Magnetic resonance imaging最新文献

Objective: To investigate whether radiomic features obtained from the intratumoral and peritumoral regions of pretreatment magnetic resonance imaging (MRI) can predict progression in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC) in comparison with the previously determined clinical score.

Methods: This single-center retrospective study evaluated 224 women with TNBC who underwent NAC between 2010 and 2019. Women were randomly allocated to the training set (n = 169) for model development and the test set (n = 55) for model validation. The clinical score consisted of the histologic type, Ki-67 index, and degree of edema on T2-weighted imaging. Intratumoral and peritumoral radiomic features were extracted from T2-weighted images and the first- and last-phase images of dynamic contrast-enhanced MRI. The radiomics model was built using only radiomic features, whereas the combined model incorporated the clinical score along with radiomic features. The area under the receiver operating characteristic curve (AUC) was used to assess performance.

Results: Progression occurred in 18 and five patients in the training and test sets, respectively. The radiomics model selected three radiomic features (two peritumoral and one intratumoral), while the combined model selected the clinical score and five radiomic features (four peritumoral and one intratumoral). Among the total radiomic features, Inverse Difference Normalized of the peritumoral region of the T2-weighted images, reflective of peritumoral heterogeneity, demonstrated the highest level of association with tumor progression. In the test set, the AUC values of the radiomics-only model, the combined model, and the clinical score were 0.592, 0.764, and 0.720, respectively. Compared to the clinical score, the radiomics-only model (0.720 vs. 0.592, p = 0.468) and the combined model (0.720 vs. 0.764, p = 0.553) did not show superior performance.

Conclusion: The radiomics features were not superior in predicting the progression of TNBC compared to the clinical score, although the peritumoral heterogeneity on T2-weighted images showed a potential.

Purpose: Diffusion-weighted imaging (DWI) has significant value in clinical application, which however suffers from a serious low signal-to-noise ratio (SNR) problem, especially at high spatial resolution and/or high diffusion sensitivity factor.

Methods: Here, we propose a denoising method for magnitude DWI. The method consists of two modules: pre-denoising and post-filtering, the former mines the diffusion redundancy by local kernel principal component analysis, and the latter fully mines the non-local self-similarity using patch-based non-local mean.

Results: Validated by simulation and in vivo datasets, the experiment results show that the proposed method is capable of improving the SNR of the whole brain, thus enhancing the performance for diffusion metrics estimation, crossing fiber discrimination, and human brain fiber tractography tracking compared with the different three state-of-the-art comparison methods. More importantly, the proposed method consistently exhibits superior performance to comparison methods when used for denoising diffusion data acquired with sensitivity encoding (SENSE).

Conclusion: The proposed denoising method is expected to show significant practicability in acquiring high-quality whole-brain diffusion data, which is crucial for many neuroscience studies.

Purpose: To evaluate the effectiveness of fat suppression techniques experimentally and illustrate their influence on the accuracy of PRFS MR-thermometry.

Methods: The residual magnitudes of the main fat peaks are measured using a water-fat decomposition algorithm in an oil phantom and in vivo in swine bone marrow, either with spectral fat saturation (FS), water excitation (WE) or fast water excitation (FWE), as implemented on 1.5 T whole-body clinical MRIs. Thermometry experiments in tissue-mimicking oil-water phantoms (10 and 30 % fat) allow determining temperature errors in PRFS MR-thermometry with no fat suppression, FS and WE, compared against reference fiber optic thermometry.

Results: WE attenuates the signal of the main methylene fat peak more than FS (2 % and 22 % amplitude attenuation in the oil phantom, respectively), while the olefinic and glycerol peaks surrounding the water peak remain unaltered with both FS and WE. Within the 37 °C to 60 °C temperature range explored, FS and WE strongly attenuate temperature errors compared to PRFS without fat suppression. The residual fat signal after FS and WE leads to errors in PRFS thermometry, that increase with the fat content and oscillate with TE and temperature. In our tests limited to a single MR provider, fat suppression with WE appears to suppress fat signal more effectively.

Conclusions: We propose a protocol to quantify the remaining fraction of each spectral fat peak after fat suppression. In PRFS thermometry, despite spectral fat suppression, the remnant fat signal leads to temperature underestimation or overestimation depending on TE, fat fraction and temperature range. Fat suppression techniques should be evaluated specifically for quantitative MRI methods such as PRFS thermometry.

Brain tumor growth is associated with angiogenesis, wherein the density of newly developed blood vessels indicates tumor progression and correlates with the tumor grade. Quantitative dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has shown potential in brain tumor grading and treatment response assessment. Segmentation of large-blood-vessels is crucial for automatic and accurate tumor grading using quantitative DCE-MRI. Traditional manual and semi-manual rule-based large-blood-vessel segmentation methods are time-intensive and prone to errors. This study proposes a novel deep learning-based technique for automatic large-blood-vessel segmentation using Swin UNETR architectures and comparing it with U-Net and Attention U-Net architectures. The study employed MRI data from 187 brain tumor patients, with training, validation, and testing datasets sourced from two centers, two vendors, and two field-strength magnetic resonance scanners. To test the generalizability of the developed model, testing was also carried out on different brain tumor types, including lymphoma and metastasis. Performance evaluation demonstrated that Swin UNETR outperformed other models in segmenting large-blood-vessel regions (achieving Dice scores of 0.979, and 0.973 on training and validation sets, respectively, with test set performance ranging from 0.835 to 0.982). Moreover, most quantitative parameters showed significant differences (p < 0.05) between with and without large-blood-vessel. After large-blood-vessel removal, using both ground truth and predicted masks, the values of parameters in non-vascular tumoral regions were statistically similar (p > 0.05). The proposed approach has potential applications in improving the accuracy of automatic grading of tumors as well as in treatment planning.

Purpose: To evaluate the association between preoperative breast MRI with surgery type, contralateral cancer, recurrence-free (RFS) and overall survival (OS) in women with early-stage breast cancer.

Materials and methods: In this dual-affiliated single institution, retrospective study, we identified women with Stage I-III breast cancer diagnosed between 03/01/2013-03/31/2016 with available follow-up. Patient and tumor characteristics were recorded. Two cohorts were created based on the use of preoperative MRI(PMRI) versus no preoperative MRI(no-MRI) with Wilcoxon signed-rank and χ2 tests utilized for cross-group comparisons. Kaplan-Meier, log-rank and cox proportional hazards model analysis were used to compare RFS and OS in women with and without MRI.

Results: 593 eligible patients were included [322(54.3 %) with PMRI, 271(45.7 %) no-MRI]. Mean patient age was younger (53.8 ± 11.8vs59.3 ± 12.6 years, p < 0.001) and dense breasts more common (51.6 %vs22.5 %, p < 0.001) in PMRI group. Seventeen bilateral cancers (5.3 %) were in PMRI [14/17(82.4 %) detected only on MRI] vs 10 (3.7 %) in no-MRI (p = 0.34). Molecular subtype distribution(luminal A:27.2 % vs 31.1 %; luminal B:51.8 %vs44.2 %; HER2:5.4 %vs4.2 %; triple negative:15.6 %vs20.5 %, p = 0.28) were similar in PMRI vs no-MRI groups. PMRI group had higher rates of cT2-4(45.0 %vs28.8 %, p < 0.001), cN+(27.3 % vs 18.1 %, p < 0.01), and neoadjuvant therapy (NAC, 41.3 % vs 18.8 %, p < 0.001). Total mastectomy(57.8 %vs51.3 %, p = 0.12), margin positivity(6.2 %vs7.4 %, p = 0.63), recurrence(10.2 %vs7.0 %, p = 0.20) and death rates(8.1 %vs7.7 %, p = 0.88) were similar in PMRI vs no-MRI. Mastectomy rates remained comparable after adjusting for age and breast density (p = 0.28). At median follow-up of 70 months(IQR, 64-70), time to recurrence was [PMRI:30(IQR, 19-47)vs no-MRI:23(IQR, 9-31) months, p = 0.04]. Contralateral cancers were identified sooner and more frequently in the no-MRI group [4(2.1 %)vs2(0.9 %) cancers, p = 0.32, 21 ± 20vs48 ± 13 months, p = 0.27]. There was no significant difference in 5-year RFS[hazard ratio(HR) 1.05, 95 %CI 0.67-1.67, p = 0.84] and OS[HR 0.94, 95 %CI: 0.51-1.74, p = 0.85] between PMRI and no-MRI groups even after adjusting for age, cancer type, breast density, cN stage, and NAC. which were different between two groups (RFS, HR 0.87, 95 %CI: 0.53-1.43, p = 0.57; OS, HR 0.78, 95 %CI: 0.40-1.52, p = 0.46). NHW patients had higher RFS compared to Black patients in PMRI group (HR 0.45, 95 % CI: 0.21-0.96, p = 0.04) in adjusted analysis.

Conclusions: Preoperative MRI utilization is not associated with improved surgical margin, 5-year RFS or OS in our cohort. This effect persisted after adjusting for patient age, tumor stage, cancer type, breast density and NAC. At post therapy surveillance, contralateral cancers are identified earlier and more frequently in the no-MRI group.

Purpose: Phase-contrast MRI (2D PC-MRI) and Dixon techniques share the characteristic that the difference in frequency between water and fat, as well as the velocity, are encoded in the phase of the MR signal. We propose to take advantage of this characteristic to obtain both sets of images simultaneously. Such an acquisition will improve efficiency by obtaining both types of images in the same scan and will provide co-registered images of water-fat species and velocity images. This, in turn, will correct fat artifacts due to chemical shift in PC-MRI based measurements.

Methods: This study presents a novel PC multi-echo (PCME-MRI) sequence jointly with a 3-point (3p-) Dixon pipeline that enables reconstruction of water, fat, and velocity images simultaneously. The proposed 3p-Dixon approach preserves the phase information of water-fat images, while velocity images are obtained from the resulting water components.

Results: Numerical phantom tests and 2D MR axial images of the neck acquired in 12 healthy volunteers demonstrated the feasibility of the PC 3p-Dixon method, showing comparable performance to standard techniques. In volunteers the median and range MAE comparing PC 3p-Dixon, and standard 3p-Dixon fat fraction were 0.06 and [0.03, 0.09]. The median and range of velocity for PC 3p-Dixon were 6.15 ml and [3.86, 7.21]ml, compared to 6.43 ml and [4.62, 8.27]ml obtained by 2D PC-MRI.

Conclusion: Numerical phantom experiments and acquisitions from healthy volunteers showed promising results in fat fraction and velocity estimation of PC 3p-Dixon compared with standard 3p-Dixon and 2D PC-MRI, obtaining both data sets in similar times as standard 3p Dixon.

Purpose: To determine whether quantitative parameters derived using diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) imaging reflect pathological changes in fibrosarcoma.

Methods: Thirty nude mouse models of fibrosarcoma underwent T1/T2-weighted imaging, DKI, and IVIM imaging on a 3.0-T scanner. Immunohistochemistry was utilized for the hematoxylin and eosin, aquaporin 1 (AQP1), aquaporin 4 (AQP4), and Ki-67 staining of fibrosarcoma tissue, and AQP1 and AQP4 staining of normal muscle tissue (NMT). The independent-sample t-test was used to compare AQP1 and AQP4 expression in fibrosarcoma and NMT. Pearson and Spearman correlation analyses were conducted to evaluate the correlation between imaging parameters and pathological indicators. Multiple linear regression analysis was employed to identify the pathological indicators independently associated with quantitative DKI and IVIM parameters.

Results: Apparent diffusion coefficient (ADC), D, f, and mean kurtosis (MK) indicated cell density and Ki-67 and AQP1 expression intensity. D values reflected AQP4 expression intensity, while MD reflected cell density and AQP1 expression intensity. Cell density (CD) independently influenced ADC and f values, while CD and AQP1 independently influenced D values.

Conclusion: CD and Ki-67 independently influenced MK. DKI- and IVIM imaging-derived ADC, D, f, MD, and MK were correlated with AQP1, AQP4, Ki-67, and CD in nude mice with fibrosarcoma.

Purpose: To investigate the relationship between perirectal fat content and metachronous liver metastasis (MLM) in patients with Mid-low rectal cancer (MLRC).

Materials and methods: A retrospective analysis was conducted on 254 patients who underwent curative surgery for MLRC between December 2016 and December 2021. Preoperative MRI measurements of the rectal mesenteric fat area (MFA), rectal posterior mesorectal thickness (PMT), and rectal mesenteric fascia envelopment volume (MFEV) were performed, along with collection of relevant clinical, pathological, and imaging data. Patients were categorized into the MLM group (Group A), other recurrence or metastasis group (Group B), and no recurrence and metastasis group (Group C). Analyze the differences between Group A and the other groups, and independent risk factors for MLM were explored. Kaplan-Meier analysis and log-rank test were used to validate independent predictive biomarkers for MLM.

Results: Patients with MLM from MLRC had later pathological and imaging T stages and lower perirectal fat content (all P < 0.05). Compared to patients with other types of recurrent metastasis, male gender, poorly differentiated tumors, and advanced tumor N stage were more likely to develop MLM (all P < 0.05). In Cox univariate and multivariate regression analysis, smaller rectal PMT (hazard ratio (HR) 0.361 [0.154-0.846], P = 0.019) and MFEV (HR 0.983 [0.968-0.998], P = 0.022) were independently associated with MLM in MLRC (HR 0.361;0.983). Kaplan-Meier analysis showed that patients with rectal PMT <1.43 cm and rectal MFEV <137.46 cm³ had a significantly higher risk of MLM compared to patients with rectal PMT ≥1.43 cm and rectal MFEV ≥137.46 cm³ (all P < 0.05).

Conclusion: Rectal PMT and rectal MFEV can serve as novel parameters for predicting MLM in patients with MLRC.