Spherical harmonics-based deep learning achieves generalized and accurate diffusion tensor imaging.

Diffusion tensor imaging (DTI) is a prevalent magnetic resonance imaging (MRI) technique, widely used in clinical and neuroscience research. However, the reliability of DTI is affected by the low signal-to-noise ratio inherent in diffusion-weighted (DW) images. Deep learning (DL) has shown promise in improving the quality of DTI, but its limited generalization to variable acquisition schemes hinders practical applications. This study aims to develop a generalized, accurate, and efficient DL-based DTI method. By leveraging the representation of voxel-wise diffusion MRI (dMRI) signals on the sphere using spherical harmonics (SH), we propose a novel approach that utilizes SH coefficient maps as input to a network for predicting the diffusion tensor (DT) field, enabling improved generalization. Extensive experiments were conducted on simulated and in-vivo datasets, covering various DTI application scenarios. The results demonstrate that the proposed SH-DTI method achieves advanced performance in both quantitative and qualitative analyses of DTI. Moreover, it exhibits remarkable generalization capabilities across different acquisition schemes, centers, and scanners, ensuring its broad applicability in diverse settings.

Z-Spectral MRI Quantifies the Mass and Metabolic Activity of Adipose Tissues With Fat-Water-Fraction and Amide-Proton-Transfer Contrasts.

BACKGROUND: Brown adipose tissue (BAT) is metabolically activatable and plays an important role in obesity and metabolic diseases. With reduced fat-water-fraction (FWF) compared with white adipose tissue (WAT), BAT mass and its functional activation may be quantified with Z-spectra MRI, with built-in FWF and the metabolic amide proton transfer (APT) contrasts. PURPOSE: To investigate if Z-spectral MRI can quantify the mass and metabolic activity of adipose tissues. STUDY TYPE: Prospective. SUBJECTS: Seven groups of 8-week-old male rats, including two groups (n = 7 per group) for in vivo MRI study and five groups (n = 5 per group) for ex vivo validation; 12 young and healthy volunteers with 6 male and 6 female. FIELD STRENGTH/SEQUENCE: The 7 T small animal and 3 T clinical systems, T2-weighted imaging, Rapid Acquisition with Relaxation Enhancement (RARE) readout based chemical exchange saturation transfer (CEST) Z-spectral MRI sequence. ASSESSMENT: Quantified FWF and APT from Z-spectra in rats before and after norepinephrine (NE) stimulation and in healthy human subjects; ex vivo measurements of total proteins in BAT from rats. STATISTICAL TESTS: Two-tailed unpaired Student's t-tests and repeated measures ANOVA. P-value <0.05 was considered significant. RESULTS: Decreased FWF (from 39.6% ± 7.2% before NE injection to 16.4% ± 7.2% 120 minutes after NE injection, P < 0.0001) and elevated APT (from 1.1% ± 0.5% before NE injection to 2.9% ± 0.5% 120 minutes after NE injection, P < 0.0001) signals in BAT were observed with in vivo Z-spectral MRI in rats injected with NE at 7 T MRI. At clinical 3 T, Z-spectral MRI was used to quantify the FWF (58.5% ± 7.2% in BAT and 73.7% ± 6.5% in WAT with P < 0.0001) and APT (2.6% ± 0.8% in BAT and 0.9% ± 0.3% in WAT with P < 0.0001) signals in healthy volunteers. APT signals of BAT were negatively correlated with the BMI in humans (r = 0.71). DATA CONCLUSION: Endogenous Z-spectral MRI was demonstrated to simultaneously quantify BAT mass and function based on its FWF and APT contrasts. TECHNICAL EFFICACY STAGE: 1.

Non-complete recovery of temporal lobe white matter diffusion metrics at one year Post-Radiotherapy: Implications for Radiation-Induced necrosis risk.

BACKGROUND: Temporal lobe (TL) white matter (WM) injuries are often seen early after radiotherapy (RT) in nasopharyngeal carcinoma patients (NPCs), which fail to fully recover in later stages, exhibiting a "non-complete recovery pattern". Herein, we explored the correlation between non-complete recovery WM injuries and TL necrosis (TLN), identifying dosimetric predictors for TLN-related high-risk WM injuries. METHODS: We longitudinally examined 161 NPCs and 19 healthy controls employing multi-shell diffusion MRI. Automated fiber-tract quantification quantified diffusion metrics within TL WM tract segments. ANOVA identified non-complete recovery WM tract segments one-year post-RT. Cox regression models discerned TLN risk factors utilizing non-complete recovery diffusion metrics. Normal tissue complication probability (NTCP) models and dose-response analysis further scrutinized RT-related toxicity to high-risk WM tract segments. RESULTS: Seven TL WM tract segments exhibited a "non-complete recovery pattern". Cox regression analysis identified mean diffusivity of the left uncinate fasciculus segment 1, neurite density index (NDI) of the left cingulum hippocampus segment 1, and NDI of the right inferior longitudinal fasciculus segment 1 as TLN risk predictors (hazard ratios [HRs] with confidence interval [CIs]: 1.45 [1.17-1.81], 1.07 [1.00-1.15], and 1.15 [1.03-1.30], respectively; all P-values < 0.05). In NTCP models, D10cc.L, D20cc.L and D10cc.R demonstrated superior performance, with TD50 of 37.22 Gy, 24.96 Gy and 37.28 Gy, respectively. CONCLUSIONS: Our findings underscore the significance of the "non-complete recovery pattern" in TL WM tract segment injuries during TLN development. Understanding TLN-related high-risk WM tract segments and their tolerance doses could facilitate early intervention in TLN and improve RT protocols.

Non-invasive mapping of brown adipose tissue activity with magnetic resonance imaging.

Thermogenic brown adipose tissue (BAT) has a positive impact on whole-body metabolism. However, in vivo mapping of BAT activity typically relies on techniques involving ionizing radiation, such as [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) and computed tomography (CT). Here we report a noninvasive metabolic magnetic resonance imaging (MRI) approach based on creatine chemical exchange saturation transfer (Cr-CEST) contrast to assess in vivo BAT activity in rodents and humans. In male rats, a single dose of the ß3-adrenoceptor agonist (CL 316,243) or norepinephrine, as well as cold exposure, triggered a robust elevation of the Cr-CEST MRI signal, which was consistent with the [18F]FDG PET and CT data and 1H nuclear magnetic resonance measurements of creatine concentration in BAT. We further show that Cr-CEST MRI detects cold-stimulated BAT activation in humans (both males and females) using a 3T clinical scanner, with data-matching results from [18F]FDG PET and CT measurements. This study establishes Cr-CEST MRI as a promising noninvasive and radiation-free approach for in vivo mapping of BAT activity.

Model-based quantitative photoacoustic tomography with directional total variation.

In photoacoustic tomography (PAT), acoustic inversion aims to recover the spatial distribution of light energy deposition within the imaging object from the signals captured by detectors. To achieve quantitative imaging, optical inversion is further employed to derive absorption coefficient (AC) images. However, limitations such as restricted detection angles and inherent noise lead to substantial artifacts and degradation in the quality of PAT images, consequently affecting the accuracy of optical inversion results. In this study, we propose a directional total variation constrained optical inversion model to reconstruct the AC image. By incorporating anatomy prior information into the optical inversion process, our method can effectively suppress artifacts in AC images while maintaining structural integrity. Simulation, phantom, and in vivo experimental results demonstrate that our method significantly improves the reconstructed AC image quality. Our method provides a reliable foundation for achieving high-quality quantitative PAT imaging.

Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.

PURPOSE: To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. METHODS: Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. A support vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. RESULTS: Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). CONCLUSION: Radiomics analysis of quantitative maps from synthetic MRI provides a new quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.

Microstructural changes of the white matter in systemic lupus erythematosus patients without neuropsychiatric symptoms: a multi-shell diffusion imaging study.

BACKGROUND: Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) provide more comprehensive and informative perspective on microstructural alterations of cerebral white matter (WM) than single-shell diffusion tensor imaging (DTI), especially in the detection of crossing fiber. However, studies on systemic lupus erythematosus patients without neuropsychiatric symptoms (non-NPSLE patients) using multi-shell diffusion imaging remain scarce. METHODS: Totally 49 non-NPSLE patients and 41 age-, sex-, and education-matched healthy controls underwent multi-shell diffusion magnetic resonance imaging. Totally 10 diffusion metrics based on DKI (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis and radial kurtosis) and NODDI (neurite density index, orientation dispersion index and volume fraction of the isotropic diffusion compartment) were evaluated. Tract-based spatial statistics (TBSS) and atlas-based region-of-interest (ROI) analyses were performed to determine group differences in brain WM microstructure. The associations of multi-shell diffusion metrics with clinical indicators were determined for further investigation. RESULTS: TBSS analysis revealed reduced FA, AD and RK and increased ODI in the WM of non-NPSLE patients (P < 0.05, family-wise error corrected), and ODI showed the best discriminative ability. Atlas-based ROI analysis found increased ODI values in anterior thalamic radiation (ATR), inferior frontal-occipital fasciculus (IFOF), forceps major (F_major), forceps minor (F_minor) and uncinate fasciculus (UF) in non-NPSLE patients, and the right ATR showed the best discriminative ability. ODI in the F_major was positively correlated to C3. CONCLUSION: This study suggested that DKI and NODDI metrics can complementarily detect WM abnormalities in non-NPSLE patients and revealed ODI as a more sensitive and specific biomarker than DKI, guiding further understanding of the pathophysiological mechanism of normal-appearing WM injury in SLE.

Facile Synthesis of Ag-Doped Urchin-like MnO2 on Carbon Cloth for Supercapacitors.

Based on MnO2/carbon cloth (CC) composite materials, an Ag-doped MnO2 nanowire, self-assembled, urchin-like structure was synthesized in situ on the surface of CC using a simple method, and a novel and efficient flexible electrode material for supercapacitors was developed. The morphology, structure, elemental distribution, and pore distribution of the material were analyzed using SEM, TEM, XRD, XPS, and BET. The electrochemical performance was tested using cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD). In the three-electrode system, GCD testing showed that the specific capacitance of the material reached 520.8 F/g at 0.5 A/g. After 2000 cycles at a current density of 1 A/g, the capacitance retention rate was 90.6%, demonstrating its enormous potential in the application of supercapacitor electrode materials.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings.

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice. Thus, deep learning methods applicable to MPM with varying acquisition settings are highly required but still rarely investigated. In this work, we develop a model-based deep network termed MMPM-Net for robust MPM with varying acquisition settings. A deep learning-based denoiser is introduced to construct the regularization term in the nonlinear inversion problem of MPM. The alternating direction method of multipliers is used to solve the optimization problem and then unrolled to construct MMPM-Net. The variation in acquisition parameters can be addressed by the data fidelity component in MMPM-Net. Extensive experiments are performed on R2 mapping and R1 mapping datasets with substantial variations in acquisition settings, and the results demonstrate that the proposed MMPM-Net method outperforms other state-of-the-art MR parameter mapping methods both qualitatively and quantitatively.

A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

PURPOSE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver. METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots. RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets. CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

Aberrant resting-state co-activation network dynamics in major depressive disorder.

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.

BACKGROUND: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. METHODS: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. RESULTS: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). CONCLUSIONS: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

Disparate Radiation-Induced Microstructural Injuries in Whole-Brain White Matter of Patients With Nasopharyngeal Carcinoma: A Longitudinal Study Using Multishell Diffusion MRI.

BACKGROUND: Evidence for prevention strategies of radiotherapy (RT)-related injury in patients with nasopharyngeal carcinoma (NPC) was lacking. Understanding the dynamic alterations in the cerebral white matter (WM) microstructure after RT may be helpful. PURPOSE: To investigate the dynamic alterations in the whole brain WM microstructure in patients with NPC in the 12 months after RT using multishell diffusion MRI (MS-dMRI). STUDY TYPE: Single-center longitudinal study. POPULATION: A total of 28 treatment-naïve patients with pathologically confirmed NPC (age: 39.68 ± 8.93 years, 11 female) and 20 healthy controls (age: 40.65 ± 9.76 years, 7 female). FIELD STRENGTH/SEQUENCES: A 3 T, MS-dMRI using a single-shot echo planar imaging sequence. ASSESSMENT: MS-dMRI was acquired at baseline for the NPC patients and healthy controls, at 0-3 (acute, AC), 6 (early delayed, ED) and 12 months (late delayed, LD) after RT for the NPC patients. The mean and maximum radiation doses to the temporal lobe were acquired. The quality of images was reviewed. MS-dMRI was analyzed using tract-based spatial statistics (TBSS). The presentations of injury were defined by the findings of TBSS. STATISTICAL TESTS: Chi-square, t tests, repeated ANOVA, and Spearman-rank correlation analysis were used. P < 0.05 was considered to be statistically significant. RESULTS: TBSS showed two WM injuries (injuries 1 and 2). Injury 1 emerged in the ED phase in the bilateral temporal poles and persisted throughout the ED and LD phases. Injury 2 developed from the AC to ED phase in the bilateral hemisphere and partially recovered in the LD phase. In the ED and LD phases, the multiple diffusion metrics were well correlated (r > 0.5 or <-0.5) with the RT dose, especially in the WM tracts in the temporal lobes. DATA CONCLUSION: Disparate WM injuries were observed in NPC patients after RT. The injuries may be primarily or secondarily induced by radiation. Injury 1 may be irreversible, while injury 2 seems to partially recover. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 4.

Evaluation of gadolinium deposition in cortical bone using three-dimensional ultrashort echo time quantitative susceptibility mapping: A preliminary study.

The aim of the current study was to investigate the feasibility of three-dimensional ultrashort echo time quantitative susceptibility mapping (3D UTE-QSM) for the assessment of gadolinium (Gd) deposition in cortical bone. To this end, 40 tibial bovine cortical bone specimens were divided into five groups then soaked in phosphate-buffered saline (PBS) solutions with five different Gd concentrations of 0, 0.4, 0.8, 1.2, and 1.6 mmol/L for 48 h. Additionally, eight rabbits were randomly allocated into three groups, consisting of a normal-dose macrocyclic gadolinium-based contrast agent (GBCA) group (n = 3), a high-dose macrocyclic GBCA group (n = 3), and a control group (n = 2). All bovine and rabbit tibial bone samples underwent magnetic resonance imaging (MRI) on a 3-T clinical MR system. A 3D UTE-Cones sequence was utilized to acquire images with five different echo times (i.e., 0.032, 0.2, 0.4, 0.8, and 1.2 ms). The UTE images were subsequently processed with the morphology-enabled dipole inversion algorithm to yield a susceptibility map. The average susceptibility was calculated in three regions of interest in the middle of each specimen, and the Pearson's correlation between the estimated susceptibility and Gd concentration was calculated. The bone samples soaked in PBS with higher Gd concentrations exhibited elevated susceptibility values. A mean susceptibility value of -2.47 ± 0.23 ppm was observed for bovine bone soaked in regular PBS, while the mean QSM value increased to -1.75 ± 0.24 ppm for bone soaked in PBS with the highest Gd concentration of 1.6 mmol/L. A strong positive correlation was observed between Gd concentrations and QSM values. The mean susceptibility values of rabbit tibial specimens in the control group, normal-dose GBCA group, and high-dose GBCA group were -4.11 ± 1.52, -3.85 ± 1.33, and -3.39 ± 1.35 ppm, respectively. In conclusion, a significant linear correlation between Gd in cortical bone and QSM values was observed. The preliminary results suggest that 3D UTE-QSM may provide sensitive noninvasive assessment of Gd deposition in cortical bone.

Assessment of rhabdomyolysis-induced acute kidney injury with chemical exchange saturation transfer magnetic resonance imaging.

Background: Rhabdomyolysis (RM)-induced acute kidney injury (AKI) is a common renal disease with low survival rate and inadequate prognosis. In this study, we investigate the feasibility of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) for assessing the progression of RM-induced AKI in a mouse model. Methods: AKI was induced in C57BL/6J mice via intramuscular injection of 7.5 mL/kg glycerol (n=30). Subsequently, serum creatinine (SCr), blood urea nitrogen (BUN), and hematoxylin-eosin (HE) and Masson staining, were performed. Longitudinal CEST-MRI was conducted on days 1, 3, 7, 15, and 30 after AKI induction using a 7.0-T MRI system. CEST-MRI quantification parameters including magnetization transfer ratio (MTR), MTR asymmetric analysis (MTRasym), apparent amide proton transfer (APT*), and apparent relayed nuclear Overhauser effect (rNOE*) were used to investigate the feasibility of detecting RM-induced renal damage. Results: Significant increases of SCr and BUN demonstrated established AKI. The HE staining revealed various degrees of tubular damage, and Masson staining indicted an increase in the degree of fibrosis in the injured kidneys. Among CEST parameters, the cortical MTR presented a significant difference, and it also showed the best diagnostic performance for AKI [area under the receiver operating characteristic curve (AUC) =0.915] and moderate negative correlations with SCr and BUN. On the first day of renal damage, MTR was significantly reduced in cortex (22.7%±0.04%, P=0.013), outer stripe of outer medulla (24.7%±1.6%, P<0.001), and inner stripe of outer medulla (27.0%±1.5%, P<0.001) compared to the control group. Longitudinally, MTR increased steadily with AKI progression. Conclusions: The MTR obtained from CEST-MRI is sensitive to the pathological changes in RM-induced AKI, indicating its potential clinical utility for the assessment of kidney diseases.

Analysis of the suitable thawing endpoint of the frozen chicken breast using video recording analysis, shear force, and bioelectrical impedance measurement.

This study focuses on analyzing the texture properties and bioelectrical impedance characteristics of frozen chicken breasts during low-temperature thawing, meanwhile, we also compared the differences in physiochemical properties. Frozen chicken breasts were thawed at 4 ± 2°C for 2, 4, 6, 8, and 10 h separately, then the physiochemical properties (color, pH, water-holding capacity, water distribution), the texture properties (easy-to-cut level), and the bioelectrical impedance were determined and analyzed. The easy-to-cut level of the samples was evaluated by the sensory panel and two indexes, one is Warner-Bratzler shear force measured by texture analysis machine, and the other is cutting speed value calculated by the consumer-oriented cutting behavior analysis using frame-by-frame video recording analysis method. These two methods were used to characterize the easy-to-cut level of the frozen samples during thawing from the industrial processing and home cooking standpoint. Strong correlations were observed between the easy-to-cut level and the bioelectrical impedance of the frozen chicken breasts during thawing. The impedance magnitude at 100 kHz showed a high correlation coefficient (R2 = .9417) with Warner-Bratzler shear force, and the impedance magnitude at 50 Hz showed a high correlation coefficient (R2 = .8658) with cutting speed. Our results indicated the acceptability of using bioelectrical impedance to evaluate the easy-to-cut thawing endpoint for both industry processing and home cooking.

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability. PURPOSE: To develop a deep learning-based method for automatic septum segmentation from black-blood MR images for the myocardial T2* measurement of thalassemia patients. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: One hundred forty-six transfusion-dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, multiecho gradient-echo sequence. ASSESSMENT: A modified attention U-Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods. STATISTICAL TESTS: Agreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland-Altman plot and the coefficient of variation (CoV). RESULTS: The median Dice coefficient of deep network-based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: -0.74 to 0.79) msec, 0.43 (95% LoA: -2.1 to 3.0) msec, and 0.36 (95% LoA: -0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively. DATA CONCLUSIONS: The proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator-dependent variability in practice. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

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.

Dual-targeting nanotheranostics for MRI-guided enhanced chemodynamic therapy of hepatoma via regulating the tumor microenvironment.

Chemodynamic therapy (CDT), as a reactive oxygen species (ROS)-based therapeutic modality, has attracted much attention in recent years. However, the insufficient therapeutic effect of CDT is due to the antioxidant system in the tumor microenvironment, such as high levels of glutathione (GSH). In this study, we developed a biological/physical dual-targeting nanotheranostic agent (relaxation rate, r1: 6.3 mM-1 s-1 and r2: 13.11 mM-1 s-1) for enhanced CDT of SMCC-7721 tumors. This nanotheranostic agent is composed of a homologous tumor cell membrane (TCM), magnetic ferric oxide, and manganese oxide and is denoted as FM@TCM nanoparticles (NPs). A favorable effect of in vitro CDT on SMCC-7721 cells (IC50: 20 µg mL-1) is demonstrated, attributed to the Fenton reaction and oxidative stress resulting from the reduction of the GSH level. In vivo T1/T2 magnetic resonance imaging (MRI) confirms that the tumor accumulation of FM@TCM NPs is promoted by concurrent bioactive targeting of the homologous TCM and physico-magnetic targeting of tumor tissues with an external magnetic field. Impressive chemodynamic therapeutic effects on SMCC-7721 tumors are demonstrated through the catalysis of endogenous hydrogen peroxide and depletion of GSH to generate high levels of ROS. Dual-targeting FM@TCM NPs inhibit SMCC-7721 tumor growth (∼90.9%) in vivo without any biotoxicity. This nanotheranostic agent has great potential for use in MRI-guided CDT.

Multi-Parametric MRI for Evaluating Variations in Renal Structure, Function, and Endogenous Metabolites in an Animal Model With Acute Kidney Injury Induced by Ischemia Reperfusion.

BACKGROUND: Ischemia reperfusion injury (IRI)-induced acute kidney injury (AKI) may occur after renal ischemic injury. There is a lack of an accurate and comprehensive detection technique for IRI-AKI. PURPOSE: To longitudinally evaluate IRI-AKI in rats by renal structure, function, and metabolites using multi-parametric MRI (mpMRI). STUDY TYPE: Prospective. ANIMAL MODEL: Forty-eight rats undergoing IRI-AKI. FIELD STRENGTH/SEQUENCE: 7-T, T1 mapping, and arterial spin labeling (ASL): echo planar imaging (EPI) sequence; blood oxygen level-dependent (BOLD): gradient recalled echo (GRE) sequence; T2 mapping, quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST): rapid acquisition with relaxation enhancement (RARE) sequence. ASSESSMENT: The mpMRI for IRI-AKI was conducted at 0 (control), 1, 3, 7, 14, and 28 days, all included eight rats. The longitudinal mpMRI signal of manually outlined cortex, outer stripe of the outer medulla (OSOM), inner stripe of the outer medulla, and medulla plus pelvis were calculated and compared, their diagnosis performance for IRI-AKI also been evaluated. STATISTICAL TESTS: Pearson correlations analysis for correlation between mpMRI signal and renal injury, unpaired t-tests for comparing the signal changes, and receiver operating characteristics (ROC) analysis was used to identify most sensitive indicator of mpMRI. A P-value <0.05 was considered statistically significant. RESULTS: Compared with control kidneys, the T1 and T2 values of the cortex and medulla in IRI kidneys increased and reached their highest values on day 14, and the kidneys also showed the most severe edema and segments blurred. The RBF in the cortex and OSOM showed a significant decline after day 3. The BOLD signal in the OSOM largest increased on day 28. The cortical PSR and the amine-CEST both decreased with IRI-AKI progression, and amine-CEST achieved the highest AUC for the diagnosis (0.899). DATA CONCLUSION: Multi-parametric MRI may show comprehensive variations in IRI-AKI, and amine-CEST may exhibit the highest accuracy for diagnosis of IRI-AKI. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.