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BACKGROUND A low-cryogen, compact 3T (C3T) MRI scanner with high-performance gradients capable of simultaneously achieving 80 mT/m gradient amplitude and 700 T/m/second slew rate has been in use to study research patients since March 2016 but has not been implemented in the clinical practice. PURPOSE To compare head MRI examinations obtained with the C3T system and a conventional whole-body 3T (WB3T) scanner in seven parameters across five commonly used brain imaging sequences. STUDY TYPE Prospective. SUBJECTS Thirty patients with a clinically indicated head MRI. SEQUENCE 3T; T1 FLAIR, T1 MP-RAGE, 3D T2 FLAIR, T2 FSE, and DWI. ASSESSMENT All patients tolerated the scans well. Three board-certified neuroradiologists scored the comparative quality of C3T and WB3T images in blinded fashion using a five-point Likert scale in terms of: signal-to-noise ratio, lesion conspicuity, motion artifact, gray/white matter contrast, cerebellar folia, susceptibility artifact, and overall quality. STATISTICAL TEST Left-sided, right-sided, and two-sided Wilcoxon signed rank test; Fisher's method. A P value

Purpose To address the need for a method to acquire 3D data for MR elastography (MRE) of the whole brain with substantially improved spatial resolution, high SNR, and reduced acquisition time compared with conventional methods. Methods We combined a novel 3D spiral staircase data-acquisition method with a spoiled gradient-echo pulse sequence and MRE motion-encoding gradients (MEGs). The spiral-out acquisition permitted use of longer-duration motion-encoding gradients (ie, over two full oscillatory cycles) to enhance displacement SNR, while still maintaining a reasonably short TE for good phase-SNR. Through-plane parallel imaging with low noise penalties was implemented to accelerate acquisition along the slice direction. Shared anatomical information was exploited in the deblurring procedure to further boost SNR for stiffness inversion. Results In vivo and phantom experiments demonstrated the feasibility of the proposed method in producing brain MRE results comparable to the spin-echo-based approaches, both qualitatively and quantitatively. High-resolution (2-mm isotropic) brain MRE data were acquired in 5 minutes using our method with good SNR. Joint deblurring with shared anatomical information produced SNR-enhanced images, leading to upward stiffness estimation. Conclusion A novel 3D gradient-echo-based approach has been designed and implemented, and shown to have promising potential for fast and high-resolution in vivo MRE of the whole brain.

Ultrasound vascular imaging based on ultrafast plane wave imaging and singular value decomposition (SVD) clutter filtering has demonstrated superior sensitivity in blood flow detection. However, ultrafast ultrasound vascular imaging is susceptible to electronic noise due to the weak penetration of unfocused waves, leading to a lower signal-to-noise ratio (SNR) at larger depths. In addition, incoherent clutter artifacts originating from strong and moving tissue scatterers that cannot be completely removed create a strong mask on top of the blood signal that obscures the vessels. Herein, a method that can simultaneously suppress the background noise and incoherent artifacts is proposed. The method divides the tilted plane or diverging waves into two sub-groups. Coherent spatial compounding is performed within each sub-group, resulting in two compounded datasets. An SVD-based clutter filter is applied to each dataset, followed by a correlation between the two datasets to produce a vascular image. Uncorrelated noise and incoherent artifacts can be effectively suppressed with the correlation process, while the coherent blood signal can be preserved. The method was evaluated in wire-target simulations and phantom, in which around 7 dB to 10 dB SNR improvement was shown. Consistent results were found in a flow channel phantom with improved SNR by the proposed method (39.9 ± 0.2 dB) against conventional power Doppler (29.1 ± 0.6 dB). Last, we demonstrated the effectiveness of the method combined with block-wise SVD clutter filtering in a human liver, breast tumor and inflammatory bowel disease datasets. The improved blood flow visualization may facilitate more reliable small vessel imaging for a wide range of clinical applications, such as cancer and inflammatory diseases.

OBJECTIVE. The Adaptive Image Receive (AIR) radiofrequency coil is an emergent technology that is lightweight and flexible and exhibits electrical characteristics that overcome many of the limitations of traditional rigid coil designs. The purpose of this study was to apply the AIR coil for whole-brain imaging and compare the performance of a prototype AIR coil array with the performance of conventional head coils. SUBJECTS AND METHODS. A phantom and 15 healthy adult participants were imaged. A prototype 16-channel head AIR coil was compared with conventional 8- and 32-channel head coils using clinically available MRI sequences. During consensus review, two board-certified neuroradiologists graded the AIR coil compared with an 8-channel coil and a 32-channel coil on a 5-point ordinal scale in multiple categories. One- and two-sided Wilcoxon signed rank tests were performed. Noise covariance matrices and geometry factor (g-factor) maps were calculated. RESULTS. The signal-to-noise ratio, structural sharpness, and overall image quality scores of the prototype 16-channel AIR coil were better than those of the 8-channel coil but were not as good as those of the 32-channel coil. Noise covariance matrices showed stable performance of the AIR coil across participants. The median g-factors for the 16-channel AIR coil were, overall, less than those of the 8-channel coil but were greater than those of the 32-channel coil. CONCLUSION. On average, the prototype 16-channel head AIR coil outperformed a conventional 8-channel head coil but did not perform as well as a conventional 32-channel head coil. This study shows the feasibility of the novel AIR coil technology for imaging the brain and provides insight for future coil design improvements.

Objective The quality of all clinical MRI is dependent on B0 homogeneity, which is optimized during the shimming part of a prescan or preparatory phase before image acquisition. The purpose of this study was to assess shimming techniques clinically employed for breast MRI across our practice, and to determine factors that correlate with higher image quality for contrast-enhanced breast MRI at 1.5T. Methods One hundred consecutive female patients were retrospectively collected with Institutional Review Board approval. Shimming-related parameters, including shim-box placement and shimming gradient offsets were extracted from prior contrast-enhanced 3D fat-suppressed T1-weighted gradient echo image acquisitions. Three breast radiologists evaluated these images for fat saturation, breast density, overall image quality, and artifacts. Technologist experience was also evaluated for variability of shimming. Generalized linear mixed models were used to compare acquisition parameters between fat saturation. P < 0.05 was considered as statistical significance. Results The percentage of soft tissue inside the field of view (FOV) (ie, Tissue/FOV) in the good fat-saturation group (0.37 ± 0.06) was significantly lower (P < 0.01) than that in the poor fat-saturation group (0.39 ± 0.06). Other shimming-related parameters were found not significantly affecting the fat-saturation outcomes. Technologists with more experience tended to have less variable shimming performance than junior technologists did. Conclusions The quality of clinical MRI and especially breast MRI is highly dependent on shimming. Decreasing Tissue/FOV was associated with good image quality (good fat saturation). Optimization of shimming may require manual shimming or higher-order field-correction strategies.

Ultrasound attenuation coefficient estimation has diagnostic potential for clinical applications such as differentiating tumors and quantifying fat content in the liver. The two commonly used attenuation coefficient estimation methods in ultrasound array imaging system are the spectral shift method and the reference-phantom-based methods. The spectra shift method estimates the central frequency downshift along depth, whereas the reference-phantom-based methods use a well-calibrated phantom to cancel system dependent effects in attenuation estimation. In this study, we propose a novel system- independent attenuation coefficient estimation technique based on spectra normalization of different frequencies. This technique does not require a reference phantom for normalization. The power of each frequency component is normalized by the power of an adjacent frequency component in the spectrum to cancel system-dependent effects such as focusing and time gain compensation. This method is referred to as the reference frequency method, and its performance has been evaluated in phantoms and in-vivo liver studies. The reference frequency method technique can be applied to various transducer geometries (e.g. linear or curved arrays) with different beam patterns (e.g. focused or unfocused).

BACKGROUND AND PURPOSE Neurodegeneration of the substantia nigra in Lewy body disease is associated with iron deposition, which increases the magnetic susceptibility of the substantia nigra on MRI. Our objective was to measure iron deposition in the substantia nigra in patients with probable dementia with Lewy bodies (pDLB) and patients who are at risk for pDLB by quantitative susceptibility mapping (QSM). METHODS Participants included pDLB (n = 36), mild cognitive impairment with at least one core feature of DLB (MCI-LB; n = 15), idiopathic rapid eye movement sleep behavior disorder (iRBD; n = 11), and an age-and gender-matched clinically unimpaired control group (n = 102). QSM was derived from multi-echo 3D gradient recalled echo MRI at 3T, and groups were compared on mean susceptibility values of the substantia nigra and its relation to parkinsonism severity. RESULTS Patients with pDLB had higher susceptibility in the substantia nigra compared to controls (p< 0.001) and MCI-LB (p = 0.043). The susceptibility of substantia nigra showed an increasing trend from controls to iRBD and MCI-LB, and to pDLB (p< 0.001). Parkinsonism severity was not associated with the mean susceptibility in the substantia nigra in the patient groups. CONCLUSIONS Our data suggested that QSM is sensitive to the increased magnetic susceptibility due to higher iron content in the substantia nigra in pDLB. The trend of increasing susceptibility from controls to iRBD and MCI-LB, and to pDLB suggests that iron deposition in the substantia nigra starts to increase as early as the prodromal stage in DLB and continues to increase as the disease progresses, independent of parkinsonism severity.

Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133 μm to 35 μm, which is smaller than the ultrasound wavelength (73 μm). The proposed method was further tested in an in vivo human liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170 μm (FWHM). Adjacent microvessels with a distance of 670 μm, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.

Contrast microbubble-based super-resolution ultrasound microvessel imaging (SR-UMI) overcomes the compromise in conventional ultrasound imaging between spatial resolution and penetration depth, and has been successfully applied to a wide range of clinical applications. However, clinical translation of SR-UMI remains challenging due to the limited number of microbubbles (MBs) detected within a given accumulation time. Here we propose a Kalman filter-based method for robust MB tracking and improved blood flow speed measurement with reduced numbers of MBs. An acceleration constraint and a direction constraint for MB movement were developed to control the quality of the estimated MB trajectory. An adaptive interpolation approach was developed to inpaint the missing microvessel signal based on the estimated local blood flow speed, facilitating more robust depiction of microvasculature with a limited amount of MBs. The proposed method was validated on an ex ovo chorioallantoic membrane and an in vivo rabbit kidney. Results demonstrated improved imaging performance on both microvessel density maps and blood flow speed maps. With the proposed method, the percentage of microvessel filling in a selected blood vessel at a given accumulation period was increased from 28.17% to 74.45%. A similar SR-UMI performance was achieved with MB numbers reduced by 85.96%, compared to that with the original MB number. The results indicate that the proposed method substantially improves the robustness of SR-UMI under a clinically relevant imaging scenario where SR-UMI is challenged by a limited MB accumulation time, reduced number of MBs, lowered imaging frame rate, and degraded signal-to-noise ratio.

PURPOSE To implement a reduced field of view (rFOV) technique for cardiac MR elastography (MRE) and to demonstrate the improvement in image quality of both magnitude images and post-processed MRE stiffness maps compared to the conventional full field of view (full-FOV) acquisition. METHODS With Institutional Review Board approval, 17 healthy volunteers underwent both full-FOV and rFOV cardiac MRE scans using 140-Hz vibrations. Two cardiac radiologists blindly compared the magnitude images and stiffness maps and graded the images based on several image quality attributes using a 5-point ordinal scale. Fisher's combined probability test was performed to assess the overall evaluation. The octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and median stiffness over the left ventricular myocardium were also compared. RESULTS One volunteer was excluded because of an inconsistent imaging resolution during the exam. In the remaining 16 volunteers (9 males, 7 females), the rFOV scans outperformed the full-FOV scans in terms of subjective image quality and ghosting artifacts in the magnitude images and stiffness maps, as well as the overall preference. The quantitative measurements showed that rFOV had significantly higher OSS-SNR (median: 1.4 [95% confidence interval (CI): 1.2-1.5] vs. 2.1 [95% CI: 1.8-2.4]), P

Ultrafast plane wave microvessel imaging significantly improves ultrasound Doppler sensitivity by increasing the number of Doppler ensembles that can be collected within a short period of time. The rich spatiotemporal plane wave data also enable more robust clutter filtering based on singular value decomposition. However, due to the lack of transmit focusing, plane wave microvessel imaging is very susceptible to noise. This paper was designed to: 1) study the relationship between ultrasound system noise (primarily time gain compensation induced) and microvessel blood flow signal and 2) propose an adaptive and computationally cost-effective noise equalization method that is independent of hardware or software imaging settings to improve microvessel image quality.

The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi-institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application-specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48-61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Purpose Imaging gradients result in the generation of concomitant fields, or Maxwell fields, which are of increasing importance at higher gradient amplitudes. These time-varying fields cause additional phase accumulation, which must be compensated for to avoid image artifacts. In the case of gradient systems employing symmetric design, the concomitant fields are well described with second-order spatial variation. Gradient systems employing asymmetric design additionally generate concomitant fields with global (zeroth-order or B0) and linear (first-order) spatial dependence. Methods This work demonstrates a general solution to eliminate the zeroth-order concomitant field by applying the correct B0 frequency shift in real time to counteract the concomitant fields. Results are demonstrated for phase contrast, spiral, echo-planar imaging (EPI), and fast spin-echo imaging. Results A global phase offset is reduced in the phase-contrast exam, and blurring is virtually eliminated in spiral images. The bulk image shift in the phase-encode direction is compensated for in EPI, whereas signal loss, ghosting, and blurring are corrected in the fast-spin echo images. Conclusion A user-transparent method to compensate the zeroth-order concomitant field term by center frequency shifting is proposed and implemented. This solution allows all the existing pulse sequences—both product and research—to be retained without any modifications. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

To assess whether acquisition with 32 receiver coils rather than the vendor-recommended 12 coils provides significantly improved performance in 3D dynamic contrast-enhanced MRI (DCE-MRI) of the prostate.The study was approved by the institutional review board and was compliant with HIPAA. 50 consecutive male patients in whom prostate MRI was clinically indicated were prospectively imaged in March 2015 with an accelerated DCE-MRI sequence in which image reconstruction was performed using 12 and 32 coil elements. The two reconstructions were compared quantitatively and qualitatively. The first was done using signal-to-noise ratio (SNR) and g-factor analysis to assess sensitivity to acceleration. The second was done using a five-point scale by two experienced radiologists using criteria of perceived SNR, artifact, sharpness, and overall preference. Significance was assessed with the Wilcoxon signed rank test. Extension to T2-weighted spin-echo and diffusion sequences was assessed in phantom studies.Reconstruction using 32 vs. 12 coil elements provided improved performance in DCE-MRI based on intrinsic SNR (18% higher) and g-factor statistics (14% higher), with a median 32% higher overall SNR within the prostate volume over all subjects. Reconstruction using 32 coils was qualitatively rated significantly improved (p0.001) vs. 12 coils on the basis of perceived SNR and radiologist preference and equivalent for sharpness and artifact. Phantom studies suggested the improvement in intrinsic SNR could extend to T2-weighted spin-echo and diffusion sequences.Reconstruction of 3D accelerated DCE-MRI studies of the prostate using 32 independent receiver coils provides improved overall performance vs. using 12 coils.

Purpose The homeostasis of intracranial pressure (ICP) is of paramount importance for maintaining normal brain function. A noninvasive technique capable of making direct measurements of ICP currently does not exist. MR elastography (MRE) is capable of noninvasively measuring brain tissue stiffness in vivo, and may act as a surrogate to measure ICP. The objective of this study was to investigate the impact of changing ICP on brain stiffness using MRE in a swine model. Methods Baseline MRE measurements were obtained, and then catheters were surgically placed into the left and right lateral ventricles of three animals. ICP was systematically increased over the range of 0 to 55 millimeters mercury (mmHg), and stiffness measurements were made using brain MRE at vibration frequencies of 60 hertz (Hz), 90 Hz, 120 Hz, and 150 Hz. Results A significant linear correlation between stiffness and ICP in the cross-subject comparison was observed for all tested vibrational frequencies (P ≤ 0.01). The 120 Hz (0.030 ± 0.004 kilopascal (kPa)/mmHg, P

Purpose The stiffness of a myocardial infarct affects the left ventricular pump function and remodeling. Magnetic resonance elastography (MRE) is a noninvasive imaging technique for measuring soft-tissue stiffness in vivo. The purpose of this study was to investigate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE in a porcine model of myocardial infarction, and compare the results with ex vivo uniaxial tensile testing. Methods Myocardial infarct was induced in a porcine model by embolizing the left circumflex artery. Fourteen days postinfarction, MRE imaging was performed in diastole using an echocardiogram-gated spin-echo echo-planar-imaging sequence with 140-Hz vibrations and 3D MRE processing. The MRE stiffness and tensile modulus from uniaxial testing were compared between the remote and infarcted myocardium. Results Myocardial infarcts showed increased in vivo MRE stiffness compared with remote myocardium (4.6 ± 0.7 kPa versus 3.0 ± 0.6 kPa, P = 0.02) within the same pig. Ex vivo uniaxial mechanical testing confirmed the in vivo MRE results, showing that myocardial infarcts were stiffer than remote myocardium (650 ± 80 kPa versus 110 ± 20 kPa, P = 0.01). Conclusions These results demonstrate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE. Magn Reson Med, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Purpose To evaluate if cardiac magnetic resonance elastography (MRE) can measure increased stiffness in patients with cardiac amyloidosis. Myocardial tissue stiffness plays an important role in cardiac function. A noninvasive quantitative imaging technique capable of measuring myocardial stiffness could aid in disease diagnosis, therapy monitoring, and disease prognostic strategies. We recently developed a high-frequency cardiac MRE technique capable of making noninvasive stiffness measurements. Materials and Methods In all, 16 volunteers and 22 patients with cardiac amyloidosis were enrolled in this study after Institutional Review Board approval and obtaining formal written consent. All subjects were imaged head-first in the supine position in a 1.5T closed-bore MR imager. 3D MRE was performed using 5 mm isotropic resolution oblique short-axis slices and a vibration frequency of 140 Hz to obtain global quantitative in vivo left ventricular stiffness measurements. The median stiffness was compared between the two cohorts. An octahedral shear strain signal-to-noise ratio (OSS-SNR) threshold of 1.17 was used to exclude exams with insufficient motion amplitude. Results Five volunteers and six patients had to be excluded from the study because they fell below the 1.17 OSS-SNR threshold. The myocardial stiffness of cardiac amyloid patients (median: 11.4 kPa, min: 9.2, max: 15.7) was significantly higher (P = 0.0008) than normal controls (median: 8.2 kPa, min: 7.2, max: 11.8). Conclusion This study demonstrates the feasibility of 3D high-frequency cardiac MRE as a contrast-agent-free diagnostic imaging technique for cardiac amyloidosis. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1361–1367.

Super-resolution ultrasound localization microscopy (ULM), based on localization and tracking of individual microbubbles (MBs), offers unprecedented microvascular imaging resolution at clinically relevant penetration depths. However, ULM is currently limited by the requirement of dilute MB concentrations to ensure spatially sparse MB events for accurate localization and tracking. The corresponding long imaging acquisition times (tens of seconds or several minutes) to accumulate sufficient isolated MB events for full reconstruction of microvasculature preclude the clinical translation of the technique. To break this fundamental tradeoff between acquisition time and MB concentration, in this paper we propose to separate spatially overlapping MB events into sub-populations, each with sparser MB concentration, based on spatiotemporal differences in the flow dynamics (flow speeds and directions). MB localization and tracking are performed for each sub-population separately, permitting more robust ULM imaging of high-concentration MB injections. The superiority of the proposed MB separation technique over conventional ULM processing is demonstrated in flow channel phantom data, and in the chorioallantoic membrane of chicken embryos with optical imaging as an in vivo reference standard. Substantial improvement of ULM is further demonstrated on a chicken embryo tumor xenograft model and a chicken brain, showing both morphological and functional microvasculature details at super-resolution within a short acquisition time (several seconds). The proposed technique allows more robust MB localization and tracking at relatively high MB concentrations, alleviating the need for dilute MB injections, and thereby shortening the acquisition time of ULM imaging and showing great potential for clinical translation.

This study presents the first implementation of functional ultrasound (fUS) imaging of the spinal cord to monitor local hemodynamic response to epidural electrical spinal cord stimulation (SCS) on two small and large animal models. SCS has been successfully applied to control chronic refractory pain and recently was evolved to alleviate motor impairment in Parkinson's disease and after spinal cord injury. At present, however, the mechanisms underlying SCS remain unclear, and current methods for monitoring SCS are limited in their capacity to provide the required sensitivity and spatiotemporal resolutions to evaluate functional changes in response to SCS. fUS is an emerging technology that has recently shown promising results in monitoring a variety of neural activities associated with the brain. Here we demonstrated the feasibility of performing fUS on two animal models during SCS. We showed in vivo spinal cord hemodynamic responses measured by fUS evoked by different SCS parameters. We also demonstrated that fUS has a higher sensitivity in monitoring spinal cord response than electromyography. The high spatial and temporal resolutions of fUS were demonstrated by localized measurements of hemodynamic responses at different spinal cord segments, and by reliable tracking of spinal cord responses to patterned electrical stimulations, respectively. Finally, we proposed optimized fUS imaging and post-processing methods for spinal cord. These results support feasibility of fUS imaging of the spinal cord and could pave the way for future systematic studies to investigate spinal cord functional organization and the mechanisms of spinal cord neuromodulation in vivo.

Purpose: To derive a noniterative gridding-type reconstruction framework for nonCartesian magnetic resonance imaging(MRI) that prospectively accounts for gradient nonlinearity (GNL)-induced image geometrical distortion during MR image reconstruction, as opposed to the standard, image-domain based GNL correction that is applied after reconstruction; to demonstrate that such framework is able to reduce the image blurring introduced by the conventional GNL correction, while still offering effective correction of GNL-induced geometrical distortion and compatibility with off-resonance correction. Methods: After introducing the nonCartesian MRI signal model that explicitly accounts for the effects of GNL and off-resonance, a noniterative gridding-type reconstruction framework with integrated GNL correction based on the type-III nonuniform fast Fourier transform (NUFFT) is derived. A novel type-III NUFFT implementation is then proposed as a numerically efficient solution to the proposed framework. The incorporation of simultaneous B0 off-resonance correction to the proposed framework is then discussed. Several phantom and in vivo data acquired via various 2D and 3D nonCartesian acquisitions, including 2D Archimedean spiral, 3D shells with integrated radial and spiral, and 3D radial sampling, are used to compare the results of the proposed and the standard GNL correction methods. Results: Various phantom and in vivo data demonstrate that both the proposed and the standard GNL correction methods are able to correct the coarse-scale geometric distortion and blurring induced by GNL and off-resonance. However, the standard GNL correction method also introduces blurring effects to corrected images, causing blurring of resolution inserts in the phantom images and loss of small vessel clarity in the angiography examples. On the other hand, the results after the proposed GNL correction show better depiction of resolution inserts and higher clarity of small vessel. Conclusions: The proposed GNL-integrated nonCartesian reconstruction method can mitigate the resolution loss that occurs during standard image-domain GNL correction, while still providing effective correction of coarse-scale geometric distortion and blurring induced by GNL and off-resonance.

Singular value decomposition (SVD)-based clutter filters can robustly reject the tissue clutter as compared with the conventional high pass filter-based clutter filters. However, the computational burden of SVD makes real time SVD-based clutter filtering challenging (e.g. frame rate at least 10–15 Hz with region of interest of about 4 × 4 cm2). Recently, we proposed an acceleration method based on randomized SVD (rSVD) clutter filtering and randomized spatial downsampling, which can significantly reduce the computational complexity without compromising the clutter rejection capability. However, this method has not been implemented on an ultrasound scanner and tested for its performance. In this study, we implement this acceleration method on a Verasonics scanner using a multi-core CPU architecture, and evaluate the selections of the imaging and processing parameters to enable real time micro-vessel imaging. The Blood-to-Clutter Ratio (BCR) performance was evaluated on a Verasonics machine with different settings of parameters such as block size and ensemble size. The demonstration of real time process was implemented on a 12-core CPU (downsampling factor of 12, 12-threads in this study) host computer. The processing time of the rSVD-based clutter filter was less than 30 ms and BCRs were higher than 20 dB as the block size, ensemble size and the rank of tissue clutter subspace were set as 30 × 30, 45 and 26 respectively. We also demonstrate that the micro-vessel imaging frame rate of the proposed architecture can reach approximately 22 Hz when the block size, ensemble size and the rank of tissue clutter subspace were set as 20 × 20 pixels, 45 and 26 respectively (using both images and supplementary videos). The proposed method may be important for real time 2D scanning of tumor microvessels in 3D to select and store the most representative 2D view with most abnormal micro-vessels for better diagnosis.

Singular value decomposition (SVD)-based clutter filter is widely used for functional ultrasound imaging such as super-resolution ultrasound localized microscopy and microvessel imaging. However, the demand of high computational complexity becomes one of the critical issues for the SVD-based clutter filter. Recently, randomized SVD-based (rSVD) clutter filter has been proposed for clutter suppression; the computational time can be dramatically reduced when combing with randomized spatial downsampling. In this study, we propose the use of multicore CPU architecture to implement the rSVD-based clutter filter with randomized spatial downsampling to demonstrate that it can perform in real time. The proposed multi-core CPU architecture was embedded as an external function on a Verasonics Vantage system (Verasonics Inc., Kirkland, WA, USA). As the number of ensembles and the rank of tissue subspace are set as 50 and 20, respectively, the corresponding processing time required only around 20 ms. In addition, we also demonstrated the feasibility of real time microvessel perfusion imaging by rSVD-based clutter filter and random spatial downsampling.

Ultrafast plane wave microvessel imaging significantly improves ultrasound Doppler sensitivity. The rich spatiotemporal plane wave data enables robust clutter filtering based on singular value decomposition (SVD). However, due to the lack of transmit focusing, plane wave microvessel imaging is very susceptible to noise. The mid-to-deep region of the microvessel image is typically saturated by noise, hampering the visibility of microvessels. This study was designed to: 1) study the fundamental relationship between ultrasound system noise and microvessel signal; 2) propose an adaptive and computationally cost-effective noise equalization method derived from the SVD clutter filtering process to improve microvessel image quality.

Singular value decomposition (SVD)-based clutter filtering has demonstrated superior clutter rejection performance in the emerging field of ultrasound microvessel imaging. To alleviate the computational burden of SVD, here we present a fast and robust clutter filter using randomized SVD (rSVD) and randomized spatial downsampling (rSD). rSVD accelerates SVD by approximating and removing the first k-order singular values that represent tissue, and rSD achieves further speed-up by allowing parallel processing without introducing artifacts associated with regularized downsampling.

Recently multiple studies have demonstrated microbubble (MB)-based super-resolution imaging (SRI) with ∼λ/10 subwavelength resolution on mice and rats. Clinical translation of SRI, however, faces many technical challenges such as low signal-to-noise-ratio (SNR) of the MB signal and physiologic and operator-induced motion in humans. In this study, we take advantage of the rich spatiotemporal information and high frame rate recording of MB signals by ultrafast imaging, and propose a spatiotemporal nonlocal means (NLM) denoising filter and bipartite graph(BG)-based MB pairing and tracking algorithm to achieve robust SRI.

Purpose The purpose of this work is to compare the behavior of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in contrast-enhanced MR angiography with background suppression performed by either a Dixon-type or subtraction-type method. Theory and Methods Theoretical expressions for the SNR and CNR for both background suppression techniques were derived. The theoretical Dixon:subtraction SNR and CNR ratios were compared to empirical ratios measured from phantom and in vivo studies for Dixon techniques utilizing one, two, and three echoes. Specifically, the SNR and CNR ratios were compared as the concentration of contrast material in the blood changed. Results Empirical measurements of the SNR and CNR ratios compared favorably with the ratios predicted by theory. As the contrast concentration was reduced, the SNR advantage of the Dixon techniques increased asymptotically. In the ideal case, the SNR improvement over subtraction contrast-enhanced MR angiography was at least twofold for one- and two-echo Dixon techniques and at least a factor of for the three-echo Dixon technique. Conclusion Expressions showing a contrast concentration-dependent SNR and CNR improvement of at least a factor of two when Dixon-type contrast-enhanced MR angiography is used in place of subtraction-type contrast-enhanced MR angiography were derived and validated with phantom and in vivo experiments. Magn Reson Med 74:81–92, 2015. © 2014 Wiley Periodicals, Inc.

Purpose Spatial position accuracy in magnetic resonance imaging (MRI) is an important concern for a variety of applications, including radiation therapy planning, surgical planning, and longitudinal studies of morphologic changes to study neurodegenerative diseases. Spatial accuracy is strongly influenced by gradient linearity. This work presents a method for characterizing the gradient non-linearity fields on a per-system basis, and using this information to provide improved and higher-order (9th vs. 5th) spherical harmonic coefficients for better spatial accuracy in MRI. Methods A large fiducial phantom containing 5229 water-filled spheres in a grid pattern is scanned with the MR system, and the positions all the fiducials are measured and compared to the corresponding ground truth fiducial positions as reported from a computed tomography (CT) scan of the object. Systematic errors from off-resonance (i.e., B0) effects are minimized with the use of increased receiver bandwidth (± 125 kHz) and two acquisitions with reversed readout gradient polarity. The spherical harmonic coefficients are estimated using an iterative process, and can be subsequently used to correct for gradient non-linearity. Test-retest stability was assessed with five repeated measurements on a single scanner, and cross-scanner variation on four different, identically-configured 3 T wide-bore systems. Results A decrease in the root-mean-square error (RMSE) over a 50 cm diameter spherical volume from 1.80 mm to 0.77 mm is reported here in the case of replacing the vendor's standard 5th order spherical harmonic coefficients with custom fitted 9th order coefficients, and from 1.5 mm to 1 mm by extending custom fitted 5th order correction to the 9th order. Minimum RMSE varied between scanners, but was stable with repeated measurements in the same scanner. Conclusions The results suggest that the proposed methods may be used on a per-system basis to more accurately calibrate MR gradient non-linearity coefficients when compared to vendor standard corrections.

Purpose To describe and evaluate the method we refer to as “vascular masking” for improving signal-to-noise ratio (SNR) retention in sensitivity encoding (SENSE)-accelerated contrast-enhanced magnetic resonance angiography (CE-MRA). Materials and Methods Vascular masking is a technique that restricts the SENSE unfolding of an accelerated subtraction angiogram to the voxels within the field of view known to have enhancing signal. This is a more restricted voxel set than that identified with conventional masking, which excludes only voxels in the air around the object. Thus, improved retention of SNR is expected. Evaluation was done in phantom and in vivo studies by comparing SNR and the g-factor in results reconstructed using vascular versus conventional masking. A radiological evaluation was also performed comparing conventional and vascular masking in R = 8 accelerated CE-MRA studies of the thighs (n = 21) and calves (n = 13). Results Images reconstructed with vascular masking showed a significant reduction in g-factor and improved retention of SNR versus those reconstructed with conventional masking. In the radiological evaluation, vascular masking consistently provided reduced background noise, improved luminal signal smoothness, and better small vessel conspicuity. Conclusion Vascular masking provides improved SNR retention and improved depiction of the vasculature in accelerated, subtraction 3D CE-MRA of the thighs and calves. J. Magn. Reson. Imaging 2014;39:1161–1170. © 2013 Wiley Periodicals, Inc.

Robust clutter filtering is essential for ultrasound small vessel imaging. Eigen-based clutter filtering techniques have recently shown great improvement in clutter rejection over conventional clutter filters in small animals. However, for in vivo human imaging, eigen-based clutter filtering can be challenging due to the complex spatially-varying tissue and noise characteristics. To address this challenge, we present a novel block-wise adaptive singular value decomposition (SVD) based clutter filtering technique. The proposed method divides the global plane wave data into overlapped local spatial segments, within which tissue signals are assumed to be locally coherent and noise locally stationary. This, in turn, enables effective separation of tissue, blood and noise via SVD. For each block, the proposed method adaptively determines the singular value cutoff thresholds based on local data statistics. Processing results from each block are redundantly combined to improve both the signal-to-noise-ratio (SNR) and the contrast-to-noise-ratio (CNR) of the small vessel perfusion image. Experimental results show that the proposed method achieved more than two-fold increase in SNR and more than three-fold increase in CNR in dB scale over the conventional global SVD filtering technique for an in vivo human native kidney study. The proposed method also showed substantial improvement in suppression of the depth-dependent background noise and better rejection of near field tissue clutter. The effects of different processing block size and block overlap percentage were systematically investigated as well as the tradeoff between imaging quality and computational cost.

CAPR is a SENSE-type parallel 3DFT acquisition paradigm for 4D contrast-enhanced magnetic resonance angiography (CE-MRA) that has been demonstrated capable of providing high spatial and temporal resolution, diagnostic-quality images at very high acceleration rates. However, CAPR images are typically reconstructed online using Tikhonov regularization and partial Fourier methods, which are prone to exhibit noise amplification and undersampling artifacts when operating at very high acceleration rates. In this work, a sparsity-driven offline reconstruction framework for CAPR is developed and demonstrated to consistently provide improvements over the currently-employed reconstruction strategy against these ill-effects. Moreover, the proposed reconstruction strategy requires no changes to the existing CAPR acquisition protocol, and an efficient numerical optimization and hardware system are described that allow for a 256×160×80 volume CE-MRA volume to be reconstructed from an 8-channel data set in less than two minutes.

Purpose: To investigate a novel locally adaptive projection space denoising algorithm for low-dose CT data. Methods: The denoising algorithm is based on bilateral filtering, which smooths values using a weighted average in a local neighborhood, with weights determined according to both spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus maintaining high spatial resolution. A CTnoise model that takes into account the bowtie filter and patient-specific automatic exposure control effects is also incorporated into the denoising process. The authors evaluated the noise-resolution properties of bilateral filtering incorporating such a CTnoise model in phantom studies and preliminary patient studies with contrast-enhanced abdominal CT exams. Results: On a thin wire phantom, the noise-resolution properties were significantly improved with the denoising algorithm compared to commercial reconstruction kernels. The noise-resolution properties on low-dose (40 mA s) data after denoising approximated those of conventional reconstructions at twice the dose level. A separate contrast plate phantom showed improved depiction of low-contrast plates with the denoising algorithm over conventional reconstructions when noise levels were matched. Similar improvement in noise-resolution properties was found on CT colonography data and on five abdominal low-energy (80 kV) CT exams. In each abdominal case, a board-certified subspecialized radiologist rated the denoised 80 kV images markedly superior in image quality compared to the commercially available reconstructions, and denoising improved the image quality to the point where the 80 kV images alone were considered to be of diagnostic quality. Conclusions: The results demonstrate that bilateral filtering incorporating a CTnoise model can achieve a significantly better noise-resolution trade-off than a series of commercial reconstruction kernels. This improvement in noise-resolution properties can be used for improving image quality in CT and can be translated into substantial dose reduction.

In clinical magnetic resonance imaging (MRI), any reduction in scan time offers a number of potential benefits ranging from high-temporal-rate observation of physiological processes to improvements in patient comfort. Following recent developments in compressive sensing (CS) theory, several authors have demonstrated that certain classes of MR images which possess sparse representations in some transform domain can be accurately reconstructed from very highly undersampled K-space data by solving a convex lscr1-minimization problem. Although lscr1-based techniques are extremely powerful, they inherently require a degree of over-sampling above the theoretical minimum sampling rate to guarantee that exact reconstruction can be achieved. In this paper, we propose a generalization of the CS paradigm based on homotopic approximation of the lscr0 quasi-norm and show how MR image reconstruction can be pushed even further below the Nyquist limit and significantly closer to the theoretical bound. Following a brief review of standard CS methods and the developed theoretical extensions, several example MRI reconstructions from highly undersampled K-space data are presented.

Dynamic contrast-enhanced (DCE) MRI is an important tool for the detection and characterization of primary and recurring prostate cancer. Advanced reconstruction strategies (e.g., sparse or low-rank regression) provide improved depiction of contrast dynamics and pharmacokinetic parameters; however, the high computation cost of reconstructing 4D (3D+time, 50+ frames) datasets typically inhibits their routine clinical use. Here, a novel alternating direction method-of-multipliers (ADMM) optimization strategy is described that enables these methods to be executed in ∠5 minutes, and thus within the standard clinical workflow. After overviewing the mechanics of this approach, high-performance implementation strategies will be discussed and demonstrated through clinical cases.

Purpose Noninvasive stiffness imaging techniques (elastography) can image myocardial tissue biomechanics in vivo. For cardiac MR elastography (MRE) techniques, the optimal vibration frequency for in vivo experiments is unknown. Furthermore, the accuracy of cardiac MRE has never been evaluated in a geometrically accurate phantom. Therefore, the purpose of this study was to determine the necessary driving frequency to obtain accurate three-dimensional (3D) cardiac MRE stiffness estimates in a geometrically accurate diastolic cardiac phantom and to determine the optimal vibration frequency that can be introduced in healthy volunteers. Methods The 3D cardiac MRE was performed on eight healthy volunteers using 80 Hz, 100 Hz, 140 Hz, 180 Hz, and 220 Hz vibration frequencies. These frequencies were tested in a geometrically accurate diastolic heart phantom and compared with dynamic mechanical analysis (DMA). Results The 3D Cardiac MRE was shown to be feasible in volunteers at frequencies as high as 180 Hz. MRE and DMA agreed within 5% at frequencies greater than 180 Hz in the cardiac phantom. However, octahedral shear strain signal to noise ratios and myocardial coverage was shown to be highest at a frequency of 140 Hz across all subjects. Conclusion This study motivates future evaluation of high-frequency 3D MRE in patient populations. Magn Reson Med, 2016. © 2016 Wiley Periodicals, Inc.

Sparsity-driven image reconstruction is a promising paradigm for improving the spatial, temporal, and contrast resolution in magnetic resonance imaging (MRI). However, high computational expense continues to inhibit the translation of these techniques into routine clinical practice. In many MRI acquisition protocols (e.g., time-resolved CAPR), the sampling operator can be factored into a uniform and non-uniform component. In this work, we present a novel alternating direction method-of-multipliers (ADMM) strategy for sparse reconstruction of multilevel sampled Cartesian SENSE-type MRI data, and discuss how this framework enables certain operations to be computed once offline and recycled during the reconstruction process rather than repeated at every iteration. We then demonstrate that this algorithmic framework enables sparse reconstruction of 3D contrast-enhanced MR angiogram (CE-MRA) time-series in just several minutes (which is clinically practical) rather than the several hours previously required.

In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra- and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.

Purpose: Due to engineering limitations, the spatial encoding gradient fields generated by an MRI scanner are inherently nonlinear and images must undergo geometric correction prior to display. The gradient nonlinearity (GNL) of a conventional symmetric whole-body gradient is typically characterized using a spherical harmonic polynomial (SHP) expansion of magnetic gradient fields with up to fifth-order terms. Only odd-order terms are required due to design symmetry (i.e., 3rd/5th-order). Recently, a high-performance asymmetric gradient system was developed which exhibits more complex GNL. Here, the GNL across the 26cm diameter-spherical-volume (DSV) of this system is characterized using a phantom-based iterative calibration procedure. Methods: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) phantom was scanned with a 3D IR-FSPGR sequence (sagittal acquisition, Nx=Ny=256, Nz=196, Δx=Δy=1.05mm, Δz=1.3mm, BW=±125kHz) on the asymmetric gradient system. The phantom (20cm diameter with 160 spherical fiducials of 1.0/1.5cm diameter) was imaged at isocenter and at spatial offsets of ±3cm along the superior/inferior, right/left, anterior/posterior axes to cover the 26cm DSV. The spatial positions of fiducials were measured from MR images and compared with the design positions to extract distortion field, which is then used to fit the SHP model using an iterative calibration procedure. The effect of higher-order terms was tested by incrementally increasing the model order from 5 to 10. The calibrated coefficients were then used to geometrically correct the images, and for each case the residual root-mean-square-error (RMSE) was determined. For comparison, RMSE was also determined for images corrected using coefficients obtained from electromagnetic simulation. Results: The calibrated coefficients (10th-order) reduce RMSE from 0.96mm (using a 10th-order correction from electromagnetic simulation) to 0.36mm. Conclusion: The model parameters for GNL correction over the 26cm DSV of an asymmetric gradient system were successfully determined. The use of calibrated coefficients up to 10thorder coefficients including even-order terms yielded improved geometric accuracy (RMSE) comparable to conventional symmetric gradients. Funding support: NIH R01EB010065

Radiation dose from CT scans is an increasing health concern in the practice of radiology. Higher dose scans can produce clearer images with high diagnostic quality, but may increase the potential risk of radiation-induced cancer or other side effects. Lowering radiation dose alone generally produces a noisier image and may degrade diagnostic performance. Recently, CT dose reduction based on non-local means (NLM) filtering for noise reduction has yielded promising results. However, traditional NLM denoising operates under the assumption that image noise is spatially uniform noise, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT data using a global filtering strength cannot achieve optimal denoising performance. In this work, we have developed a technique for efficiently estimating the local noise level for CT images, and have modified the NLM algorithm to adapt to local variations in noise level. The local noise level estimation technique matches the true noise distribution determined from multiple repetitive scans of a phantom object very well. The modified NLM algorithm provides more effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with the clinical workflow.

“Auto-calibrated” parallel imaging (PI) strategies provide a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit formation of either coil sensitivity profiles or a cross-coil correlation operator, and as a result reconstruction corresponds to solving a challenging bilinear optimization problem. In this work, we present a novel image-domain approach to calibration-free parallel imaging (CPI) reconstruction called CLEAR. After reviewing contemporary parallel MRI strategies and overviewing CLEAR, we investigate its computational complexity and memory footprint, demonstrate its standalone application to undersampled Cartesian acquisitions, and discuss generalizations and future directions of investigation.

The application of sparsity-driven reconstruction methods to MRI to date has largely focused on situations where high-contrast features (e.g., gadolinium-enhanced vessels) are of primary interest. In clinical practice, however, low contrast features such as subtle lesions are often of equal or greater interest. Using an American College of Radiology MR quality assurance phantom and test, we describe a novel framework for systematically and automatically evaluating the low-contrast object detectability performance of different undersampled image reconstruction methods. This platform is used to evaluate three such methods, two based on classic Tikhonov regularization and one sparsity-driven method based on l1-norm minimization (which is commonly used in compressive sensing, also known as compressed sensing, applications), across a wide range of sampling rates and parameterizations. Both the automated evaluation system and a manual evaluation of anatomical images with numerically-generated low contrast inserts demonstrate that sparse reconstructions exhibit superior low-contrast object detectability performance compared to both Tikhonov-regularized reconstructions. The implications of this result, and potential applications of both the described low-contrast object detectability platform and generalizations of it are then discussed. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc.

Compressive Sensing (CS) is a nascent sampling and reconstruction paradigm that describes how sparse or compressible signals can be accurately approximated using many fewer samples than traditionally believed. In magnetic resonance imaging (MRI), where scan duration is directly proportional to the number of acquired samples, CS has the potential to dramatically decrease scan time. However, the computationally expensive nature of CS reconstructions has so far precluded their use in routine clinical practice - instead, more-easily generated but lower-quality images continue to be used. We investigate the development and optimization of a proven inexact quasi-Newton CS reconstruction algorithm on several modern parallel architectures, including CPUs, GPUs, and Intel's Many Integrated Core (MIC) architecture. Our (optimized) baseline implementation on a quad-core Core i7 is able to reconstruct a 256 × 160×80 volume of the neurovasculature from an 8-channel, 10 × undersampled data set within 56 seconds, which is already a significant improvement over existing implementations. The latest six-core Core i7 reduces the reconstruction time further to 32 seconds. Moreover, we show that the CS algorithm benefits from modern throughput-oriented architectures. Specifically, our CUDA-base implementation on NVIDIA GTX480 reconstructs the same dataset in 16 seconds, while Intel's Knights Ferry (KNF) of the MIC architecture even reduces the time to 12 seconds. Such level of performance allows the neurovascular dataset to be reconstructed within a clinically viable time.

The purpose of this paper is to present a new image reconstruction algorithm for dynamic data, termed non-convex prior image constrained compressed sensing (NC-PICCS). It generalizes the prior image constrained compressed sensing (PICCS) algorithm with the use of non-convex priors. Here, we concentrate on perfusion studies using computed tomography examples in simulated phantoms (with and without added noise) and in vivo data, to show how the NC-PICCS method holds potential for dramatic reductions in radiation dose for time-resolved CT imaging. We show that NC-PICCS can provide additional undersampling compared to conventional convex compressed sensing and PICCS, as well as, faster convergence under a quasi-Newton numerical solver.

In medical imaging, image background is often defined by zero signal. Moreover, in medical images the background area - or conversely, the spatial support (the extent of the non-zero part of the image) - is often known a priori or can be easily estimated. For example, support information can be estimated from the low-resolution "scout" images typically acquired during pre-scan localization in both MRI and CT. In dynamic scans, object support in a single time-frame is often obtainable from a prior time frame, or from a composite image formed from data from multiple time frames. In this work, incorporation of either complete or partial a priori knowledge of object spatial support into the compressive sensing (CS) framework is investigated. Following development of the augmented reconstruction model, examples of support-constrained CS reconstruction of phantom and MR images under both exact and inexact support definitions are given. For each experiment, the straightforward incorporation of the proposed spatial support constraint into the standard CS model was shown to both significantly accelerate reconstruction convergence and yield a lower terminal RMSE compared to a conventional CS reconstruction. The proposed augmented reconstruction model was also shown to be robust to inaccuracies in the estimated object support.

In this work, a nonconvex Compressive Sensing model targeted at true 3D reconstructions of highly undersampled MR angiograms acquired with parallel imaging is proposed. When combined with the Max-CAPR acquisition sequence, it is demonstrated that high quality, non-view-shared, 3D images of the contrast-filled neurovasculature can be acquired (at acceleration factors exceeding the number of coils) in just over 2 seconds and reconstructed in as few as 14 minutes on a high-performance workstation.

A novel method for highly-undersampled Magnetic Resonance Image (MRI) reconstruction is presented. One of the principal challenges faced in clinical MR imaging is the fundamental linear relation between net exam duration and admissible spatial resolution. Increased scan duration diminishes patient comfort while increasing the risk of susceptibility to motion artifact and limits the ability to depict many physiological events at high temporal rates. With the recent development of Compressive Sampling theory, several authors have successfully demonstrated that clinical MR images possessing a sparse representation in some transform domain can be accurately reconstructed even when sampled at rates well below the Nyquist limit by casting the recovery as a convex e 1 -minimization problem. While e 1 -based techniques offer a sizeable advantage over Nyquist-limited methods, they nonetheless require a modest degree of over-sampling above the true theoretical minimum sampling rate in order to guarantee the achievability of exact reconstruction. In this work, we present a reconstruction model based on homotopic approximation of the e 0 quasi-norm and discuss the ability of this technique to reconstruct undersampled MR images at rates even lower than are achievable than with e 1 -minimization and arbitrarily close to the true minimum sampling rate. A semi-implicit numerical solver is presented for efficient numerical computation of the reconstruction process and several examples depicting the capability for accurate MRI reconstructions from highly-undersampled K-space data are presented.

Optimal noise control is critical for dose reduction in CT. In this work, we investigated the use of a locally-adaptive method for noise reduction in low-dose CT. This method is based upon bilateral filtering, which smoothes the projection data using a weighted average in a local neighborhood, where the weights are determined according to both the spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus without significantly sacrificing the spatial resolution. It is closely related to anisotropic diffusion, but is significantly faster. More importantly, a CT noise model can be readily incorporated in the filtering and denoising process. We have evaluated the noise-resolution properties of the bilateral filtering in a phantom study and a preliminary patient study with contrast-enhanced abdominal CT exams. The results demonstrated that bilateral filtering can achieve a better noise-resolution tradeoff than a series of commercial reconstruction kernels. This improvement on noise-resolution properties can be used for improving the image quality in low-dose CT and can also be translated to substantial dose reduction.

A major challenge in contemporary magnetic resonance imaging (MRI) lies in providing the highest resolution exam possible in the shortest acquisition period. Recently, several authors have proposed the use of L1-norm minimization for the reconstruction of sparse MR images fromhighly-undersampled k-space data. Despite promising results demonstrating the ability to accurately reconstruct images sampled at rates significantly below the Nyquist criterion, the extensive computational complexity associated with the existing framework limits its clinical practicality. In this work, we propose an alternative recovery framework based on homotopic approximation of the L0-norm and extend the reconstruction problemto a multiscale formulation. In addition to several interesting theoretical properties, practical implementation of this technique effectively resorts to a simple iterative alternation between bilteral filtering and projection of themeasured k-space sample set that can be computed in a matter of seconds on a standard PC.

"Compressed Sensing" and related L1-minimization methods for reconstructing sparse magnetic resonance images (MRI) acquired at sub-Nyquist rates have shown great potential for dramatically reducing exam duration. Nonetheless, the non-triviality of numerical implementation and computational intensity of these reconstruction algorithms has thus far precluded their widespread use in clinical practice. In this work, we propose a novel MRI reconstruction framework based on homotopy continuation of the L0 semi-norm using redescending M-estimator functions. Following analysis of the continuation scheme, the sparsity measure is extended to multiscale form and a simple numerical solver that can achieve accurate reconstructions in a matter of seconds on a standard desktop computer is presented.

The recent introduction of variable-velocity control for Continuously Moving Table Magnetic Resonance Imaging has introduced the ability for interactive examination across extended fields of view. A common application of this method is contrast-enhanced angiography, where propagation of an intravenously injected contrast agent is tracked throughout the peripheral vascular tree. Whereas current methods for performing contrast bolus tracking are entirely manual, we discuss the complete automation of this process through the use of real-time image processing. Specifically, we provide a coupled intensity-correction procedure and modified Fast Marching method for rapid segmentation of contrast enhanced vasculature in CE-MRA and discuss the incorporation of this process into a framework for fully automated and adaptive control of table motion for real-time tracking of contrast bolus propagation through the lower peripheral vasculature.