Multi-contrast Imaging Research Articles

Analysis of leakage artifacts and their impact on convergence of algebraic reconstruction in multi-contrast magnetic particle imaging

Objective.Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconstructs the signal from different tracer materials or environments separately, resulting in multi-channel images that enable quantification of, for example, temperature or viscosity. Single- and multi-contrast MPI reconstructions produce different kinds of artifacts. The objective of this work is threefold: first, to present the concept of multi-contrast specific MPI channel leakage artifacts; second, to ascertain the source of these leakage artifacts; and third, to introduce a method for their reduction.Approach.A definition for leakage artifacts is established, and a quantification method is proposed. A comprehensive analysis is conducted to establish a connection between the properties of the multi-contrast MPI system matrix and the leakage artifacts. Moreover, a two-step measurement and reconstruction method is introduced to reduce channel leakage artifacts between multi-contrast MPI channels.Main results.The severity of these artifacts correlates with the system matrix shape and condition number and depends on the similarity of the corresponding frequency components. Using the proposed two-step method on both semi-simulated and measured data a significant leakage reduction and speed up the convergence of the multi-contrast MPI reconstruction was observed.Significance.The multi-contrast system matrix analysis we conducted is essential for understanding the source of the channel leakage artifacts and finding methods to reduce them. Our proposed two-step method is expected to improve the potential for real-time multi-contrast MPI applications.

Targeted K-Edge Nanoprobes From Praseodymium and Hafnium for Ratiometric Tracking of Dual Biomarkers using Spectral Photon Counting CT.

Utilizing metal nanoprobes with unique K-edge identities to visualize complementary biological activities simultaneously can provide valuable information about complex biological processes. This study describes the design and preparation of an innovative pair of K-edge metal nanoprobes and demonstrates the feasibility of their simultaneous quantitative detection using spectral photon-counting computed tomography (SPCCT). Glycosaminoglycan (GAG) capped nanoparticles (ca. 15-20nm) targeting two distinct components of the cartilage tissue, namely, aggrecan (acan) and aggrecanase (acanase) are designed and synthesized. These targeted nanoparticles comprised of praseodymium (Pr) and hafnium (Hf), with well-separated K-edge energies, enable simultaneous and ratiometric imaging of dual biomarkers in cartilage tissue. Following extensive physico-chemical characterization of the ligand-targeted particles, the feasibility of homing dual biomarkers in vitro is demonstrated. The material discrimination and simultaneous quantification of these targeted particles are also achieved and corroborated with inductively coupled plasmon spectroscopy. For the first time, the use of praseodymium is reported as a contrast agent for SPCCT imaging and demonstrates the ability to pair it with hafnium nanoprobes for multicontrast imaging of diseases. Importantly, the potential for ratiometric molecular imaging and tracking of osteoarthritis (OA) progression is shown with SPCCT K-edge based imaging approach.

Multi-contrast image super-resolution with deformable attention and neighborhood-based feature aggregation (DANCE): Applications in anatomic and metabolic MRI

Multi-contrast magnetic resonance imaging (MRI) reflects information about human tissues from different perspectives and has wide clinical applications. By utilizing the auxiliary information from reference images (Refs) in the easy-to-obtain modality, multi-contrast MRI super-resolution (SR) methods can synthesize high-resolution (HR) images from their low-resolution (LR) counterparts in the hard-to-obtain modality. In this study, we systematically discussed the potential impacts caused by cross-modal misalignments between LRs and Refs and, based on this discussion, proposed a novel deep-learning-based method with Deformable Attention and Neighborhood-based feature aggregation to be Computationally Efficient (DANCE) and insensitive to misalignments. Our method has been evaluated in two public MRI datasets, i.e., IXI and FastMRI, and an in-house MR metabolic imaging dataset with amide proton transfer weighted (APTW) images. Experimental results reveal that our method consistently outperforms baselines in various scenarios, with significant superiority observed in the misaligned group of IXI dataset and the prospective study of the clinical dataset. The robustness study proves that our method is insensitive to misalignments, maintaining an average PSNR of 30.67 dB when faced with a maximum range of ±9°and ±9 pixels of rotation and translation on Refs. Given our method’s desirable comprehensive performance, good robustness, and moderate computational complexity, it possesses substantial potential for clinical applications.

Simultaneous iodine and barium imaging with photon-counting CT

Photon-counting computed tomography (PCCT) could potentially offer significant improvements in image quality over conventional CT. This study leverages PCCT technology to achieve simultaneous multi-contrast imaging of barium (Z=56) and iodine (Z=53), addressing the limitations of conventional CT in differentiating materials with similar atomic numbers. Using a bench-top PCCT system with a cadmium zinc telluride (CZT) detector, experiments were conducted with various phantom setups and 1-5\% contrast agent concentrations and in a biological sample. Energy thresholds were adjusted to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed with K-edge subtraction and principal component analysis (PCA) techniques, demonstrating clear differentiation and accurate quantification of contrast agents in phantom combinations and the biological sample. The PCCT system successfully differentiated and quantified barium and iodine, with high correlations ($R^2 \approx 1$) between true and reconstructed concentrations. PCA outperformed K-edge subtraction by providing a superior capability to differentiate barium and iodine, particularly when calcium was present in the scanned object. These findings highlight the potential of PCCT for reliable, detailed imaging in clinical and research applications, particularly for contrast agents with close atomic numbers.

Retrospective motion correction for cardiac multi-parametric mapping with dictionary matching-based image synthesis and a low-rank constraint.

To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping. The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively. The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments. The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods.

Abdominal MR Multitasking for radiotherapy treatment planning: A motion-resolved and multicontrast 3D imaging approach.

Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion-resolved imaging are desired for accurate delineation of the target and various organs-at-risk and patient-tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)-based technique to produce volumetric, motion-resolved, multicontrast images for abdominal radiotherapy treatment planning. The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization-prepared (saturation recovery and T2 preparations) spoiled gradient-echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast-weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). The developed MT-based, abdomen-dedicated technique is capable of providing motion-resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning.

Pathophysiology of carotid atherosclerosis: Calcification, intraplaque haemorrhage and pulse pressure as key players

PurposeIntraplaque haemorrhage (IPH) is a well-known risk factor for faster plaque progression (volume increase); however, its etiology is unclear. We aimed at determining what other local plaque- and systemic factors contribute to plaque progression and to the development and progression of IPH. MethodsWe examined 98 asymptomatic participants with carotid plaque using serial multi-contrast magnetic resonance imaging. We measured the percent of wall volume (%WV=100 x [wall volume] / [total vessel volume]) and measured IPH and calcification volumes. We used generalized estimating equations-based regression to analyze predictors of %WV change and new IPH while accounting for covariates (sex, age and statin use), and multiple non-independent observations per participant. ResultsTotal follow-up was 1.8 ± 0.8 years on average. The presence of IPH (β: 0.6 %/y, p = 0.033) and calcification (β: 1.2 %/y, p = 0.028) were each associated with faster plaque progression. New IPH, detected on a subsequent scan in 4 % of arteries that did not initially have IPH, was associated with larger calcification (odds ratio [OR]: 2.6 per 1-SD increase, p = 0.038) and higher pulse pressure (OR: 2.3 per 1-SD increase, p = 0.016). Larger calcification was associated with greater increases in pulse pressure (β: 1.4 mm Hg/y per 1-SD increase, p = 0.040). ConclusionsIPH and calcification are each independently associated with faster plaque progression. The association of carotid calcification to increased pulse pressure and new IPH development suggests a possible mechanism by which calcification drives IPH development and plaque progression.

In vivo multi-contrast depth-resolved choroidal imaging of a mouse using polarization-diversity optical coherence tomography.

The results of depth-resolved multi-contrast in vivo mouse choroidal imaging using a polarization-diversity optical coherence tomography (PD-OCT) system are presented. A selectively chosen depth of focus that was fine-tuned with a sensorless adaptive optics technique and a simple segmentation based on the degree of polarization uniformity signal visualizes the detailed features of a mouse choroid from the OCT angiography images. A comprehensive image analysis of the choroid revealed the distinctive pathological characteristics of the laser-induced choroidal neovascularization mouse.

A multi-task generative model for simultaneous post-contrast MR image synthesis and brainstem glioma segmentation

ObjectivesThis study aims to generate post-contrast MR images reducing the exposure of gadolinium-based contrast agents (GBCAs) for brainstem glioma (BSG) detection, simultaneously delineating the BSG lesion, and providing high-resolution contrast information. MethodsA retrospective cohort of 30 patients diagnosed with brainstem glioma was included. Multi-contrast images, including pre-contrast T1 weighted (pre-T1w), T2 weighted (T2w), arterial spin labeling (ASL) and post-contrast T1w images, were collected. A multi-task generative model was developed to synthesize post-contrast T1w images and simultaneously segment BSG masks from the multi-contrast inputs. Performance evaluation was conducted using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) metrics. A perceptual study was also undertaken to assess diagnostic quality. ResultsThe proposed model achieved SSIM of 0.86 ± 0.04, PSNR of 26.33 ± 0.05 and MAE of 57.20 ± 20.50 for post-contrast T1w image synthesis. Automated delineation of the BSG lesions achieved Dice similarity coefficient (DSC) score of 0.88 ± 0.27. ConclusionsThe proposed model can synthesize high-quality post-contrast T1w images and accurately segment the BSG region, yielding satisfactory DSC scores. Clinical relevance statementThe synthesized post-contrast MR image presented in this study has the potential to reduce the usage of gadolinium-based contrast agents, which may pose risks to patients. Moreover, the automated segmentation method proposed in this paper aids radiologists in accurately identifying the brainstem glioma lesion, facilitating the diagnostic process.

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The ‘CMRxRecon’ dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN

To propose a style transfer model for multi-contrast magnetic resonance imaging (MRI) images with a cycle-consistent generative adversarial network (CycleGAN) and evaluate the image quality and prognosis prediction performance for glioblastoma (GBM) patients from the extracted radiomics features. Style transfer models of T1 weighted MRI image (T1w) to T2 weighted MRI image (T2w) and T2w to T1w with CycleGAN were constructed using the BraTS dataset. The style transfer model was validated with the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset. Moreover, imaging features were extracted from real and synthesized images. These features were transformed to rad-scores by the least absolute shrinkage and selection operator (LASSO)-Cox regression. The prognosis performance was estimated by the Kaplan–Meier method. For the accuracy of the image quality of the real and synthesized MRI images, the MI, RMSE, PSNR, and SSIM were 0.991 ± 2.10 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document}, 2.79 ± 0.16, 40.16 ± 0.38, and 0.995 ± 2.11 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document}, for T2w, and .992 ± 2.63 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document}, 2.49 ± 6.89 ×10-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-2}$$\\end{document}, 40.51 ± 0.22, and 0.993 ± 3.40 ×10-4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes {10}^{-4}$$\\end{document} for T1w, respectively. The survival time had a significant difference between good and poor prognosis groups for both real and synthesized T2w (p < 0.05). However, the survival time had no significant difference between good and poor prognosis groups for both real and synthesized T1w. On the other hand, there was no significant difference between the real and synthesized T2w in both good and poor prognoses. The results of T1w were similar in the point that there was no significant difference between the real and synthesized T1w. It was found that the synthesized image could be used for prognosis prediction. The proposed prognostic model using CycleGAN could reduce the cost and time of image scanning, leading to a promotion to build the patient’s outcome prediction with multi-contrast images.

Assessing the efficacy of 3D Dual-CycleGAN model for multi-contrast MRI synthesis

BackgroundThis research presents a novel methodology for synthesizing 3D multi-contrast MRI images utilizing the 3D Dual-CycleGAN architecture. The performance of the model is evaluated on different MRI sequences, including T1-weighted (T1W), T1-weighted contrast-enhanced (T1c), T2-weighted (T2W), and FLAIR sequences.ResultsOur approach demonstrates proficient learning capabilities in transforming T1W images into target modalities. The proposed framework encompasses a combination of different loss functions including voxel-wise, gradient difference, perceptual, and structural similarity losses. These loss components, along with adversarial and dual cycle-consistency losses, contribute significantly to realistic and accurate syntheses. Evaluation metrics including MAE, PMAE, RMSE, PCC, PSNR, and SSIM are employed to assess the fidelity of synthesized images compared to their ground truth counterparts. Empirical results indicate the effectiveness of the 3D Dual-CycleGAN model in generating T1c images from T1W inputs with minimal average discrepancies (MAE of 2.8 ± 2.61) and strong similarity (SSIM of 0.82 ± 0.28). Furthermore, the synthesis of T2W and FLAIR images yields promising outcomes, demonstrating acceptable average discrepancies (MAE of 3.87 ± 3.32 for T2W and 3.82 ± 3.32 for FLAIR) and reasonable similarities (SSIM of 0.82 ± 0.28 for T2W and 0.80 ± 0.29 for FLAIR) relative to the original images.ConclusionsThese findings underscore the efficacy of the 3D Dual-CycleGAN model in generating high-fidelity images, with significant implications for diverse applications in the field of medical imaging.

A pilot study on interobserver variability in organ-at-risk contours in magnetic resonance imaging-guided online adaptive radiotherapy for pancreatic cancer.

Differences in the contours created during magnetic resonance imaging-guided online adaptive radiotherapy (MRgOART) affect dose distribution. This study evaluated the interobserver error in delineating the organs at risk (OARs) in patients with pancreatic cancer treated with MRgOART. Moreover, we explored the effectiveness of drugs that could suppress peristalsis in restraining intra-fractional motion by evaluating OAR visualization in multiple patients. This study enrolled three patients who underwent MRgOART for pancreatic cancer. The study cohort was classified into three conditions based on the MRI sequence and butylscopolamine administration (Buscopan): 1, T2 imaging without butylscopolamine administration; 2, T2 imaging with butylscopolamine administration; and 3, multi-contrast imaging with butylscopolamine administration. Four blinded observers visualized the OARs (stomach, duodenum, small intestine, and large intestine) on MR images acquired during the initial and final MRgOART sessions. The contour was delineated on a slice area of ±2 cm surrounding the planning target volume. The dice similarity coefficient (DSC) was used to evaluate the contour. Moreover, the OARs were visualized on both MR images acquired before and after the contour delineation process during MRgOART to evaluate whether peristalsis could be suppressed. The DSC was calculated for each OAR. Interobserver errors in the OARs (stomach, duodenum, small intestine, large intestine) for the three conditions were 0.636, 0.418, 0.676, and 0.806; 0.725, 0.635, 0.762, and 0.821; and 0.841, 0.677, 0.762, and 0.807, respectively. The DSC was higher in all conditions with butylscopolamine administration compared with those without it, except for the stomach in condition 2, as observed in the last session of MR image. The DSCs for OARs (stomach, duodenum, small intestine, large intestine) extracted before and after contouring were 0.86, 0.78, 0.88, and 0.87; 0.97, 0.94, 0.90, and 0.94; and 0.94, 0.86, 0.89, and 0.91 for conditions 1, 2, and 3, respectively. Butylscopolamine effectively reduced interobserver error and intra-fractional motion during the MRgOART treatment.

Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.

To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast MRI to improve upon conventional risk assessment models in determining culprit carotid artery lesions. Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features. Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone (area under curve [AUC], 0.819 ± 0.002 vs 0.689 ± 0.019, respectively, P = .014). The radiomic model alone also provided value over the conventional model (AUC, 0.805 ± 0.003 vs 0.689 ± 0.019, respectively, P = .031). T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix, Grey Level Dependence Matrix, and Grey Level Size Zone Matrix sub-types were particularly useful in identifying textures which could detect vulnerable lesions. The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischaemic cerebrovascular events. The clinical relevance of this work is that it addresses the need for a more comprehensive method of risk assessment for patients at risk of ischaemic stroke, beyond conventional stenosis measurement. This paper shows that in the case of carotid stroke, high-dimensional radiomics features can improve classification capabilities compared with stenosis measurement alone.

Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study.

This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.

Multi-contrast echo-planar imaging sequence (Echo-planar imaging mix) in clinical situations demanding faster MRI-brain scans.

The excellent resolution offered by magnetic resonance imaging (MRI) has a trade-off in the form of scan duration. The purpose of the present study was to assess the clinical utility of echo-planar imaging mix (EPIMix), an echo-planar imaging-based MRI sequence for the brain with a short acquisition time. This was a retrospective observational study of 50 patients, who could benefit from faster MRI brain scans. The T1, T2, fluid attenuated inversion recovery, diffusion-weighted imaging (DWI), and T2*/susceptibility-weighted imaging sequences were acquired, conventionally and with EPIMix. Conventional and EPIMix images were assessed by two radiologists for overall quality, motion, and susceptibility artifacts and scored on a Likert scale. The scores given for conventional and EPIMix images were compared. The diagnostic performance of EPIMix was also assessed by the ability to detect clinically relevant findings. The acquisition time for conventional MRI was 11 min and 45 s and for EPIMix 1 min and 15 s. All EPIMix images were sufficient for diagnostic use. On assessment of the diagnostic performance, it was excellent for ischemic and hemorrhagic strokes. Smaller lesions, lesions adjacent to bone, and post-operative tumors were difficult to identify. Moderate to perfect agreement (Kappa values 0.41-1) was seen between radiologists for all categories except skull base, calvarial, and orbital lesions. Image quality, artifact assessment showed excellent interobserver agreement (>90%) for the scores. All EPIMix images showed reduced motion artifacts. The EPIMix-DWI was comparable to conventional-DWI in terms of quality and artifacts. The remaining sequences showed reduced quality and increased susceptibility. The EPIMix has a significantly reduced acquisition time than conventional MRI and could be used instead of conventional MRI in situations demanding faster scans such as suspected acute ischemic or hemorrhagic stroke. In other clinical scenarios, it could help tailor the MRI examination for each patient.

Detection of early pulmonary emphysema by multi-contrast x-ray Talbot-Lau interferometer.

Pulmonary emphysema is a part of chronic obstructive pulmonary disease, which is an irreversible chronic respiratory disease. In order to avoid further damage to lung tissue, early diagnosis and treatment of pulmonary emphysema is essential. Early pulmonary emphysema diagnosis is difficult with conventional radiographic imaging. Recently, x-ray phase contrast imaging has proved to be an effective and promising imaging strategy for soft tissue, due to its high sensitivity and multi-contrast. The aim of this study is to diagnose pulmonary emphysema early utilizing an x-ray Talbot-Lau interferometer (TLI). We successfully established the mouse model of emphysema by porcine pancreatic elastase treatment, and then used the established x-ray TLI to perform imaging experiments on the mice with different treatment time. The traditional absorption CT and phase contrast CT were obtained simultaneously through TLI. The CT results and histopathology of mice lung in different treatment time were quantitatively analyzed. By imaging mice lungs, it can be found that phase contrast has higher sensitivity than absorption contrast in early pulmonary emphysema. The results show that the phase contrast signal could distinguish the pulmonary emphysema earlier than the conventional attenuation signal, which can be consistent with histological images. Through the quantitative analysis of pathological section and phase contrast CT, it can be found that there is a strong linear correlation. In this study, we quantitatively analyze mean linear intercept of histological sections and CT values of mice. The results show that the phase contrast signal has higher imaging sensitivity than the attenuation signal. X-ray TLI multi-contrast imaging is proved as a potential diagnostic method for early pulmonary emphysema in mice.

A four-grating interferometer for x-ray multi-contrast imaging.

X-ray multi-contrast imaging with gratings provides a practical method to detect differential phase and dark-field contrast images in addition to the x-ray absorption image traditionally obtained in laboratory or hospital environments. Systems have been developed for preclinical applications in areas including breast imaging, lung imaging, rheumatoid arthritis hand imaging and kidney stone imaging. Prevailing x-ray interferometers for multi-contrast imaging include Talbot-Lau interferometers and universal moiré effect-based phase-grating interferometers. Talbot-Lau interferometers suffer from conflict between high interferometer sensitivity and large field of view (FOV) of the object being imaged. A small period analyzer grating is necessary to simultaneously achieve high sensitivity and large FOV within a compact imaging system but is technically challenging to produce for high x-ray energies. Phase-grating interferometers suffer from an intrinsic fringe period ranging from a few micrometers to several hundred micrometers that can hardly be resolved by large area flat panel x-ray detectors. The purpose of this work is to introduce a four-grating x-ray interferometer that simultaneously allows high sensitivity and large FOV, without the need for a small period analyzer grating. The four-grating interferometer consists of a source grating placed downstream of and close to the x-ray source, a pair of phase gratings separated by a fixed distance placed downstream of the source grating, and an analyzer grating placed upstream of and close to the x-ray detector. The object to be imaged is placed upstream of and close to the phase-grating pair. The distance between the source grating and the phase-grating pair is designed to be far larger than that between the phase-grating pair and the analyzer grating to promote simultaneously high sensitivity and large FOV. The method was evaluated by constructing a four-grating interferometer with an 8µm period source grating, a pair of phase gratings of 2.4µm period, and an 8µm period analyzer grating. The fringe visibility of the four-grating interferometer was measured to be ≈24% at 40kV and ≈18% at 50kV x-ray tube operating voltage. A quartz bead of 6mm diameter was imaged to compare the theoretical and experimental phase contrast signal with good agreement. Kidney stone specimens were imaged to demonstrate the potential of such a system for classification of kidney stones. The proposed four-grating interferometer geometry enables a compact x-ray multi-contrast imaging system with simultaneously high sensitivity and large FOV. Relaxation of the requirement for a small period analyzer grating makes it particularly suitable for high x-ray energy applications such as abdomen and chest imaging.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping.

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI

The hippocampus is a crucial brain structure involved in memory formation, spatial navigation, emotional regulation, and learning. An accurate MRI image segmentation of the human hippocampus plays an important role in multiple neuro-imaging research and clinical practice, such as diagnosing neurological diseases and guiding surgical interventions. While most hippocampus segmentation studies focus on using T1-weighted or T2-weighted MRI scans, we explore the use of diffusion-weighted MRI (dMRI), which offers unique insights into the microstructural properties of the hippocampus. Particularly, we utilize various anisotropy measures derived from diffusion MRI (dMRI), including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, for a multi-contrast deep learning approach to hippocampus segmentation. To exploit the unique benefits offered by various contrasts in dMRI images for accurate hippocampus segmentation, we introduce an innovative multimodal deep learning architecture integrating cross-attention mechanisms. Our proposed framework comprises a multi-head encoder designed to transform each contrast of dMRI images into distinct latent spaces, generating separate image feature maps. Subsequently, we employ a gated cross-attention unit following the encoder, which facilitates the creation of attention maps between every pair of image contrasts. These attention maps serve to enrich the feature maps, thereby enhancing their effectiveness for the segmentation task. In the final stage, a decoder is employed to produce segmentation predictions utilizing the attention-enhanced feature maps. The experimental outcomes demonstrate the efficacy of our framework in hippocampus segmentation and highlight the benefits of using multi-contrast images over single-contrast images in diffusion MRI image segmentation.