS: Wave-encoded variable-density SSFSE significantly reduced the perceived noise level and improved the sharpness of the abdominal wall and the kidneys compared with standard acquisition (mean scores 0.8, 1.2, and 0.8, respectively, P < 0.003). No significant difference was observed in relation to other features (P = 0.11). An average of 21% decrease in scan time was achieved using the proposed method.
DATA CONCLUSION: Wave-encoded variable-density sampling SSFSE achieves improved image quality with clinically relevant echo time and reduced scan time, thus providing a fast and robust approach for clinical SSFSE imaging.
LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 6 J. Magn. Reson. Imaging 2017.
PMID: 28906567 [PubMed - as supplied by publisher]
Robust Self-Calibrating nCPMG Acquisition: Application to Body Diffusion-Weighted Imaging.
IEEE Trans Med Imaging. 2017 Aug 17;:
Authors: Gibbons EK, Roux PL, Vasanawala SS, Pauly JM, Kerr AB
This work demonstrates a robust diffusion-weighted single-shot FSE sequence in the presence of significant offresonance, which includes a variable-density acquisition and a self-calibrated reconstruction as improvements. An nCPMG SSFSE acquisition stabilizes both the main and parasitic echo families for each echo. This preserves both the in-phase and quadrature component of the magnetization throughout the echo train. However, nCPMG SS-FSE also promotes aliasing of the quadrature component, which complicates reconstruction. A new acquisition and reconstruction approach is presented here where the FOV is effectively doubled, but a partial k-space and variable density sampling is used to improve scan efficiency. The technique is presented in phantom scans to validate SNR and robustness against rapidly varying object phase. In vivo healthy volunteer examples and clinical cases are demonstrated in abdominal imaging. This new approach provides comparable SNR to previous nCPMG acquisition techniques as well as providing more uniform ADC maps in phantom scans. In vivo scans suggest that this method is more robust against motion than previous approaches. The proposed reconstruction is an improvement to the nCPMG sequence as it is auto-calibrating and is justified to accurately treat the signal model for the nCPMG SS-FSE sequence.
PMID: 28829307 [PubMed - as supplied by publisher]
Comprehensive Multi-Dimensional MRI for the Simultaneous Assessment of Cardiopulmonary Anatomy and Physiology.
Sci Rep. 2017 Jul 13;7(1):5330
Authors: Cheng JY, Zhang T, Alley MT, Uecker M, Lustig M, Pauly JM, Vasanawala SS
Diagnostic testing often assesses the cardiovascular or respiratory systems in isolation, ignoring the major pathophysiologic interactions between the systems in many diseases. When both systems are assessed currently, multiple modalities are utilized in costly fashion with burdensome logistics and decreased accessibility. Thus, we have developed a new acquisition and reconstruction paradigm using the flexibility of MRI to enable a comprehensive exam from a single 5-15 min scan. We constructed a compressive-sensing approach to pseudo-randomly acquire highly subsampled, multi-dimensionally-encoded and time-stamped data from which we reconstruct volumetric cardiac and respiratory motion phases, contrast-agent dynamics, and blood flow velocity fields. The proposed method, named XD flow, is demonstrated for (a) evaluating congenital heart disease, where the impact of bulk motion is reduced in a non-sedated neonatal patient and (b) where the observation of the impact of respiration on flow is necessary for diagnostics; (c) cardiopulmonary imaging, where cardiovascular flow, function, and anatomy information is needed along with pulmonary perfusion quantification; and in (d) renal function imaging, where blood velocities and glomerular filtration rates are simultaneously measured, which highlights the generality of the technique. XD flow has the ability to improve quantification and to provide additional data for patient diagnosis for comprehensive evaluations.
PMID: 28706270 [PubMed - in process]
Slice profile effects on nCPMG SS-FSE.
Magn Reson Med. 2017 Mar 31;:
Authors: Gibbons EK, Le Roux P, Pauly JM, Kerr AB
PURPOSE: To determine the effects of the RF refocusing pulse profile on the magnitude of the transverse signal smoothness throughout the echo train in non-Carr-Purcell-Meiboom-Gill (nCPMG) single-shot fast spin echo (SS-FSE) imaging and to design an RF refocusing pulse that provides improved signal stability. THEORY AND METHODS: nCPMG SS-FSE quadratic phase modulation requires sufficiently high and uniform refocusing flip angle to achieve a stable signal. Typically, refocusing pulses used in SS-FSE sequences are designed for minimum duration to minimize echo spacing and as a consequence have poor selectivity. However, delay-insensitive variable rate excitation Shinnar-Le Roux (DV-SLR) refocusing pulses can achieve both improved selectivity as well as a short duration. This class of RF pulse is compared against a traditional low time-bandwidth refocusing pulse in a nCPMG SS-FSE in simulation, phantom, and in vivo.
RESULTS: DV-SLR pulses achieve a more stable signal in simulation, phantom, and in vivo cases while maintaining an appropriately short duration as well as not dramatically increasing specific absorption rate (SAR) accumulation.
CONCLUSION: The nCPMG SS-FSE method demonstrates improved robustness when a more selective refocusing pulse is used. Refocusing pulses that use a time-varying excitation gradient can achieve this selectivity while maintaining short echo spacing. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
PMID: 28370409 [PubMed - as supplied by publisher]