NMR in Biomedicine最新文献

Hyperpolarized gas (HPG) magnetic resonance (MR) imaging allows for the quantification of pulmonary defects with the ventilation defect percentage (VDP). Although informative, VDPs lack information regarding the spatial distribution of defects. We developed a method of quantifying the focality/sparseness of ventilation defects in hyperpolarized-gas lung MR images. The study involved a total of 56 subjects: 14 asthmatics (age mean ± sd = 45.1 ± 18.9), 25 COPD subjects (age = 60.6 ± 10.4), and 17 CF subjects (age = 21.8 ± 8.4). The analyzed data are from four different studies: Study 1 used a 3-T gradient echo (GRE) sequence, Study 2 used a 1.5-T GRE sequence, Study 3 used a 1.5-T two-dimensional spiral sequence, and Study 4 used a 1.5-T three-dimensional balanced steady-state free precession (bSSFP) sequence. We developed an algorithm that quantifies the focality/sparseness of ventilation defects as a subject's cluster index (CI). The algorithm was assessed on synthesized spherical defect clusters and 3D lung volume defects of varying sizes/distributions. CI and whole-lung VDP were calculated for asthmatic, COPD, and CF subjects. Pearson correlation coefficients and linear regression models between CI and FEV1pp, as well as CI and VDP, were performed to evaluate CI among asthma, COPD, and CF. T tests were performed to evaluate CI/VDP ratios among previously mentioned lung diseases. p values less than 0.05 were statistically significant. Subject CI well represents defect focality by visual inspection. Pearson correlation coefficients between CI and VDP were r = 0.60 (p = 2.21 × 10^-2) for asthma, r = 0.79 (p = 3.15 × 10^-6) for COPD, and r = 0.84 (p = 2.80 × 10^-5) for CF. Pearson correlation coefficients between CI and FEV1pp was r = -0.47 (p = 0.0002). Analysis of variance (ANOVA) and a Tukey's honestly significant difference (HSD) test revealed that the ratio of whole-lung CI/VDP was significantly different between asthma/CF (p = 0.04) and CF/COPD (p = 0.008), but not among asthma/COPD (p = 0.95). This method of volumetric quantification of defect spatial distribution may provide information regarding defect cluster size in which VDP alone is uninformative.

Gliomas are highly heterogeneous and often include a nonenhancing component that is hyperintense on T₂ weighted MRI. This can often not be distinguished from secondary gliosis and surrounding edema. We hypothesized that the extent of these T₂ hyperintense areas can more accurately be determined on high-quality 7 T MRI scans. We investigated the extension, volume, and complexity (shape) of T₂ hyperintense areas in patients with glioma on high-quality 7 T MRI scans compared to clinical MRI scans. T₂ hyperintense areas of 28 patients were visually compared and manually segmented on 7 T MRI and corresponding clinical (1.5 T/3 T) MRI scans, and the volume and shape markers were calculated and subsequently compared between scans. We showed extension of the T₂ hyperintense areas via the corpus callosum to the opposite hemisphere in four patients on the 7 T scans that was not visible on the clinical scan. Furthermore, we found a significantly larger volume of the T₂ hyperintense areas on the 7 T scans compared with the clinical scans (7 T scans: 28 mL [12.5-59.1]; clinical scans: 11.9 mL [11.8-56.6]; p = 0.01). We also found a higher complexity of the T₂ hyperintense areas on the 7 T scans compared with the clinical scans (convexity, solidity, concavity index and fractal dimension [p < 0.001]). Our study suggests that high-quality 7 T MRI scans may show more detail on the exact extension, size, and complexity of the T₂ hyperintense areas in patients with a glioma. This information could aid in more accurate planning of treatment, such as surgery and radiotherapy.

Hemodynamic measurements such as cerebral blood flow (CBF) and cerebrovascular reactivity (CVR) can provide useful information for the diagnosis and characterization of brain tumors. Previous work showed that arterial spin labeling (ASL) in combination with vasoactive stimulation enabled simultaneous non-invasive evaluation of both parameters, however this approach had not been previously tested in tumors. The aim of this work was to investigate the application of this technique, using a pseudo-continuous ASL (PCASL) sequence combined with breath-holding at 3 T, to measure CBF and CVR in high-grade gliomas and metastatic lesions, and to explore differences across tumoral-peritumoral regions and tumor types. To that end, 27 patients with brain tumor were studied. Baseline CBF and CVR were measured in tumor, edema, and gray matter (GM) volumes-of-interest (VOIs). Peritumoral ipsilateral ring-shaped VOIs were also generated and mirrored to the contralateral hemisphere. Differences in baseline CBF and CVR were evaluated between contralateral and ipsilateral GM, contralateral and ipsilateral peritumoral rings, and among VOIs and tumor types. CBF in the tumor was higher in grade 4 gliomas than metastases. In grade 4 gliomas, edema had lower CBF than the tumor and contralateral GM. CVR values were different between grade 3 and grade 4 gliomas, and between grade 4 and metastases. CVR values in the tumor were lower compared to the contralateral GM. Differences in CVR between contralateral and ipsilateral-ring VOIs were also found in grade 4 gliomas, presumably suggesting tumor infiltration within the peritumoral tissue. A cut-off value for CVR of 27.9%-signal-change is suggested to differentiate between grade 3 and grade 4 gliomas (specificity = 83.3%, sensitivity = 70.6%). In conclusion, CBF and CVR mapping with ASL offered insights into the perilesional environment that could help to detect infiltrative disease, particularly in grade 4 gliomas. CVR emerged as a potential biomarker to differentiate between grade 3 and grade 4 gliomas.

Synthetic magnetic resonance spectra (MRS) are mathematically generated spectra which can be used to investigate the assumptions of data analysis strategies, optimize experimental design, and as training data for the development and validation of machine learning tools. In this work, we extend Magnetic Resonance Spectrum Simulator (MARSS), a popular MRS basis set simulation tool, to be able to generate synthetic spectra for an arbitrary MRS sequence. The extension, referred to as synMARSS, converts a basis set as well as a set of NMR, tissue-related and additional sequence parameters into high-quality synthetic spectra via a parametric model. synMARSS is highly versatile, incorporating T₁ and T₂ relaxation, arbitrary line shape distortions and diffusion, while also quickly generating the large amount of training data needed for machine learning applications. Additionally, we extend MARSS to non-¹H nuclei, such as ²H, ¹³C, and ³¹P. We use synthetic spectra to investigate the effects of approximating ¹⁴N heteronuclear coupling as weak homonuclear coupling, which was found to have small effects on the quantified concentrations for major metabolites for the implementation of PRESS at short echo time, but these effects increased at longer echo times.

Non-¹H nuclei magnetic resonance spectroscopy (MRS) offers insights into metabolism, which may aid for example early stages of disease diagnosis, tissue characterization or therapy response evaluation. Sodium MRI can provide valuable information about tissue health and cellular function. When combined with ³¹P MR spectroscopic imaging (MRSI), complementary metabolic information on energy metabolism and cell proliferation can be obtained. However, sensitivity challenges stemming from low natural abundances and low gyromagnetic ratios of different nuclei have hindered progress. Besides, due to hardware constraints, different nuclei are often studied separately, and the need for dedicated hardware for x-nuclei imaging hampers clinical efficiency and patient-friendly assessments. This work introduces an interleaved acquisition scheme for 3D ³¹P-MRSI and 3D radial ²³Na-MR imaging (²³Na-MRI) at 7 Tesla (7T) and demonstrates the feasibility of interleaving these two nuclei acquisitions. The interleaved protocol effectively merged ³¹P-MRSI with ²³Na-MRI, while remaining within specific absorption rate (SAR) limits. Results revealed comparable signal-to-noise ratios (SNRs) and spectral quality between interleaved and non-interleaved scans, highlighting the approach's efficiency without compromising data quality.

The purpose of this study was to measure T₁ and T₂ relaxation times of NAD⁺ proton resonances in the downfield ¹H MRS spectrum in human brain at 7 T in vivo and to assess the propagation of relaxation time uncertainty in NAD⁺ quantification. Downfield spectra from eight healthy volunteers were acquired at multiple echo times to measure T₂ relaxation times, and saturation recovery data were acquired to measure T₁ relaxation times. The downfield acquisition used a spectrally selective 90° sinc pulse for excitation centered at 9.1 ppm with a bandwidth of 2 ppm, followed by a 180° spatially selective Shinnar-Le Roux refocusing pulse for localization. Uncertainty propagation analysis on metabolite quantification was performed analytically and with Monte Carlo simulation. [NAD⁺] was quantified in five participants. The mean ± standard deviation of T₁ relaxation times of the H2, H6, and H4 NAD⁺ protons were 205.6 ± 25.7, 211.6 ± 33.5, and 237.3 ± 42.4 ms, respectively. The mean ± standard deviation of T₂ relaxation times of the H2, H6, and H4 protons were 33.6 ± 7.4, 29.1 ± 4.7, and 42.3 ± 11.6 ms, respectively. The relative uncertainty in NAD⁺ concentration due to relaxation time uncertainty was 8.4%-11.4%, and measured brain [NAD⁺] (N = 5) was 0.324 ± 0.050 mM. Using downfield spectrally selective spectroscopy with single-slice localization, we found T₁ and T₂ relaxation times averaged across all NAD⁺ resonances to be approximately 218 and 35 ms, respectively, in the human brain in vivo at 7 T.

The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field strengths of 3 and 0.55 T. We trained DL reconstructions using two different training strategies, supervised (SV) and self-supervised (SSV), with retrospectively sixfold undersampled data obtained at 3 T. We then evaluated the DL reconstructions against standard techniques, parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. We also tested DL reconstructions with prospectively accelerated acquisitions and evaluated their robustness when changing fields strengths from 3 to 0.55 T. DL reconstructions demonstrated a reduction in average acquisition time from 599/542 to 255/180 s for MRCP at 3 T/0.55 T. In both retrospective and prospective undersampling, PSNR and SSIM of DL reconstructions were higher than those of PI and CS. At the same time, DL reconstructions preserved the image quality of undersampled data, including sharpness and the visibility of hepatobiliary ducts. In addition, both DL approaches produced high-quality reconstructions at 0.55 T. In summary, DL reconstructions trained for highly accelerated MRCP enabled a reduction in acquisition time by a factor of 2.4/3.0 at 3 T/0.55 T while maintaining the image quality of conventional acquisitions.

Deuterium (²H) and phosphorus (³¹P) magnetic resonance spectroscopy (MRS) are complementary methods for evaluating tissue metabolism noninvasively in vivo. Combined ²H and ³¹P MRS would therefore be of interest for various applications, from cancer to diabetes. Loop coils are commonly used in X-nuclei studies in the human body for both transmit and receive. However, loop coils suffer from limited penetration depth and inhomogeneous B₁ ⁺ field. The purpose of this work is to develop a double tuned ²H/³¹P whole-body birdcage transmit coil for 7 T for ²H and ³¹P MRS imaging (MRSI) with homogeneous excitation over a large field-of-view. The performance of the ²H/³¹P birdcage coil was assessed on B₁ ⁺ fields over a body-sized phantom at ²H and ³¹P frequencies using an 8-channel ²H/³¹P receive array. Using two elements of the ²H/³¹P receive array, natural abundance ²H and ³¹P 3D MRSI data at rest were acquired consecutively in the brain and lower leg muscles. Additionally, ²H and ³¹P 3D MRSI data were acquired from one volunteer 90 min after [6,6'-²H₂]-glucose intake, using 8-channel ²H/³¹P receive array around the abdomen. The B₁ ⁺ variation of the whole-body birdcage coil over the phantom was 12.1% for ²H and 19.2% for ³¹P. High-quality ²H and ³¹P 3D MRSI data were acquired from the brain and the lower leg. Whole liver coverage was achieved in both ²H and ³¹P 3D MRSI data. The developed ²H/³¹P whole-body birdcage transmit coil allows simultaneous 3D mapping of glucose and energy metabolism and membrane turnover throughout the human body.

The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve problems, like strokes, brain tumors, multiple sclerosis, and dementia, brain magnetic resonance imaging (MRI) is normally utilized. Identifying the tumor on early stage can improve the patient's survival rate. However, it is difficult to identify the exact tumor region with less computational complexity. Also, the tumors can vary significantly in shape, size, and appearance, which complicates the task of correctly classifying tumor types and detecting subtle pixel changes over time. Hence, an Adam kookaburra optimization-based Shepard convolutional neural network (AKO-based Shepard CNN) is established in this study for the classification and pixel change detection of brain tumor. The Adam kookaburra optimization (AKO) is established by integrating the kookaburra optimization algorithm (KOA) and Adam. Here, the pre- and post-operative MRIs are pre-processed and then segmented by U-Net++. The tuning of U-Net++ is done by the bald Border collie firefly optimization algorithm (BBCFO). The bald eagle search (BES), firefly algorithm (FA), and Border collie optimization (BCO) are combined to form the BBCFO. The next operation is the feature extraction and the classification is conducted at last using ShCNN. The AKO is utilized to tune the ShCNN for obtaining effective classification results. Unlike conventional optimization algorithms, AKO offers faster convergence and higher accuracy in classification. The highest negative predictive value (NPV), true negative rate (TNR), true positive rate (TPR), positive predictive value (PPV), and accuracy produced by the AKO-based ShCNN are 89.91%, 92.26%, 93.78%, and 93.60%, respectively, using Brain Images of Tumors for Evaluation database (BITE).

Ultra-high field (7T) MRI allows scans at sub-millimetre resolution with exquisite signal-to-noise ratio (SNR). As 7T MRI becomes more widely used clinically, the challenge of patient motion must be overcome. Retrospective motion correction is used successfully for some protocols, but for acquisitions such as slice-by-slice scans only prospective motion correction can deliver the full potential of 7T MRI. We report the first implementation of prospective 3D Fat Navigator ("FatNav") motion correction for the Siemens 7T Terra MRI. We implemented a modular Sequence Building Block for FatNav and embedded it into the vendor's gradient-recalled echo (GRE) sequence. We modified the reconstruction pipeline to reconstruct FatNav images online, coregistering them and sending motion updates to the host sequence online. We tested five registration algorithms for performance and accuracy on synthetic FatNav data. We implemented the best three of these in our sequence and tested them online. We acquired T₁ and T₂* weighted brain images of healthy volunteers correcting every other image for motion to visualise the effectiveness of online motion correction. Data were acquired with and without head immobilisation. We also tested performance while correcting every measurement for motion. Our implementation uses a 1.23 s 3D FatNav acquisition module and delivers motion updates in less than 3 s, which is sufficient for motion updates every few k-space lines in typical scans. Corrected images are crisper with fewer visible motion artefacts. This improved sharpness is reflected quantitatively by an increase in the variance of the image Laplacian which is 1.59 x better for corrected vs uncorrected images. Profiles across the cerebral falx are 33% steeper for corrected vs uncorrected images. Prospective FatNav improves GRE image quality in the brain. Our modular Sequence Building Block provides a simple method to integrate motion correction in 7T MRI pulse sequences.