3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping.

PURPOSE: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. METHODS: 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B0 field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction. RESULTS: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. For T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis. CONCLUSIONS: The proposed technique enables rapid 3D distortion-free high-resolution imaging and T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm3 resolution.

In-plane simultaneous multisegment imaging using a 2D RF pulse.

PURPOSE: To develop an in-plane simultaneous multisegment (IP-SMS) imaging technique using a 2D-RF pulse and to demonstrate its ability to achieve high spatial resolution in EPI while reducing image distortion. METHODS: The proposed IP-SMS technique takes advantage of periodic replicates of the excitation profile of a 2D-RF pulse to simultaneously excite multiple segments within a slice. These segments were acquired over a reduced FOV and separated using a joint GRAPPA reconstruction by leveraging virtual coils that combined the physical coil sensitivity and 2D-RF pulse spatial response. Two excitations were used with complementary spatial response profiles to adequately cover a full FOV, producing a full-FOV image that had the benefits of reduced FOV with high spatial resolution and reduced distortion. The IP-SMS technique was implemented in a diffusion-weighted single-shot EPI sequence. Experimental demonstrations were performed on a phantom and healthy human brain. RESULTS: In the phantom experiment, IP-SMS enabled a four-fold acceleration using an eight-channel coil without causing residual aliasing artifacts. In the human brain experiment, diffusion-weighted images with high in-plane resolution (1 × 1 mm2 ) and substantially reduced image distortion were obtained in all imaging planes in comparison with a commercial diffusion-weighted EPI sequence. The capability of IP-SMS for contiguous whole-brain coverage was also demonstrated. CONCLUSION: The proposed IP-SMS technique can realize the benefits of reduced-FOV imaging while achieving a full-FOV coverage with good image quality and time efficiency.

Diffusion-weighted magnetic resonance imaging in rat kidney using two-dimensional navigated, interleaved echo-planar imaging at 7.0 T.

The purpose of this study was to investigate the feasibility of two-dimensional (2D) navigated, interleaved multishot echo-planar imaging (EPI) to enhance kidney diffusion-weighted imaging (DWI) in rats at 7.0 T. Fully sampled interleaved four-shot EPI with 2D navigators was tailored for kidney DWI (Sprague-Dawley rats, n = 7) on a 7.0-T small bore preclinical scanner. The image quality of four-shot EPI was compared with T2 -weighted rapid acquisition with relaxation enhancement (RARE) (reference) and single-shot EPI (ss-EPI) without and with parallel imaging (PI). The contrast-to-noise ratio (CNR) was examined to assess the image quality for the EPI approaches. The Dice similarity coefficient and the Hausdorff distance were used for evaluation of image distortion. Mean diffusivity (MD) and fractional anisotropy (FA) were calculated for renal cortex and medulla for all DWI approaches. The corticomedullary difference of MD and FA were assessed by Wilcoxon signed-rank test. Four-shot EPI showed the highest CNR among the three EPI variants and lowest geometric distortion versus T2 -weighted RARE (mean Dice: 0.77 for ss-EPI without PI, 0.88 for ss-EPI with twofold undersampling, and 0.92 for four-shot EPI). The FA map derived from four-shot EPI clearly identified a highly anisotropic region corresponding to the inner stripe of the outer medulla. Four-shot EPI successfully discerned differences in both MD and FA between renal cortex and medulla. In conclusion, 2D navigated, interleaved multishot EPI facilitates high-quality rat kidney DWI with clearly depicted intralayer and interlayer structure and substantially reduced image distortion. This approach enables the anatomic integrity of DWI-MRI in small rodents and has the potential to benefit the characterization of renal microstructure in preclinical studies.

[Secondary or Second Primary Endotracheal Cancer after Surgery for Primary Lung Cancer: Case Series and Review of the Literature].

BACKGROUND: The clinical feature, treatment and outcomes of the patients with endotracheal cancer after radical surgery for primary lung cancer are unclear. This article will provide a detailed explanation of the above issues by summarizing the relevant cases. METHODS: We retrospectively reviewed five patients in Guangdong Provincial People's Hospital and retrieved 9 cases from other literatures by searching PubMed. RESULTS: For five patients in out institute, 4 endotracheal cancers were considered as secondary to lung cancers. Radical stump resection (n=2), concurrent chemoradiotherapy (CCRT) (n=1), chemotherapy (n=1) and palliative care (n=1) were performed separately in 5 patients. The patient underwent CCRT achieved the longest progression-free survival of 29.5 months. For 9 patients retrieved from other studies, 8 endotracheal cancers were defined as metastases. Radiotherapy alone (n=4), CCRT (n=2), chemotherapy alone (n=2) or surgery (n=2) were performed separately in 10 lesions of 9 patients. 1 patient with radiotherapy alone and 1 patient with CCRT achieved complete response. CONCLUSIONS: More attention should be paid to the abnormality of the trachea after surgery of lung cancer. CCRT may be a good choice for endotracheal cancers after primary lung cancer.

New 3D phase-unwrapping method based on voxel clustering and local polynomial modeling: application to quantitative susceptibility mapping.

Background: To develop an accurate and robust 3-dimensional (3D) phase-unwrapping method that works effectively in the presence of severe noise, disconnected regions, rapid phase changes, and open-ended lines for quantitative susceptibility mapping (QSM). Methods: We developed a 3D phase-unwrapping method based on voxel clustering and local polynomial modeling named CLOSE3D, which firstly explores the 26-neighborhood to calculate local variation of the phasor and the phase, and then according to the local variation of the phasor, clusters the phase data into easy-to-unwrap blocks and difficult-to-unwrap residual voxels. Next, CLOSE3D sequentially performs intrablock, interblock, and residual-voxel unwrapping by using the region-growing local polynomial modeling method. CLOSED3D was evaluated in simulation and using in vivo brain QSM data, and was compared with classical region-growing and region-expanding labeling for unwrapping estimates methods. Results: The simulation experiments showed that CLOSE3D achieved accurate phase-unwrapping results with a mean error ratio <0.39%, even in the presence of serious noise, disconnected regions, and rapid phase changes. The error ratios of region-growing (P=0.000 and P=0.000) and region-expanding labeling for unwrapping estimates (P=0.007, P=0.014) methods were both significantly higher than that of CLOSE3D, when the noise level was ≥60%. The results of the in vivo brain QSM experiments showed that CLOSE3D unwrapped the phase data and accurately reconstructed quantitative susceptibility data, even with serious noise, rapid-varying phase, or an open-ended cutline. Conclusions: CLOSE3D achieves phase unwrapping with high accuracy and robustness, which will help phase-related 3D magnetic resonance imaging (MRI) applications such as QSM and susceptibility weighted imaging.

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping.

Background: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline. Methods: In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model. Initially, the method leverages phase partitioning to create initial regions. Noisy voxels connecting areas within these regions are excluded and grouped into residual voxels. The connected regions within the region of interest are then reidentified and categorized into blocks and residual voxels based on voxel count thresholds. Subsequently, the method sequentially performs inter-block and residual voxel phase unwrapping using the local polynomial model. The proposed method was evaluated on simulation and in vivo abdominal QSM data, and was compared with the classical Region-growing, Laplacian_based, Graph-cut, and PRELUDE methods. Results: Simulation experiments, conducted under different signal-to-noise ratios and phase change levels, consistently demonstrate that the proposed method achieves accurate unwrapping results, with mean error ratios not exceeding 0.01%. In contrast, the error ratios of Region-growing (N/A, 84.47%), Laplacian_based (20.65%, N/A), Graph-cut (2.26%, 20.71%), and PRELUDE (4.28%, 10.33%) methods are all substantially higher than those of the proposed method. In vivo abdominal QSM experiments further confirm the effectiveness of the proposed method in unwrapping phase data and successfully reconstructing susceptibility maps, even in scenarios with significant noise, rapidly changing phase, and open-end cutline in a large field of view. Conclusion: The proposed method demonstrates robust and accurate phase unwrapping capabilities, positioning it as a promising option for abdominal QSM applications.

Parametric MRI Detects Aristolochic Acid Induced Acute Kidney Injury.

Exposure to aristolochic acid (AA) is of increased concern due to carcinogenic and nephrotoxic effects, and incidence of aristolochic acid nephropathy (AAN) is increasing. This study characterizes renal alterations during the acute phase of AAN using parametric magnetic resonance imaging (MRI). An AAN and a control group of male Wistar rats received administration of aristolochic acid I (AAI) and polyethylene glycol (PEG), respectively, for six days. Both groups underwent MRI before and 2, 4 and 6 days after AAI or PEG administration. T2 relaxation times and apparent diffusion coefficients (ADCs) were determined for four renal layers. Serum creatinine levels (sCr) and blood urea nitrogen (BUN) were measured. Tubular injury scores (TIS) were evaluated based on histologic findings. Increased T2 values were detected since day 2 in the AAN group, but decreased ADCs and increased sCr levels and BUN were not detected until day 4. Significant linear correlations were observed between T2 of the cortex and the outer stripe of outer medulla and TIS. Our results demonstrate that parametric MRI facilitates early detection of renal injury induced by AAI in a rat model. T2 mapping may be a valuable tool for assessing kidney injury during the acute phase of AAN.

Label-free identification of human coronary atherosclerotic plaque based on a three-dimensional quantitative assessment of multiphoton microscopy images.

The rupture of coronary atherosclerotic plaque (CAP) and the resulting intracoronary thrombosis account for most acute coronary syndromes. Thus, the early identification and risk assessment of CAP is crucial for timely medical intervention. In this study, we propose a quantitative and label-free method for human CAP identification using multiphoton microscopy (MPM) and three-dimensional (3D) image analysis techniques. By detecting the intrinsic MPM signals, the microstructures of collagen and elastin fibers within normal and CAP-lesioned human coronary artery walls were imaged. Using a 3D gray level co-occurrence matrix method and 3D weighted vector summation algorithm, quantitative indicators that characterize the spatial texture and orientation features of the fibers were extracted. We demonstrate that these indicators show superior accuracy and repeatability over 2D texture features in CAP discrimination. Furthermore, by combining the 3D microstructural indicators, a support vector machine model that classifies CAP from the normal arterial wall with an accuracy of >97% was established. In conjunction with advances in multiphoton endoscopy, the proposed method shows great potential in providing a quantitative, label-free, and real-time tool for the early identification and risk assessment of CAP in the future.

[Elimination of Gibbs artifact based on local subpixel shift and interlaced local variation].

OBJECTIVE: To extend the application of Gibbs artifact reduction method that exploits local subvoxel- shifts (LSS) to zero- padded k-space magnetic resonance imaging (MRI) data. METHODS: We investigated two approaches to extending the application of LSS-based method to under-sampled data. The first approach, namely LSS+ interpolation, utilized the original LSS-based method to minimize the local variation on nonzero-padding reconstructed images, followed by image interpolation to obtain the final images. The second approach, interlaced local variation, used zero-padded Fourier transformation followed by elimination of Gibbs artifacts by minimizing a novel interlaced local variations (iLV) term. We compared the two methods with the original LSS and Hamming window filter algorithms, and verified their feasibility and robustness in phantom and in vivo data. RESULTS: The two methods proposed showed better performance than the original LSS and Hamming window filters and effectively eliminated Gibbs artifacts while preserving the image details. Compared to LSS + interpolation method, iLV method better preserved the details of the images. CONCLUSIONS: The iLV and LSS+interpolation methods proposed herein both extend the application of the original LSS method and can eliminate Gibbs artifacts in zero-filled k-space data reconstruction images, and iLV method shows a more prominent advantage in retaining the image details.

Rigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration.

Magnetic resonance fingerprinting (MRF) can be used to simultaneously obtain multiple parameter maps from a single pulse sequence. However, patient motion during MRF acquisition may result in blurring and artifacts in estimated parameter maps. In this work, a novel motion correction method was proposed to correct for rigid motion in MRF. The proposed method involved sliding-window reconstruction to obtain intermediate images followed by image registration to estimate rigid motion information between these images. Finally, the motion-corrupted k-space data were corrected with the estimated motion parameters and then reconstructed to obtain the parameter maps via the conventional MRF processing pipeline. The proposed method was evaluated using both simulations and in vivo MRF experiments with intently different types of motion. For motion-corrupted data, the proposed method yielded brain T1, T2 and proton density maps with obviously reduced blurring and artifacts and lower normalized root-mean-square error, compared to MRF without motion correction. In conclusion, motion-corrected MRF using the proposed method has the potential to produce accurate parameter maps in the presence of in-plane rigid motion.

eIRIS: Eigen-analysis approach for improved spine multi-shot diffusion MRI.

Image reconstruction using image-space sampling function (IRIS) corrects motion-induced inter-shot phase variations using phase maps from navigator-echo for multi-shot diffusion MRI. However, the bandwidth along the phase-encoding direction of navigator-echo is usually lower than that of image-echo, and thus their geometric distortions may be different. This geometric mismatch is corrected in IRIS by using the B0 map from an additional scan. In this paper, we present an enhanced IRIS (eIRIS) method that remove the requirement of B0 map. eIRIS treats shots as virtual coils, and then uses an eigen-analysis-based approach, which is insensitive to geometric mismatch, to estimates coil sensitivity maps containing the inter-shot phase variations. The final image is reconstructed under the framework of SENSE. Simulation, phantom, and cervical spine experiments were performed to evaluate the eIRIS method. The images generated by IRIS without B0 correction contain severe artifacts. eIRIS obtains results without noticeable artifacts and comparable to those of IRIS with B0 correction and GRAPPA with a compact kernel (GRAPPA-CK) method. eIRIS slightly outperforms GRAPPA-CK in the terms of normalized root-mean-square error and signal-to-noise ratio. eIRIS has the potential to obtain high-quality diffusion-weighted images and will benefit the research and clinical diagnosis of spinal cord.

Technical Note: Clustering-based motion compensation scheme for multishot diffusion tensor imaging.

PURPOSE: To extend image reconstruction using image-space sampling function (IRIS) to address large-scale motion in multishot diffusion-weighted imaging (DWI). METHODS: A clustered IRIS (CIRIS) algorithm that would extend IRIS was proposed to correct for large-scale motion. For DWI, CIRIS initially groups the shots into clusters without intracluster large-scale motion and reconstructs each cluster by using IRIS. Then, CIRIS registers these cluster images and combines the registered images by using a weighted average to correct for voxel mismatch caused by intercluster large-scale motion. For diffusion tensor imaging (DTI), CIRIS further reduces the effect of motion on diffusion directions by treating motion-induced direction changes as additional diffusion directions. CIRIS also introduces the detection and rejection of motion-corrupted data to avoid corresponding image degradation. The proposed method was evaluated by simulation and in vivo diffusion datasets. RESULTS: Experiments demonstrated that CIRIS can reduce motion-induced blurring and artifacts in DWI and provide more accurate DTI estimations in the presence of large-scale motion, compared with IRIS. CONCLUSION: The proposed method presents a novel approach to correct for large-scale in-plane motion for multishot DWI and is expected to benefit the practical application of high-resolution diffusion imaging.

Denoising of 3D magnetic resonance images by using higher-order singular value decomposition.

The denoising of magnetic resonance (MR) images is important to improve the inspection quality and reliability of quantitative image analysis. Nonlocal filters by exploiting similarity and/or sparseness among patches or cubes achieve excellent performance in denoising MR images. Recently, higher-order singular value decomposition (HOSVD) has been demonstrated to be a simple and effective method for exploiting redundancy in the 3D stack of similar patches during denoising 2D natural image. This work aims to investigate the application and improvement of HOSVD to denoising MR volume data. The wiener-augmented HOSVD method achieves comparable performance to that of BM4D. For further improvement, we propose to augment the standard HOSVD stage by a second recursive stage, which is a repeated HOSVD filtering of the weighted summation of the residual and denoised image in the first stage. The appropriate weights have been investigated by experiments with different image types and noise levels. Experimental results over synthetic and real 3D MR data demonstrate that the proposed method outperforms current state-of-the-art denoising methods.