Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen.

PURPOSE: Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described. METHODS: A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI. RESULTS: The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns. CONCLUSION: Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection.

PURPOSE: Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion. To improve the robustness of DW imaging in the abdomen, region-based shot rejection schemes that selectively weight regions where the shot-to-shot phase is poorly estimated were evaluated. METHODS: Spatially varying weights for each shot, reflecting both the accuracy of the estimated phase and the degree of subvoxel dephasing, were estimated from the phase navigator magnitude images. The weighting was integrated into a multishot reconstruction using different formulations and phase navigator resolutions and tested with different phase navigator resolutions in multishot DW-echo Planar Imaging acquisitions of the liver and pancreas, using conventional monopolar and velocity-compensated diffusion encoding. Reconstructed images and ADC estimates were compared qualitatively. RESULTS: The proposed region-based shot rejection reduces banding and signal dropout artifacts caused by physiological motion in the liver and pancreas. Shot rejection allows conventional monopolar diffusion encoding to achieve median ADCs in the pancreas comparable to motion-compensated diffusion encoding, albeit with a greater spread of ADCs. CONCLUSION: Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.

A joint linear reconstruction for multishot diffusion weighted non-Carr-Purcell-Meiboom-Gill fast spin echo with full signal.

PURPOSE: Diffusion weighted Fast Spin Echo (DW-FSE) is a promising approach for distortionless DW imaging that is robust to system imperfections such as eddy currents and off-resonance. Due to non-Carr-Purcell-Meiboom-Gill (CPMG) magnetization, most DW-FSE sequences discard a large fraction of the signal ( 2 - 2 × $$ \sqrt{2}-2\times $$ ), reducing signal-to-noise ratio (SNR) efficiency compared to DW-EPI. The full FSE signal can be preserved by quadratically incrementing the transmit phase of the refocusing pulses, but this method of resolving non-CPMG magnetization has only been applied to single-shot DW-FSE due to challenges associated with image reconstruction. We present a joint linear reconstruction for multishot quadratic phase increment data that addresses these challenges and corrects ghosting from both shot-to-shot phase and intrashot signal oscillations. Multishot imaging reduces T2 blur and joint reconstruction of shots improves g-factor performance. A thorough analysis on the condition number of the proposed linear system is described. METHODS: A joint multishot reconstruction is derived from the non-CPMG signal model. Multishot quadratic phase increment DW-FSE was tested in a standardized diffusion phantom and compared to single-shot DW-FSE and DW-EPI in vivo in the brain, cervical spine, and prostate. The pseudo multiple replica technique was applied to generate g-factor and SNR maps. RESULTS: The proposed joint shot reconstruction eliminates ghosting from shot-to-shot phase and intrashot oscillations. g-factor performance is improved compared to previously proposed reconstructions, permitting efficient multishot imaging. apparent diffusion coefficient estimates in phantom experiments and in vivo are comparable to those obtained with conventional methods. CONCLUSION: Multi-shot non-CPMG DW-FSE data with full signal can be jointly reconstructed using a linear model.

Circumventing the curse of dimensionality in magnetic resonance fingerprinting through a deep learning approach.

Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. In this work, we design a deep learning approach to MRF using a fully connected network (FCN). In the first part we investigate, by means of simulations, how the NN performance scales with the number of parameters to be retrieved in comparison with the standard dictionary approach. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-B1 , and IR-bSSFP-B1 , the latter two designed to be more specific for B1+ parameter encoding. Estimation accuracy, memory usage, and computational time required to perform the estimation task were considered to compare the scalability capabilities of the dictionary-based and the NN approaches. In the second part we study optimal training procedures by including different data augmentation and preprocessing strategies during training to achieve better accuracy and robustness to noise and undersampling artifacts. The study is conducted using the IR-FISP MRF sequence exploiting both simulations and in vivo acquisitions. Results demonstrate that the NN approach outperforms the dictionary-based approach in terms of scalability capabilities. Results also allow us to heuristically determine the optimal training strategy to make an FCN able to predict T1 , T2 , and M0 maps that are in good agreement with those obtained with the original dictionary approach. k-SVD denoising is proposed and found to be critical as a preprocessing step to handle undersampled data.

Volumetric and multispectral DWI near metallic implants using a non-linear phase Carr-Purcell-Meiboom-Gill diffusion preparation.

PURPOSE: DWI near metal implants has not been widely explored due to substantial challenges associated with through-slice and in-plane distortions, the increased encoding requirement of different spectral bins, and limited SNR. There is no widely adopted clinical protocol for DWI near metal since the commonly used EPI trajectory fails completely due to distortion from extreme off-resonance ranging from 2 to 20 kHz. We present a sequence that achieves DWI near metal with moderate b-values (400-500 s/mm2 ) and volumetric coverage in clinically feasible scan times. THEORY AND METHODS: Multispectral excitation with Cartesian sampling, view angle tilting, and kz phase encoding reduce in-plane and through-plane off-resonance artifacts, and Carr-Purcell-Meiboom-Gill (CPMG) spin-echo refocusing trains counteract T2* effects. The effect of random phase on the refocusing train is eliminated using a stimulated echo diffusion preparation. Root-flipped Shinnar-Le Roux refocusing pulses permits preparation of a high spectral bandwidth, which improves imaging times by reducing the number of excitations required to cover the desired spectral range. B1 sensitivity is reduced by using an excitation that satisfies the CPMG condition in the preparation. A method for ADC quantification insensitive to background gradients is presented. RESULTS: Non-linear phase refocusing pulses reduces the peak B1 by 46% which allows RF bandwidth to be doubled. Simulations and phantom experiments show that a non-linear phase CPMG pulse pair reduces B1 sensitivity. Application in vivo demonstrates complementary contrast to conventional multispectral acquisitions and improved visualization compared to DW-EPI. CONCLUSION: Volumetric and multispectral DW imaging near metal can be achieved with a 3D encoded sequence.

Clinical utility of accelerated MAVRIC-SL with robust-PCA compared to conventional MAVRIC-SL in evaluation of total hip arthroplasties.

OBJECTIVE: To compare the diagnostic performance of a conventional metal artifact suppression sequence MAVRIC-SL (multi-acquisition variable-resonance image combination selective) and a novel 2.6-fold faster sequence employing robust principal component analysis (RPCA), in the MR evaluation of hip implants at 3 T. MATERIALS AND METHODS: Thirty-six total hip implants in 25 patients were scanned at 3 T using a conventional MAVRIC-SL proton density-weighted sequence and an RPCA MAVRIC-SL proton density-weighted sequence. Comparison was made of image quality, geometric distortion, visualization around acetabular and femoral components, and conspicuity of abnormal imaging findings using the Wilcoxon signed-rank test and a non-inferiority test. Abnormal findings were correlated with subsequent clinical management and intraoperative findings if the patient underwent subsequent surgery. RESULTS: Mean scores for conventional MAVRIC-SL were better than RPCA MAVRIC-SL for all qualitative parameters (p < 0.05), although the probability of RPCA MAVRIC-SL being clinically useful was non-inferior to conventional MAVRIC-SL (within our accepted 10% difference, p < 0.05), except for visualization around the acetabular component. Abnormal imaging findings were seen in 25 hips, and either equally visible or visible but less conspicuous on RPCA MAVRIC-SL in 21 out of 25 cases. In 4 cases, a small joint effusion was queried on MAVRIC-SL but not RPCA MAVRIC-SL, but the presence or absence of a small effusion did not affect subsequent clinical management and patient outcome. CONCLUSION: While the overall image quality is reduced, RPCA MAVRIC-SL allows for significantly reduced scan time and maintains almost equal diagnostic performance.

Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction.

Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.

Flexible and efficient optimization of quantitative sequences using automatic differentiation of Bloch simulations.

PURPOSE: To investigate a computationally efficient method for optimizing the Cramér-Rao Lower Bound (CRLB) of quantitative sequences without using approximations or an analytical expression of the signal. METHODS: Automatic differentiation was applied to Bloch simulations and used to optimize several quantitative sequences without the need for approximations or an analytical expression. The results were validated with in vivo measurements and comparisons to prior art. Multi-echo spin echo and DESPO T1 were used as benchmarks to verify the CRLB implementation. The CRLB of the Magnetic Resonance Fingerprinting (MRF) sequence, which has a complicated analytical formulation, was also optimized using automatic differentiation. RESULTS: The sequence parameters obtained for multi-echo spin echo and DESPO T1 matched results obtained using conventional methods. In vivo, MRF scans demonstrate that the CRLB optimization obtained with automatic differentiation can improve performance in presence of white noise. For MRF, the CRLB optimization converges in 1.1 CPU hours for NTR = 400 and has O(NTR) asymptotic runtime scaling for the calculation of the CRLB objective and gradient. CONCLUSIONS: Automatic differentiation can be used to optimize the CRLB of quantitative sequences without using approximations or analytical expressions. For MRF, the runtime is computationally efficient and can be used to investigate confounding factors as well as MRF sequences with a greater number of repetitions.

Correction of misleading information in prescription drug television advertising: The roles of advertisement similarity and time delay.

BACKGROUND: Prescription drug television advertisements containing potentially consequential misinformation sometimes appear in the United States. When that happens, the U.S. Food and Drug Administration can request that companies distribute corrective advertisements to address misinformation and inaccurate claims. Previous research has demonstrated effectiveness in corrective advertising for various products. OBJECTIVES: The present article builds on that work with a randomized experimental study (n = 6454) of corrective advertising investigating the extent to which visual similarity matters between violative and corrective ads and the extent to which time delay matters between violative and corrective advertisement exposure. METHODS: Our study sample included overweight or obese U.S. adults recruited from an existing online consumer panel representative of the U.S. adult population. We created a brand for a fictitious prescription weight-loss drug and produced corresponding direct-to-consumer (DTC) television ads. All participants viewed the same violative ad, but were randomly assigned to view corrective ads with different levels of visual similarity and exposure time delay using a 4 × 4 between-subjects factorial design. RESULTS: Results suggest corrective ad exposure can influence consumer perceptions of drug efficacy, risks, and benefits previously established by violative ads that overstated drug efficacy, broadened drug indication, and omitted important risk information. Corrective ads also can weaken consumer intentions to consider and investigate a drug. However, ad similarity does not appear to affect consumer perceptions and preferences. Although we found that the effects of violative ad exposure tend to diminish over time, the length of the delay between violative and corrective ad exposure has limited influence. An exception to this was observed with regard to recall of drug benefits and risks, where the impact of corrective ad exposure increases with greater time delay. CONCLUSIONS: These results extend previous research to a new health condition and hold implications for regulatory policy.

Prediction of soft tissue deformations after CMF surgery with incremental kernel ridge regression.

Facial soft tissue deformation following osteotomy is associated with the corresponding biomechanical characteristics of bone and soft tissues. However, none of the methods devised to predict soft tissue deformation after osteotomy incorporates population-based statistical data. The aim of this study is to establish a statistical model to describe the relationship between biomechanical characteristics and soft tissue deformation after osteotomy. We proposed an incremental kernel ridge regression (IKRR) model to accomplish this goal. The input of the model is the biomechanical information computed by the Finite Element Method (FEM). The output is the soft tissue deformation generated from the paired pre-operative and post-operative 3D images. The model is adjusted incrementally with each new patient's biomechanical information. Therefore, the IKRR model enables us to predict potential soft tissue deformations for new patient by using both biomechanical and statistical information. The integration of these two types of data is critically important for accurate simulations of soft-tissue changes after surgery. The proposed method was evaluated by leave-one-out cross-validation using data from 11 patients. The average prediction error of our model (0.9103mm) was lower than some state-of-the-art algorithms. This model is promising as a reliable way to prevent the risk of facial distortion after craniomaxillofacial surgery.

Estimating patient-specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation.

PURPOSE: A significant number of patients suffer from craniomaxillofacial (CMF) deformity and require CMF surgery in the United States. The success of CMF surgery depends on not only the surgical techniques but also an accurate surgical planning. However, surgical planning for CMF surgery is challenging due to the absence of a patient-specific reference model. Currently, the outcome of the surgery is often subjective and highly dependent on surgeon's experience. In this paper, the authors present an automatic method to estimate an anatomically correct reference shape of jaws for orthognathic surgery, a common type of CMF surgery. METHODS: To estimate a patient-specific jaw reference model, the authors use a data-driven method based on sparse shape composition. Given a dictionary of normal subjects, the authors first use the sparse representation to represent the midface of a patient by the midfaces of the normal subjects in the dictionary. Then, the derived sparse coefficients are used to reconstruct a patient-specific reference jaw shape. RESULTS: The authors have validated the proposed method on both synthetic and real patient data. Experimental results show that the authors' method can effectively reconstruct the normal shape of jaw for patients. CONCLUSIONS: The authors have presented a novel method to automatically estimate a patient-specific reference model for the patient suffering from CMF deformity.

Estimating anatomically-correct reference model for craniomaxillofacial deformity via sparse representation.

The success of craniomaxillofacial (CMF) surgery depends not only on the surgical techniques, but also upon an accurate surgical planning. However, surgical planning for CMF surgery is challenging due to the absence of a patient-specific reference model. In this paper, we present a method to automatically estimate an anatomically correct reference shape of jaws for the patient requiring orthognathic surgery, a common type of CMF surgery. We employ the sparse representation technique to represent the normal regions of the patient with respect to the normal subjects. The estimated representation is then used to reconstruct a patient-specific reference model with "restored" normal anatomy of the jaws. We validate our method on both synthetic subjects and patients. Experimental results show that our method can effectively reconstruct the normal shape of jaw for patients. Also, a new quantitative measurement is introduced to quantify the CMF deformity and validate the method in a quantitative approach, which is rarely used before.

Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization.

PURPOSE: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. METHODS: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into a maximum a posteriori probability-based convex segmentation framework for accurate segmentation. RESULTS: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. CONCLUSIONS: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT segmentation based on 15 patients.

Automated segmentation of CBCT image using spiral CT atlases and convex optimization.

Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. CBCT scans have relatively low cost and low radiation dose in comparison to conventional spiral CT scans. However, a major limitation of CBCT scans is the widespread image artifacts such as noise, beam hardening and inhomogeneity, causing great difficulties for accurate segmentation of bony structures from soft tissues, as well as separating mandible from maxilla. In this paper, we presented a novel fully automated method for CBCT image segmentation. In this method, we first estimated a patient-specific atlas using a sparse label fusion strategy from predefined spiral CT atlases. This patient-specific atlas was then integrated into a convex segmentation framework based on maximum a posteriori probability for accurate segmentation. Finally, the performance of our method was validated via comparisons with manual ground-truth segmentations.

Incremental kernel ridge regression for the prediction of soft tissue deformations.

This paper proposes a nonlinear regression model to predict soft tissue deformation after maxillofacial surgery. The feature which served as input in the model is extracted with finite element model (FEM). The output in the model is the facial deformation calculated from the preoperative and postoperative 3D data. After finding the relevance between feature and facial deformation by using the regression model, we establish a general relationship which can be applied to all the patients. As a new patient comes, we predict his/her facial deformation by combining the general relationship and the new patient's biomechanical properties. Thus, our model is biomechanical relevant and statistical relevant. Validation on eleven patients demonstrates the effectiveness and efficiency of our method.

K-39 quadrupolar and chemical shift tensors for organic potassium complexes and diatomic molecules.

Solid-state potassium-39 NMR spectra of two potassium complexes of crown-ether-based organic ligands (1.KI and 2) have been acquired at 11.75 and 21.1 T and interpreted to provide information on the 39K quadrupolar and chemical shift tensors. The analyses reveal a large potassium chemical shift tensor span of 75+/-20 ppm for 1.KI. This appears to be the first such measurement for potassium in an organic complex, thereby suggesting the utility of potassium chemical shift tensors for characterizing organic and biomolecular K+ binding environments. Compound 2 exhibits a cation-pi interaction between K+ and a phenyl group, and therefore, the 39K NMR tensors obtained for this compound must be partly representative of this interaction. Analyses of potassium-39 spin-rotation data for gaseous 39K19F and 39K35Cl available from molecular beam experiments performed by Cederberg and co-workers reveal the largest potassium CS tensor spans known to date, 84.39 and 141 ppm, respectively. Collectively, the results obtained highlight the potential of ultrahigh-field potassium-39 solid-state NMR spectroscopy and, in particular, the wide range of the anisotropy of the potassium CS tensor when organic and diatomic systems are considered.

Zygomatic implants--protocol for immediate occlusal loading: a preliminary report.

PURPOSE: To investigate the modified protocol for immediate occlusal loading of the zygomatic implants and to report the preliminary results of this modified protocol. MATERIALS AND METHODS: Four male patients and 1 female patient with edentulous maxillae were consecutively treated with the zygomatic implants under general anesthesia. All 5 patients were examined by computed tomography and investigated by the SimPlant software (Materialise NV, Leuven, Belgium). Based on the virtual surgical plans, mucosa-supported surgical guides were manufactured by rapid prototyping technique before implant operation. Instead of making a Le Fort I Osteotomy incision or a crestal incision, buccal vestibular incision was used to expose the surgical site for the zygomatic implant osteotomy and placement. Three patients had their remaining upper teeth removed on the same day as implant placement. One patient had undergone simultaneous placement of upper and lower implants followed by immediate loading. The immediate loading protocol was a 2-stage method using a customized provisional fixed prosthesis. RESULTS: Ten zygomatic implants and 20 normal implants were installed in these 5 patients. These 5 patients were reviewed regularly for 6 to 10 months after immediate loading. The zygomatic implants were considered to be successful when they were asymptomatic with no clinical mobility and no sign of infection. All the zygomatic implants and normal implants were investigated individually after removing the provisional prosthesis and were found to be clinically stable and asymptomatic. CONCLUSION: According to our observation, immediate occlusal loading of the zygomatic implants has a very good potential for success, as much as immediate occlusal loading of normal dental implants. The surgical placement of the zygomatic implant is simplified and facilitated by making use of the computer-assisted planning and the rapid-prototyping surgical guides.

Polymorphisms within the C-reactive protein (CRP) promoter region are associated with plasma CRP levels.

Elevated plasma levels of C-reactive protein (CRP), an inflammation-sensitive marker, have emerged as an important predictor of future cardiovascular disease and metabolic abnormalities in apparently healthy men and women. Here, we performed a systematic survey of common nucleotide variation across the genomic region encompassing the CRP gene locus. Of the common single-nucleotide polymorphisms (SNPs) identified, several in the CRP promoter region are strongly associated with CRP levels in a large cohort study of cardiovascular risk in European American and African American young adults. We also demonstrate the functional importance of these SNPs in vitro.