Novel method for rapid, simultaneousT1,T*2, and proton density quantification

Noninvasive identification of HER2-zero, -low, or -overexpressing breast cancers: Multiparametric MRI-based quantitative characterization in predicting HER2-low status of breast cancer

PURPOSE

To evaluate the effectiveness of both synthetic magnetic resonance imaging (SyMRI) and conventional diffusion-weighted imaging (DWI) for identifying the human epidermal growth factor receptor 2 (HER2) status in breast cancer (BC) patients.

METHOD

In this retrospective study, 114 women with DWI and SyMRI were pathologically classified into three groups: HER2-overexpressing (n = 40), HER2-low-expressing (n = 53), and HER2-zero-expressing (n = 21). T1 and T2 relaxation times and proton density (PD) were assessed before and after enhancement, and the resulting quantitative parameters produced by SyMRI were recorded as T1, T2, and PD and T1e, T2e, and PDe. Logistic regression was used to identify the best indicators for classifying patients based on HER2 expression. The discriminative performance of the models was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Our preliminary study revealed significant differences in progesterone receptor (PR) status, Ki-67 index, and axillary lymph node (ALN) count among the HER2-zero, -low, and -overexpressing groups (p < 0.001 to p = 0.03). SyMRI quantitative indices showed significant differences among BCs in the three HER2 subgroups, except for ΔT2 (p < 0.05). our results indicate that PDe achieved an area under the curve（AUC）of 0.849 (95 % CI: 0.760-0.915) for distinguishing HER2-low and -overexpressing BCs. Further investigation revealed that both the PDe and ADC were indicators for predicting differences among patients with HER2-zero and HER2-low-expressing BC, with AUCs of 0.765(95 % CI: 0.652-0.855) and 0.684(95 % CI: 0.565-0.787), respectively. The addition of the PDe to the ADC improved the AUC to 0.825(95 % CI: 0.719-0.903).

CONCLUSIONS

SyMRI could noninvasively and robustly predict the HER2 expression status of patients with BC.

Complex multi-dimensional integration for T2* and R2* mapping

A dual Multi-Dimensional Integration (dMDI) method was proposed and demonstrated for T2* and R2* mapping. By constructing and jointly using both the original MDI term and an inversed MDI term, T2* and R2* mapping can be performed independently with intrinsic background noise suppression and spike elimination, allowing for high quantitative accuracy and robustness over a wide range of T2*. dMDI was compared to original MDI and curve fitting methods in terms of quantitative specificity, accuracy, reliability and computational efficiency. All methods were tested and compared via simulation and in vivo data. With high signal-to-noise-ratio (SNR), the proposed dMDI method yielded T2*and R2* values similar to curve fitting methods. For low SNR and background noise signals, the dMDI yielded low T2* and R2* values, thus effectively suppressing all background noise. Virtually zero spikes were observed in dMDI T2* and R2* maps in all simulation and imaging results. The dMDI method has the potential to provide improved and reliable T2* and R2* mapping results in routine and SNR-challenging scenarios.

Phosphorus-31: A table-top method for 3D B1-field amplitude and phase measurements

A novel method of high-spatial-resolution, 3D B1-field distribution measurements is presented. The method is independent of the MR-scanner, and it allows for automated acquisitions of complete maps of all magnetic field vector components for both proton and heteronuclear MR coils of arbitrary geometrical shapes. The advantage of the method proposed here, compared with methods based on measurements with an MR-scanner, is that a complete image of both receive and transmit B1-fields, including the phase of the B1-field, can be acquired. The B1 field maps obtained in this manner can be used for absolute quantification of metabolites in MRS experiments, as well as for intensity compensations in imaging experiments, both of which are important concepts in biological and medical MR applications. Another use might be in coil development and testing. A comparison with B1 field magnitude maps obtained with an MR-scanner was included to validate the accuracy of the proposed method.

Brain volume measured by synthetic magnetic resonance imaging in adult moyamoya disease correlates with cerebral blood flow and brain function

Moyamoya disease (MMD) is characterized by progressive arterial occlusion, causing chronic hemodynamic impairment, which can reduce brain volume. A novel quantitative technique, synthetic magnetic resonance imaging (SyMRI), can evaluate brain volume. This study aimed to investigate whether brain volume measured with SyMRI correlated with cerebral blood flow (CBF) and brain function in adult MMD. In this retrospective study, 18 adult patients with MMD were included. CBF was measured using iodine-123-N-isopropyl-p-iodoamphetamine single photon emission computed tomography. Cerebrovascular reactivity (CVR) to acetazolamide challenge was also evaluated. Brain function was measured using the Wechsler Adult Intelligence Scales (WAIS)-III/IV and the WAIS-R tests. Gray matter (GM), white matter, and myelin-correlated volumes were evaluated in six areas. Resting CBF was positively correlated with GM fractions in the right anterior cerebral arterial and right middle cerebral arterial (MCA) territories. CVR was positively correlated with GM fraction in the right posterior cerebral arterial (PCA) territory. Full-Scale Intelligence Quotient and Verbal Comprehension Index scores were marginally positively correlated with GM fractions in the left PCA territory. Processing Speed Index score was marginally positively correlated with GM fraction in the right MCA territory. The SyMRI-measured territorial GM fraction correlated with CBF and brain function in patients with MMD.

Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted

PURPOSE

This study aimed to compare assessments by radiologists, artificial intelligence (AI), and quantitative measurement using synthetic MRI (SyMRI) for differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, and IDH-mutant and 1p/19q-codeleted and to identify the superior method.

METHODS

Thirty-three cases (men, 14; women, 19) comprising 19 astrocytomas and 14 oligodendrogliomas were evaluated. Four radiologists independently evaluated the presence of the T2-FLAIR mismatch sign. A 3D convolutional neural network (CNN) model was trained using 50 patients outside the test group (28 astrocytomas and 22 oligodendrogliomas) and transferred to evaluate the T2-FLAIR mismatch lesions in the test group. If the CNN labeled more than 50% of the T2-prolonged lesion area, the result was considered positive. The T1/T2-relaxation times and proton density (PD) derived from SyMRI were measured in both gliomas. Each quantitative parameter (T1, T2, and PD) was compared between gliomas using the Mann-Whitney U-test. Receiver-operating characteristic analysis was used to evaluate the diagnostic performance.

RESULTS

The mean sensitivity, specificity, and area under the curve (AUC) of radiologists vs. AI were 76.3% vs. 94.7%; 100% vs. 92.9%; and 0.880 vs. 0.938, respectively. The two types of diffuse gliomas could be differentiated using a cutoff value of 2290/128 ms for a combined 90th percentile of T1 and 10th percentile of T2 relaxation times with 94.4/100% sensitivity/specificity with an AUC of 0.981.

CONCLUSION

Compared to the radiologists' assessment using the T2-FLAIR mismatch sign, the AI and the SyMRI assessments increased both sensitivity and objectivity, resulting in improved diagnostic performance in differentiating gliomas.

Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer

PURPOSE

To investigate the potential of using histogram analysis of synthetic MRI (SyMRI) images before and after contrast enhancement to predict axillary lymph node (ALN) status in patients with invasive ductal carcinoma (IDC).

METHODS

From January 2022 to October 2022, a total of 212 patients with IDC underwent breast MRI examination including SyMRI. Standard T2 weight images, DCE-MRI and quantitative maps of SyMRI were obtained. 13 features of the entire tumor were extracted from these quantitative maps, standard T2 weight images and DCE-MRI. Statistical analyses, including Student's t-test, Mann-Whiney U test, logistic regression, and receiver operating characteristic (ROC) curves, were used to evaluate the data. The mean values of SyMRI quantitative parameters derived from the conventional 2D region of interest (ROI) were also evaluated.

RESULTS

The combined model based on T1-Gd quantitative map (energy, minimum, and variance) and clinical features (age and multifocality) achieved the best diagnostic performance in the prediction of ALN between N0 (with non-metastatic ALN) and N+ group (metastatic ALN ≥ 1) with the AUC of 0.879. Among individual quantitative maps and standard sequence-derived models, the synthetic T1-Gd model showed the best performance for the prediction of ALN between N0 and N+ groups (AUC = 0.823). Synthetic T2_entropy and PD-Gd_energy were useful for distinguishing N1 group (metastatic ALN ≥ 1 and ≤ 3) from the N2-3 group (metastatic ALN > 3) with an AUC of 0.722.

CONCLUSIONS

Whole-tumor histogram features derived from quantitative parameters of SyMRI can serve as a complementary noninvasive method for preoperatively predicting ALN metastases.

Automatic estimation of brain parenchymal fraction in patients with multple sclerosis: a comparison between synthetic MRI and an established automated brain segmentation software based on FSL

PURPOSE

We aimed to validate the estimation of the brain parenchymal fraction (BPF) in patients with multiple sclerosis (MS) using synthetic magnetic resonance imaging (SyMRI) by comparison with software tools of the FMRIB Software Library (FSL). In addition to a cross-sectional method comparison, longitudinal volume changes were assessed to further elucidate the suitability of SyMRI for quantification of disease-specific changes.

METHODS

MRI data from 216 patients with MS and 28 control participants were included for volume estimation by SyMRI and FSL-SIENAX. Moreover, longitudinal data from 35 patients with MS were used to compare registration-based percentage brain volume changes estimated using FSL-SIENA to difference-based calculations of volume changes using SyMRI.

RESULTS

We observed strong correlations of estimated brain volumes between the two methods. While SyMRI overestimated grey matter and BPF compared to FSL-SIENAX, indicating a systematic bias, there was excellent agreement according to intra-class correlation coefficients for grey matter and good agreement for BPF and white matter. Bland-Altman plots suggested that the inter-method differences in BPF were smaller in patients with brain atrophy compared to those without atrophy. Longitudinal analyses revealed a tendency for higher atrophy rates for SyMRI than for SIENA, but SyMRI had a robust correlation and a good agreement with SIENA.

CONCLUSION

In summary, BPF based on data from SyMRI and FSL-SIENAX is not directly transferable because an overestimation and higher variability of SyMRI values were observed. However, the consistency and correlations between the two methods were satisfactory, and SyMRI was suitable to quantify disease-specific atrophy in MS.

Correcting synthetic MRI contrast-weighted images using deep learning

Synthetic magnetic resonance imaging (MRI) offers a scanning paradigm where a fast multi-contrast sequence can be used to estimate underlying quantitative tissue parameter maps, which are then used to synthesize any desirable clinical contrast by retrospectively changing scan parameters in silico. Two benefits of this approach are the reduced exam time and the ability to generate arbitrary contrasts offline. However, synthetically generated contrasts are known to deviate from the contrast of experimental scans. The reason for contrast mismatch is the necessary exclusion of some unmodeled physical effects such as partial voluming, diffusion, flow, susceptibility, magnetization transfer, and more. The inclusion of these effects in signal encoding would improve the synthetic images, but would make the quantitative imaging protocol impractical due to long scan times. Therefore, in this work, we propose a novel deep learning approach that generates a multiplicative correction term to capture unmodeled effects and correct the synthetic contrast images to better match experimental contrasts for arbitrary scan parameters. The physics inspired deep learning model implicitly accounts for some unmodeled physical effects occurring during the scan. As a proof of principle, we validate our approach on synthesizing arbitrary inversion recovery fast spin-echo scans using a commercially available 2D multi-contrast sequence. We observe that the proposed correction visually and numerically reduces the mismatch with experimentally collected contrasts compared to conventional synthetic MRI. Finally, we show results of a preliminary reader study and find that the proposed method statistically significantly improves in contrast and SNR as compared to synthetic MR images.

Multiparametric MRI model with synthetic MRI, DWI multi-quantitative parameters, and differential sub-sampling with cartesian ordering enables BI-RADS 4 lesions diagnosis with high accuracy

Objective

To assess the feasibility and diagnostic performances of synthetic magnetic resonance imaging (SyMRI) combined with diffusion-weighted imaging (DWI) and differential subsampling with cartesian ordering (DISCO) in breast imaging reporting and data system (BI-RADS) 4 lesions.

Methods

A total of 98 BI-RADS 4 patients, including 68 cases assigned to a malignant group and 33 cases assigned to a benign group, were prospectively enrolled, and their MRI and clinical information were collected. Two physicians jointly analyzed the characteristics of conventional MRI. T1, T2, proton density (PD), and ADC values were obtained from three different regions of interest (ROIs). Logistic regression analyses were used to select features and build models, and a nomogram was constructed with the best model.

Results

Using the ROI delineation method at the most obvious enhancement to measure the ADC value revealed the best diagnostic performance in diagnosing BI-RADS type 4 mass lesions. The diagnostic efficiency of the maximum level drawing method of the quantitative relaxation model was better than that of the whole drawing method and the most obvious enhancement method. The best relaxation model (model A) was composed of two parameters: T2stand and ΔT1%stand (AUC=0.887), and the BI-RADS model (model B) was constructed by two MRI features of edge and TIC curve (AUC=0.793). Using the quantitative parameters of SyMRI and DWI of the best ROC method combined with DISCO enhanced MRI features to establish a joint diagnostic model (model C: edge, TIC curve type, ADClocal, T2stand, ΔT1%stand) showed the best diagnostic efficiency (AUC=0.953). The nomogram also had calibration curves with good overlap.

Conclusions

The combined diagnosis model of SyMRI and DWI quantitative parameters combined with DISCO can improve the diagnostic efficiency of BI-RADS 4 types of mass lesions. Also, the line diagram based on this model can be used as an auxiliary diagnostic tool.

Synthetic MRI quantitative parameters in discriminating stage T1 nasopharyngeal carcinoma and benign hyperplasia: Combination with morphological features

PURPOSE

To investigate the feasibility of synthetic MRI (syMRI) quantitative parameters and its combination with morphological features in discriminating stage T1 nasopharyngeal carcinoma (T1-NPC) and benign hyperplasia (BH).

MATERIAL AND METHODS

Eighty-eight patients with nasopharyngeal lesions (T1-NPC, n = 54; BH, n = 34) were retrospectively enrolled between October 2020 and May 2022. The syMRI quantitative parameters of nasopharyngeal lesions (T1, T2, PD, T1SD, T2SD, PDSD) and longus capitis (T1, T2, PD) were measured, and T1ratio, T2ratio and PDratio were calculated (lesion/longus capitis). The morphological features (lesion pattern, retention cyst, serrated protrusion, middle ear effusion, tumor volume, and retropharyngeal lymph node) were compared. Statistical analyses were performed using the independent sample t test, Chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test.

RESULTS

The T1, T2, PD, T1SD, T1ratio, and T2ratio values of T1-NPC were significantly lower than those of BH. The morphological features (lesion pattern, retention cyst, retropharyngeal lymph node) were significant difference between these two entities. T2 value has the highest AUC in all syMRI quantitative parameters, followed by T1, T1ratio, PD, T2ratio and T1SD. Combined syMRI quantitative parameters (T2, PD, T1ratio) can further improve the diagnosis efficiency. Combined syMRI parameters and morphological feature (T2, PD, lesion pattern, retropharyngeal lymph node) has the excellent diagnostic efficiency, with AUC, sensitivity, specificity, and accuracy of 0.979, 96.30%, 97.06%, 96.77%.

CONCLUSIONS

Synthetic MRI was helpful in distinguishing T1-NPC from BH, and combined syMRI quantitative parameters and morphological features has the optimal diagnostic performance.

B1 mapping using pre-learned subspaces for quantitative brain imaging

PURPOSE

To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging.

THEORY AND METHODS

A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data.

RESULTS

In both phantom and healthy subject data, the proposed method produced high-quality B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues.

CONCLUSION

This work presents a novel method to estimate B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo

PURPOSE

MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T1, T2*, PD, and susceptibility) and B1 simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments.

METHODS

Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching.

RESULTS

A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods.

CONCLUSION

The proposed QPM method can map T1, T2*, PD, susceptibility, and B1 simultaneously within a scan time that can be applied to human subjects.

Time Course Changes of Synthetic Relaxation Time During Neoadjuvant Chemotherapy in Breast Cancer: The Optimal Parameter for Treatment Response Evaluation

BACKGROUND

Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer.

PURPOSE

To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer.

STUDY TYPE

Retrospective, longitudinal.

POPULATION

A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC.

FIELD STRENGTH/SEQUENCE

A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence.

ASSESSMENT

Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed.

STATISTICAL TESTS

Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis.

RESULTS

Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR = 1.206) compared to grade II patients (OR = 1.067).

DATA CONCLUSION

Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response

BACKGROUND

To evaluate the correlation between synthetic MRI (syMRI) relaxometry and apparent diffusion coefficient (ADC) maps in different breast cancer subtypes and treatment response subgroups.

METHODS

Two hundred sixty-three neoadjuvant therapy (NAT)-treated breast cancer patients with baseline MRI were enrolled. Tumor annotations were obtained by drawing regions of interest (ROIs) along the lesion on T1/T2/PD and ADC maps respectively. Histogram features from T1/T2/PD and ADC maps were respectively calculated, and the correlation between each pair of identical features was analyzed. Meanwhile, features between different NAT treatment response groups were compared, and their discriminatory power was evaluated.

RESULTS

Among all patients, 20 out of 27 pairs of features weakly correlated (r = - 0.13-0.30). For triple-negative breast cancer (TNBC), features from PD map in the pathological complete response (pCR) group (r = 0.60-0.86) showed higher correlation with ADC than that of the non-pCR group (r = 0.30-0.43), and the mean from the ADC and PD maps in the pCR group strongly correlated (r = 0.86). For HER2-positive, few correlations were found both in the pCR and non-pCR groups. For luminal HER2-negative, T2 map correlated more with ADC than T1 and PD maps. Significant differences were seen in T2 low percentiles and median in the luminal-HER2 negative subtype, yielding moderate AUCs (0.68/0.72/0.71).

CONCLUSIONS

The relationship between ADC and PD maps in TNBC may indicate different NAT responses. The no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

CRITICAL RELEVANCE STATEMENT

The relationship between ADC and PD maps in TNBC may indicate different NAT responses, and the no-to-weak correlation between the ADC and syMRI suggests their complementary roles in tumor microenvironment evaluation.

KEY POINTS

• The relationship between ADC and PD in TNBC indicates different NAT responses. • The no-to-weak correlations between ADC and syMRI complementarily evaluate tumor microenvironment. • T2 low percentiles and median predict NAT response in luminal-HER2-negative subtype.

One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each contrast provides complementary information. However, the availability of each imaging contrast may vary amongst patients, which poses challenges to radiologists and automated image analysis algorithms. A general approach for tackling this problem is missing data imputation, which aims to synthesize the missing contrasts from existing ones. While several convolutional neural networks (CNN) based algorithms have been proposed, they suffer from the fundamental limitations of CNN models, such as the requirement for fixed numbers of input and output channels, the inability to capture long-range dependencies, and the lack of interpretability. In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing. MMT consists of a multi-scale Transformer encoder that builds hierarchical representations of inputs combined with a multi-scale Transformer decoder that generates the outputs in a coarse-to-fine fashion. The proposed multi-contrast Swin Transformer blocks can efficiently capture intra- and inter-contrast dependencies for accurate image synthesis. Moreover, MMT is inherently interpretable as it allows us to understand the importance of each input contrast in different regions by analyzing the in-built attention maps of Transformer blocks in the decoder. Extensive experiments on two large-scale multi-contrast MRI datasets demonstrate that MMT outperforms the state-of-the-art methods quantitatively and qualitatively.

Tailored magnetic resonance fingerprinting

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T₁ weighted, T₁ fluid attenuated inversion recovery (FLAIR), T₂ weighted, water, and fat), and quantitative imaging (T₁ and T₂ maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T₁ and T₂ values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min^-1 which is higher compared to GS (0.32 min^-1) and MRF (0.90 min^-1).

Simultaneous T1, T2, and T2* Mapping of Carotid Plaque: The SIMPLE* Technique

Background Quantitative T1, T2, and T2* measurements of carotid atherosclerotic plaque are important in evaluating plaque vulnerability and monitoring its progression. Purpose To develop a sequence to simultaneously quantify T1, T2, and T2* of carotid plaque. Materials and Methods The simultaneous T1, T2, and T2* mapping of carotid plaque (SIMPLE*) sequence is composed of three modules with different T2 preparation pulses, inversion-recovery pulses, and acquisition schemas. Single-echo data were used for T1 and T2 quantification, while the multiecho (ME) data were used for T2* quantification. The quantitative accuracy of SIMPLE* was tested in a phantom study by comparing its measurements with those of reference standard sequences. In vivo feasibility of the technique was prospectively evaluated between November 2020 and February 2022 in healthy volunteers and participants with carotid atherosclerotic plaque. The Pearson or Spearman correlation test, Student t test, and Wilcoxon rank-sum test were used. Results T1, T2, and T2* estimated with SIMPLE* strongly correlated with inversion-recovery spin-echo (SE) (correlation coefficient [r] = 0.99), ME-SE (r = 0.99), and ME gradient-echo (r = 0.99) sequences in the phantom study. In five healthy volunteers (mean age, 25 years ± 3 [SD]; three women), measurements were similar between SIMPLE* and modified Look-Locker inversion recovery, or MOLLI (1151 msec ± 71 vs 1098 msec ± 64; P = .14), ME turbo SE (31 msec ± 1 vs 31 msec ± 1; P = .32), and ME turbo field echo (24 msec ± 2 vs 25 msec ± 2; P = .19). In 18 participants with carotid plaque (mean age, 65 years ± 9; 16 men), quantitative T1, T2, and T2* of plaque components were consistent with their signal characteristics on multicontrast images. Conclusion A quantitative technique for simultaneous T1, T2, and T2* mapping of carotid plaque with 100-mm³ coverage and 0.8-mm³ resolution was developed using the proposed SIMPLE* sequence and demonstrated high accuracy and in vivo feasibility. © RSNA, 2023 Supplemental material is available for this article.

Improved visualization of the subthalamic nucleus on synthetic MRI with optimized parameters: initial study

BACKGROUND

Synthetic magnetic resonance imaging (SyMRI) enables to reformat various images by adjusting the MR parameters.

PURPOSE

To investigate whether customization of the repetition time (TR), echo time (TE), and inversion time (TI) in SyMRI could improve the visualization of subthalamic nucleus (STN).

MATERIAL AND METHODS

We examined five healthy volunteers using both coronal SyMRI and quantitative susceptibility mapping (QSM), seven patients with Parkinson's disease (PD) using coronal SyMRI, and 15 patients with PD using coronal QSM. Two neuroradiologists reformatted SyMRI (optimized SyMRI) by adjusting TR, TE, and TI to achieve maximum tissue contrast between the STN and the adjacent brain parenchyma. The optimized MR parameters in the PD patients varied according to the individual. For regular SyMRI (T2-weighted imaging [T2WI] and STIR), optimized SyMRI, and QSM, qualitative visualization scores of the STN (STN score) were recorded. The contrast-to-noise ratio (CNR) of the STN was also measured.

RESULTS

For the STN scores in both groups, the optimized SyMRI were significantly higher than the regular SyMRI (P < 0.05), and there were no significant differences between optimized SyMRI and QSM. For the CNR of differentiation of the STN from the substantia nigra, the optimized SyMRI was higher than the regular SyMRI (volunteer: T2WI P = 0.10 and STIR P = 0.26; PD patient: T2WI P = 0.43 and STIR P = 0.25), but the optimized SyMRI was lower than the QSM (volunteer: P = 0.26; PD patient: P = 0.03).

CONCLUSIONS

On SyMRI, optimization of MR parameters (TR, TE, and TI) on an individual basis may be useful to increase the conspicuity of the STN.

Fast, interleaved, Look-Locker-based T1 mapping with a variable averaging approach: Towards temperature mapping at low magnetic field

Proton resonance frequency shift (PRFS) is currently the gold standard method for magnetic resonance thermometry. However, the linearity between the temperature-dependent phase accumulation and the static magnetic field B₀ confines its use to rather high-field scanners. Applications such as thermal therapies could naturally benefit from lower field MRI settings through leveraging increased accessibility, a lower physical and economical footprint, and further consideration of the technical challenges associated with the integration of heating systems into conventional clinical scanners. T 1 -based thermometry has been proposed as an alternative to the gold standard; however, because of longer acquisition times, it has found clinical use solely with adipose tissue where PRFS fails. At low field, the enhanced T 1 dispersion, combined with reduced relaxation times, make T 1 mapping an appealing candidate. Here, an interleaved Look-Locker-based T 1 mapping sequence was proposed for temperature quantification at 0.1 T. A variable averaging scheme was introduced, to maximize the signal-to-noise ratio throughout T 1 recovery. In calibrated samples, an average T 1 accuracy of 85% ± 4% was achieved in 10 min, compared with the 77% ± 7% obtained using a standard averaging scheme. Temperature maps between 29.0 and 41.7°C were eventually reconstructed, with a precision of 3.0 ± 1.1°C and an accuracy of 1.5 ± 1.0°C. Accounting for longer thermal treatments and less strict temperature constraints, applications such as MR-guided mild hyperthermia treatments at low field could be envisioned.

Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging

OBJECTIVES

This study aimed to evaluate the synthetic MRI (syMRI), its combination with diffusion-weighted imaging (DWI), and morphological features for discriminating benign from metastatic retropharyngeal lymph nodes (RLNs).

METHODS

Fifty-eight patients with a total of 63 RLNs (21 benign and 42 metastatic) were enrolled. The mean and standard deviation of syMRI-derived relaxometry parameters (T1, T2, PD; T1_SD, T2_SD, PD_SD) were obtained from two different regions of interest (namely, partial-lesion and full-lesion ROI). The parameters derived from benign and metastatic RLNs were compared using Student's t or chi-square tests. Logistic regression analysis was used to construct a multi-parameter model of syMRI, syMRI + DWI, and syMRI + DWI + morphological features. Areas under the curve (AUC) were compared using the DeLong test to determine the best diagnostic approach.

RESULTS

Benign RLNs had significantly higher T1, T2, PD, and T1_SD values compared with metastatic RLNs in both partial-lesion and full-lesion ROI (all p < 0.05). The T1_SD obtained from full-lesion ROI showed the best diagnostic performance among all syMRI-derived single parameters. The AUC of combined syMRI multiple parameters (T1, T2, PD, T1_SD) were higher than those of any single parameter from syMRI. The combination of synthetic MRI and DWI can improve the AUC regardless of ROI delineation. Furthermore, the combination of synthetic MRI, DWI-derived quantitative parameters, and morphological features can significantly improve the overall diagnostic performance.

CONCLUSIONS

The value of syMRI has been validated in differential diagnosis of benign and metastatic RLNs, and syMRI + DWI + morphological features can further improve the diagnostic efficiency for discriminating these two entities.

KEY POINTS

• Synthetic MRI was useful in differential diagnosis of benign and metastatic RLNs. • The combination of syMRI, DWI, and morphological features can significantly improve the diagnostic efficiency.

Application of synthetic magnetic resonance imaging and DWI for evaluation of prognostic factors in cervical carcinoma: a prospective preliminary study

OBJECTIVES

To determine the values of quantitative metrics derived from synthetic MRI (SyMRI) and apparent diffusion coefficient (ADC) in evaluating the prognostic factors of cervical carcinoma (CC).

METHODS

In this prospective study, 74 patients with pathologically confirmed CC were enrolled. Pretreatment quantitative metrics including T1, T2 and ADC values were obtained from SyMRI and diffusion-weighted imaging (DWI) sequences. The values of all metrics were compared for different prognostic features using Student's t-test or Mann-Whitney U-test. The receiver operating characteristic (ROC) curve and multivariate logistic regression analysis were utilized to evaluate the diagnostic performance of quantitative variables.

RESULTS

T1 and T2 values of parametrial involvement (PMI)-negative were significantly higher than those of PMI-positive (p = 0.002 and < 0.001), while ADC values did not show a significant difference. The area under curve (AUC) of T1 and T2 values for identifying PMI were 0.743 and 0.831. Only the T2 values showed a significant difference between the lymphovascular space involvement (LVSI)-negative and LVSI-positive (p < 0.001), and the AUC of T2 values for discriminating LVSI was 0.814. The differences of T1, T2, and ADC values between the well/moderately and the poorly differentiated CC were significant (all p < 0.001). The AUCs of T1, T2 and ADC values for predicting differentiation grades were 0.762, 0.830, and 0.808. The combined model of all metrics proved to achieve good diagnostic performance with the AUC of 0.866.

CONCLUSION

SyMRI may be a potential noninvasive tool for assessing the prognostic factors such as PMI, LVSI, and differentiation grades in CC. Moreover, the overall diagnostic performances of synthetic quantitative metrics were superior to the ADC values, especially in identifying PMI and LVSI.

ADVANCES IN KNOWLEDGE

This is the first study to assess the utility of SyMRI-derived parameters and ADC value in evaluating the prognostic factors in CC.

Pre-contrast MAGiC in treated gliomas: a pilot study of quantitative MRI

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed.

Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score

Objective

To investigate whether there is added value of quantitative parameters from synthetic magnetic resonance imaging (SyMRI) as a complement to the Kaiser score (KS) to differentiate benign and malignant breast lesions.

Materials and methods

In this single-institution study, 122 patients who underwent breast MRI from March 2020 to May 2021 were retrospectively analyzed. SyMRI and dynamic contrast-enhanced MRI were performed using a 3.0-T system. Two experienced radiologists independently assigned the KS and measured the quantitative values of T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) from SyMRI. Pathology was regarded as the gold standard. The diagnostic values were compared using the appropriate statistical tests.

Results

There were 122 lesions (86 malignant and 36 benign) in 122 women. The T1 value was identified as the only independent factor for the differentiation of malignant and benign lesions. The diagnostic accuracy of incorporating the T1 into the KS protocol (T1+KS) was 95.1% and 92.1% for all lesions (ALL) and The American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions, respectively, which was significantly higher than that of either T1 (ALL: 82.8%, P = 0.0001; BI-RADS 4: 78.9%, P = 0.002) or KS (ALL: 90.2%, P = 0.031; BI-RADS 4: 84.2%, P = 0.031) alone. The sensitivity and specificity of T1+KS were also higher than those of the T1 or KS alone. The combined diagnosis could have avoided another 15.6% biopsies compared with using KS alone.

Conclusions

Incorporating T1 into the KS protocol improved both the sensitivity and specificity to differentiate benign and malignant breast lesions, thus avoiding unnecessary invasive procedures.

Synthetic MRI in the detection and quantitative evaluation of sacroiliac joint lesions in axial spondyloarthritis

Objective

Our primary objective was to verify the hypothesis that synthetic magnetic resonance imaging (MRI) is similar to conventional MRI in detecting sacroiliac joint lesions in patients with axial spondyloarthritis (axSpA). A secondary objective was to assess the quantitative value of synthetic mapping in bone marrow edema (BME) and fat metaplasia.

Methods

A total of 132 axSpA patients who underwent synthetic and conventional MRI from October 2019 to March 2021 were included in this prospective study. Two independent readers visually evaluated active inflammatory (BME, capsulitis, enthesitis, and inflammation at site of erosion) and structural lesions (erosion, sclerosis, ankylosis, and fat metaplasia) of the sacroiliac joints on conventional and synthetic magnetic resonance (MR) images. In addition, T1, T2, and proton density (PD) values, which were generated by synthetic mapping, were used to further quantitatively evaluate BME and fat metaplasia. A McNemar test was used to compare the differences between the two methods in the detection of sacroiliac joint lesions. Intraclass correlation coefficients (ICCs) were used to assess the inter-reader consistency of quantitative values. Mann-Whitney tests were performed, and receiver operating characteristic (ROC) curves were created for all quantitative analyses.

Results

There were no statistical difference between synthetic and conventional MRI in the detection of sacroiliac joint lesions (all p-values > 0.05). A total of 103 images of BME and 111 images of fat metaplasia were quantitatively evaluated using T1, T2, and PD values. The consistency of quantitative values among readers was good (ICC 0.903-0.970). T1 and T2 values were consistently higher in BME than in normal marrow (p < 0.001), but PD values were not significantly different (p = 0.830). T2 and PD values were higher in fat metaplasia than in normal marrow, but T1 values were lower (p < 0.001). In the case of BME, T1 values had greater diagnostic efficiency [area under the curve (AUC) 0.99] than T2 values (AUC 0.78). There were no significant differences in the diagnostic efficiency of T1 (AUC 0.88), T2 (AUC 0.88), and PD (AUC 0.88) values in the case of fat metaplasia.

Conclusion

Synthetic MRI is as effective as conventional MRI in detecting sacroiliac joint lesions in patients with axSpA. Furthermore, synthetic mapping can accurately quantify BME and fat metaplasia.

Relaxometry and brain myelin quantification with synthetic MRI in MS subtypes and their associations with spinal cord atrophy

Immune-mediated demyelination and neurodegeneration are pathophysiological hallmarks of Multiple Sclerosis (MS) and main drivers of disease related disability. The principal method for evaluating qualitatively demyelinating events in the clinical context is contrast-weighted magnetic resonance imaging (MRI). Moreover, advanced MRI sequences provide reliable quantification of brain myelin offering new opportunities to study tissue pathology in vivo. Towards neurodegenerative aspects of the disease, spinal cord atrophy - besides brain atrophy - is a powerful and validated predictor of disease progression. The etiology of spinal cord volume loss is still a matter of research, as it remains unclear whether the impact of local lesion pathology or the interaction with supra- and infratentorial axonal degeneration and demyelination of the long descending and ascending fiber tracts are the determining factors. Quantitative synthetic MR using a multiecho acquisition of saturation recovery pulse sequence provides fast automatic brain tissue and myelin volumetry based on R1 and R2 relaxation rates and proton density quantification, making it a promising modality for application in the clinical routine. In this cross sectional study a total of 91 MS patients and 31 control subjects were included to investigate group differences of global and regional measures of brain myelin and relaxation rates, in different MS subtypes, using QRAPMASTER sequence and SyMRI postprocessing software. Furthermore, we examined associations between these quantitative brain parameters and spinal cord atrophy to draw conclusions about possible pathophysiological relationships. Intracranial myelin volume fraction of the global brain exhibited statistically significant differences between control subjects (10.4%) and MS patients (RRMS 9.4%, PMS 8.1%). In a LASSO regression analysis with total brain lesion load, intracranial myelin volume fraction and brain parenchymal fraction, the intracranial myelin volume fraction was the variable with the highest impact on spinal cord atrophy (standardized coefficient 4.52). Regional supratentorial MRI metrics showed altered average myelin volume fraction, R1, R2 and proton density in MS patients compared to controls most pronounced in PMS. Interestingly, quantitative MRI parameters in supratentorial regions showed strong associations with upper cord atrophy, suggesting an important role of brain diffuse demyelination on spinal cord pathology possibly in the context of global disease activity. R1, R2 or proton density of the thalamus, cerebellum and brainstem correlated with upper cervical cord atrophy, probably reflecting the direct functional connection between these brain structures and the spinal cord as well as the effects of retrograde and anterograde axonal degeneration. By using Synthetic MR-derived myelin volume fraction, we were able to effectively detect significant differences of myelination in relapsing and progressive MS subtypes. Total intracranial brain myelin volume fraction seemed to predict spinal cord volume loss better than brain atrophy or total lesion load. Furthermore, demyelination in highly myelinated supratentorial regions, as an indicator of diffuse disease activity, as well as alterations of relaxation parameters in adjacent infratentorial and midbrain areas were strongly associated with upper cervical cord atrophy.

Qualitative and Quantitative Performance of Magnetic Resonance Image Compilation (MAGiC) Method: An Exploratory Analysis for Head and Neck Imaging

The present exploratory study investigates the performance of a new, rapid, synthetic MRI method for diagnostic image quality assessment and measurement of relaxometry metric values in head and neck (HN) tumors and normal-appearing masseter muscle. The multi-dynamic multi-echo (MDME) sequence was used for data acquisition, followed by synthetic image reconstruction on a 3T MRI scanner for 14 patients (3 untreated and 11 treated). The MDME enables absolute quantification of physical tissue properties, including T1 and T2, with a shorter scan time than the current state-of-the-art methods used for relaxation measurements. The vendor termed the combined package MAGnetic resonance imaging Compilation (MAGiC). In total, 48 regions of interest (ROIs) were analyzed, drawn on normal-appearing masseter muscle and tumors in the HN region. Mean T1 and T2 values obtained from normal-appearing muscle were 880 ± 52 ms and 46 ± 3 ms, respectively. Mean T1 and T2 values obtained from tumors were 1930 ± 422 ms and 77 ± 13 ms, respectively, for the untreated group, 1745 ± 410 ms and 107 ± 61 ms, for the treated group. A total of 1552 images from both synthetic MRI and conventional clinical imaging were assessed by the radiologists to provide the rating for T1w and T2w image contrasts. The synthetically generated qualitative T2w images were acceptable and comparable to conventional diagnostic images (93% acceptability rating for both). The acceptability ratings for MAGiC-generated T1w, and conventional images were 64% and 100%, respectively. The benefit of MAGiC in HN imaging is twofold, providing relaxometry maps in a clinically feasible time and the ability to generate a different combination of contrast images in a single acquisition.

Quantitative relaxometry using synthetic MRI could be better than T2-FLAIR mismatch sign for differentiation of IDH-mutant gliomas: a pilot study

This study aimed to determine whether quantitative relaxometry using synthetic magnetic resonance imaging (SyMRI) could differentiate between two diffuse glioma groups with isocitrate dehydrogenase (IDH)-mutant tumors, achieving an increased sensitivity compared to the qualitative T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign. Between May 2019 and May 2020, thirteen patients with IDH-mutant diffuse gliomas, including seven with astrocytomas and six with oligodendrogliomas, were evaluated. Five neuroradiologists independently evaluated the presence of the qualitative T2-FLAIR mismatch sign. Interrater agreement on the presence of the T2-FLAIR mismatch sign was calculated using the Fleiss kappa coefficient. SyMRI parameters (T1 and T2 relaxation times and proton density) were measured in the gliomas and compared by the Mann-Whitney U test. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance. The sensitivity, specificity, and kappa coefficient were 57.1%, 100%, and 0.60, respectively, for the qualitative T2-FLAIR mismatch sign. The two types of diffuse gliomas could be differentiated using a cutoff value of 178 ms for the T2 relaxation time parameter with 100% sensitivity, specificity, accuracy, and positive and negative predictive values, with an area under the curve (AUC) of 1.00. Quantitative relaxometry using SyMRI could differentiate astrocytomas from oligodendrogliomas, achieving an increased sensitivity and objectivity compared to the qualitative T2-FLAIR mismatch sign.

[Differential diagnosis of benign and malignant breast lesions using quantitative synthetic magnetic resonance imaging]

OBJECTIVE

To investigate the value of quantitative synthetic magnetic resonance imaging (SyMRI) in distinguishing between benign and malignant breast lesions.

METHODS

We retrospectively collected data of preoperative conventional MRI and multi-dynamic multi-echo sequences from 95 patients with breast lesions showing mass-type enhancement on DCE-MRI, including 27 patients with benign lesions and 68 with malignant lesions. The MRI features of the lesions (shape, margin, internal enhancement pattern, time-signal intensity curve, and T2WI signal) were analyzed, and for each lesion, SyMRI-generated quantitative parameters including T1 and T2 relaxation time and proton density (PD) were measured before and after enhancement and recorded as T1p, T2p, PDp and T1e, T2e, and PDe, respectively. The relative change rate of each parameter was calculated. Logistic regression and all-subset regression analyses were performed for variable selection to construct diagnostic models of the breast lesions, and receiver-operating characteristic (ROC) analysis was used to assess the performance of each model for differentiation of benign and malignant lesions.

RESULTS

There were significant differences in the MRI features between benign and malignant lesions (P < 0.05). All the SyMRI-generated quantitative parameters, with the exception of T2e and Pdp, showed significant differences between benign and malignant lesions (P < 0.05). Among the constructed diagnostic models, the model based on all the DCE-MRI features combined with SyMRI parameters T2p and T1e (DCE-MRI+T2p+T1e) showed the best performance in the differential diagnosis malignant breast masses with an AUC of 0.995 (95% CI: 0.983-1.000).

CONCLUSION

Quantitative SyMRI can be used for differential diagnosis of benign and malignant breast lesions.

3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.

BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T1 , T2 , M0 , B0 , and B1 maps

PURPOSE

Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T₁ , T₂ , and proton density (M₀ ) parameter maps, along with B₀ and B₁ information from the acquired signals.

THEORY AND METHODS

An imaging sequence with three 90° RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B₀ inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed.

RESULTS

The proposed acquisition provided distortion-free T₁ , T₂ , relative proton density (M0), B₀ , and B₁ maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T₁ , T₂ , M₀ , B₀ , and B₁ maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels.

CONCLUSION

The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T₁ , T₂ , M₀ , B₀ , and B₁ maps at 1 × 1 × 5 mm³ resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.

A Novel MRI-Based Quantitative Water Content Atlas of the Human Brain

The measurement of quantitative, tissue-specific MR properties, e.g., water content, longitudinal relaxation time (T₁) and effective transverse relaxation time (T₂*), using quantitative MRI at a clinical field strength (1.5 T to 3T) is a well-explored topic. However, none of the commonly used standard brain atlases, such as MNI or JHU, provide quantitative information. Within the framework of quantitative MRI of the brain, this work reports on the development of the first quantitative brain atlas for tissue water content at 3T. A methodology to create this quantitative atlas of in vivo brain water content based on healthy volunteers is presented, and preliminary, practical examples of its potential applications are also shown. Established methods for the fast and reliable measurement of the absolute water content were used to achieve high precision and accuracy. Water content and T₂* were mapped based on two different methods: an intermediate-TR, two-point method and a long-TR, single-scan method. Twenty healthy subjects (age 25.3 ± 2.5 years) were examined with these quantitative imaging protocols. The images were normalised to MNI stereotactic coordinates, and water content atlases of healthy volunteers were created for each method and compared. Regions-of-interest were generated with the help of a standard MNI template, and water content values averaged across the ROIs were compared to water content values from the literature. Finally, in order to demonstrate the strength of quantitative MRI, water content maps from patients with pathological changes in the brain due to stroke, tumour (glioblastoma) and multiple sclerosis were voxel-wise compared to the healthy brain. The water content atlases were largely independent of the method used to acquire the individual water maps. Global grey matter and white matter water content values between the methods agreed with each other to within 0.5 %. The feasibility of detecting abnormal water content in the brains of patients based on comparison to a healthy brain water content atlas was demonstrated. In summary, the first quantitative water content brain atlas in vivo has been developed and a voxel-wise assessment of pathology-related changes in the brain water content has been performed. These results suggest that qMRI, in combination with a water content atlas, allows for a quantitative interpretation of changes due to disease and could be used for disease monitoring.

Tailored Magnetic Resonance Fingerprinting for simultaneous non-synthetic and quantitative imaging: A repeatability study

PURPOSE

The goals of this study include: (a) generating tailored magnetic resonance fingerprinting (TMRF) based non-synthetic imaging; (b) assessing the repeatability of TMRF and deep learning-based mapping of in vitro ISMRM/NIST phantom and in vivo brain data of healthy human subjects.

METHODS

We have acquired qualitative images obtained from the vendor-supplied gold standard, MRF (synthetic), and TMRF (non-synthetic) on one representative healthy human brain. We also acquired thirty datasets on the ISMRM/NIST phantom for the in vitro repeatability study on a GE Discovery 3T MR750w scanner using the TMRF sequence. We compared T₁ and T₂ maps generated from thirty ISMRM/NIST phantom datasets to the spin-echo (SE) based gold standard (GS) method as part of the in vitro repeatability study. R-squared coefficient of determination in a simple linear regression and Bland-Altman analysis were computed for thirty datasets of ISMRM/NIST phantom to assess the accuracy of in vitro quantitative TMRF data. The repeatability of T₁ and T₂ estimates by TMRF was evaluated by calculating the standard deviation (SD) divided by the average of thirty datasets for each sphere, respectively. We acquired ten volunteers for the in vivo repeatability study on the same scanner using the same TMRF sequence. These volunteers were imaged five times with two runs per repetition, resulting in one hundred in vivo datasets. Five contrasts, T₁ and T₂ maps of ten human volunteers acquired over five repetitions, were evaluated in the in vivo repeatability study. We computed the intraclass correlation coefficient (ICC) of the signal-to-noise ratio (SNR), signal intensities, T₁ and T₂ relaxation times in white matter (WM) and gray matter (GM).

RESULTS

The synthetic images generated from MRF show partial volume and flow artifacts compared to non-synthetic images obtained from TMRF images and the gold standard. in vitro studies show that TMRF estimates have less than 5% variations except sphere 14 in the T₂ array (6.36%). TMRF and spin-echo relaxometry measurements were strongly correlated; R² values were 0.9958 and 0.9789 for T₁ and T₂ estimates, respectively. Based on the ICC values, SNR, mean intensity values, and relaxation times of WM and GM for the in vivo studies were consistent. T₁ and T₂ values of WM and GM were similar to previously published values. The mean ± SD of T₁ and T₂ for WM for ten subjects and five repeats are 992 ± 41 ms and 99 ± 6 ms, while the corresponding values for T₁ and T₂ for GM are 1598 ± 73 ms and 152 ± 14 ms.

CONCLUSION

TMRF and deep learning-based reconstruction produce repeatable, non-synthetic multi-contrast images and parametric maps simultaneously. This article is protected by copyright. All rights reserved.

Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network

OBJECTIVE

The generation of quantification maps and weighted images in synthetic MRI techniques is based on complex fitting equations. This process requires longer image generation times. The objective of this study is to evaluate the feasibility of deep learning method for fast reconstruction of synthetic MRI.

APPROACH

A total of 44 healthy subjects were recruited and random divided into a training set (30 subjects) and a testing set (14 subjects). A multiple-dynamic, multiple-echo (MDME) sequence was used to acquire synthetic MRI images. Quantification maps (T1, T2, and proton density (PD) maps) and weighted (T1W, T2W, and T2W FLAIR) images were created with MAGiC software and then used as the ground truth images in the deep learning (DL) model. An improved multichannel U-Net structure network was trained to generate quantification maps and weighted images from raw synthetic MRI imaging data (8 module images). Quantitative evaluation was performed on quantification maps. Quantitative evaluation metrics, as well as qualitative evaluation were used in weighted image evaluation. Nonparametric Wilcoxon signed-rank tests were performed in this study.

MAIN RESULTS

The results of quantitative evaluation show that the error between the generated quantification images and the reference images is small. For weighted images, no significant difference in overall image quality or SNR was identified between DL images and synthetic images. Notably, the DL images achieved improved image contrast with T2W images, and fewer artifacts were present on DL images than synthetic images acquired by T2W FLAIR.

SIGNIFICANCE

The DL algorithm provides a promising method for image generation in synthetic MRI techniques, in which every step of the calculation can be optimized and faster, thereby simplifying the workflow of synthetic MRI techniques.

A New Method for Cartilage Evaluation in Femoroacetabular Impingement Using Quantitative T2 Magnetic Resonance Imaging: Preliminary Validation against Arthroscopic Findings

OBJECTIVE

The outcome of arthroscopic treatment for femoroacetabular impingement (FAI) depends on the preoperative status of the hip cartilage. Quantitative T2 can detect early biochemical cartilage changes, but its routine implementation is challenging. Furthermore, intrinsic T2 variability between patients makes it difficult to define a threshold to identify cartilage lesions. To address this, we propose a normalized T2-index as a new method to evaluate cartilage in FAI.

DESIGN

We retrospectively analyzed magnetic resonance imaging (MRI) data of 18 FAI patients with arthroscopically confirmed cartilage defects. Cartilage T2 maps were reconstructed from multi-spin-echo 3-T data using the echo-modulation-curve (EMC) model-based technique. The central femoral cartilage, assumed healthy in early-stage FAI, was used as the normalization reference to define a T2-index. We investigated the ability of the T2-index to detect surgically confirmed cartilage lesions.

RESULTS

The average T2-index was 1.14 ± 0.1 and 1.13 ± 0.1 for 2 separated segmentations. Using T2-index >1 as the threshold for damaged cartilage, accuracy was 88% and 100% for the 2 segmentations. We found moderate intraobserver repeatability, although separate segmentations yielded comparable accuracy. Damaged cartilage could not be identified using nonnormalized average T2 values.

CONCLUSIONS

This preliminary study confirms the importance of normalizing T2 values to account for interpatient variability and suggests that the T2-index is a promising biomarker for the detection of cartilage lesions in FAI. Future work is needed to confirm that combining T2-index with morphologic MRI and other quantitative biomarkers could improve cartilage assessment in FAI.

3Tesla post-mortem MRI quantification of anatomical brain structures

Quantitative post-mortem magnetic resonance imaging (PMMR) allows for measurement of T1 and T2 relaxation times and proton density (PD) of brain tissue. Quantitative PMMR values may be used for advanced post-mortem neuro-imaging diagnostics such as computer aided diagnosis. So far, the quantitative T1, T2 and PD post-mortem values of regular anatomical brain structures were unknown for a 3 Tesla PMMR application. The goal of this basic research study was to evaluate the quantitative values of post-mortem brain structures for a 3 T post-mortem magnetic resonance application with regard to various corpse temperatures. In 50 forensic cases, a quantitative PMMR brain sequence was applied prior to autopsy. Measurements of T1 (in ms), T2 (in ms), and PD (in %) values of cerebrum (Group 1: frontal grey matter, frontal white matter, thalamus, caudate nucleus, globus pallidus, putamen, internal capsule) brainstem and cerebellum (Group 2: cerebral peduncle, substantia nigra, red nucleus, pons, middle cerebellar peduncle, cerebellar hemisphere, medulla oblongata) were conducted in synthetically calculated axial PMMR brain images. Assessed quantitative values were corrected for corpse temperature. Temperature dependence was observed mainly for T1 values. ANOVA testing resulted in significant differences of quantitative values between the investigated anatomical brain structures in both groups. It can be concluded that temperature corrected 3 Tesla PMMR T1, T2 and PD values are feasible for characterization and discrimination of regular anatomical brain structures. This may provide a base for future advanced diagnostics of forensically relevant brain lesions and pathology.

A preliminary study of synthetic magnetic resonance imaging in rectal cancer: imaging quality and preoperative assessment

PURPOSE

To compare the imaging quality, T stage and extramural venous invasion (EMVI) evaluation between the conventional and synthetic T2-weighted imaging (T2WI), and to investigate the role of quantitative values obtained from synthetic magnetic resonance imaging (MRI) for assessing nodal staging in rectal cancer (RC).

METHODS

Ninety-four patients with pathologically proven RC who underwent rectal MRI examinations including synthetic MRI were retrospectively recruited. The image quality of conventional and synthetic T2WI was compared regarding signal-to-noise ratio (SNR), contrast-to-noise (CNR), sharpness of the lesion edge, lesion conspicuity, absence of motion artifacts, and overall image quality. The accuracy of T stage and EMVI evaluation on conventional and synthetic T2WI were compared using the Mc-Nemar test. The quantitative T1, T2, and PD values were used to predict the nodal staging of MRI-evaluated node-negative RC.

RESULTS

There were no statistically significant differences between conventional and synthetic T2WI in SNR, CNR, overall image quality, lesion conspicuity, and absence of motion artifacts (p = 0.058-0.978). There were no significant differences in the diagnostic accuracy of T stage and EMVI between conventional and synthetic T2WI from two observers (p = 0.375 and 0.625 for T stage; p = 0.625 and 0.219 for EMVI). The T2 value showed good diagnostic performance for predicting the nodal staging of RC with the area under the receiver operating characteristic, sensitivity, specificity, and accuracy of 0.854, 90.0%, 71.4%, and 80.3%, respectively.

CONCLUSIONS

Synthetic MRI may facilitate preoperative staging and EMVI evaluation of RC by providing synthetic T2WI and quantitative maps in one acquisition.

A multi-inversion multi-echo spin and gradient echo echo planar imaging sequence with low image distortion for rapid quantitative parameter mapping and synthetic image contrasts

PURPOSE

Brain imaging exams typically take 10-20 min and involve multiple sequential acquisitions. A low-distortion whole-brain echo planar imaging (EPI)-based approach was developed to efficiently encode multiple contrasts in one acquisition, allowing for calculation of quantitative parameter maps and synthetic contrast-weighted images.

METHODS

Inversion prepared spin- and gradient-echo EPI was developed with slice-order shuffling across measurements for efficient acquisition with T1 , T2 , and T 2 ∗ weighting. A dictionary-matching approach was used to fit the images to quantitative parameter maps, which in turn were used to create synthetic weighted images with typical clinical contrasts. Dynamic slice-optimized multi-coil shimming with a B0 shim array was used to reduce B0 inhomogeneity and, therefore, image distortion by >50%. Multi-shot EPI was also implemented to minimize distortion and blurring while enabling high in-plane resolution. A low-rank reconstruction approach was used to mitigate errors from shot-to-shot phase variation.

RESULTS

The slice-optimized shimming approach was combined with in-plane parallel-imaging acceleration of 4× to enable single-shot EPI with more than eight-fold distortion reduction. The proposed sequence efficiently obtained 40 contrasts across the whole-brain in just over 1 min at 1.2 × 1.2 × 3 mm resolution. The multi-shot variant of the sequence achieved higher in-plane resolution of 1 × 1 × 4 mm with good image quality in 4 min. Derived quantitative maps showed comparable values to conventional mapping methods.

CONCLUSION

The approach allows fast whole-brain imaging with quantitative parameter maps and synthetic weighted contrasts. The slice-optimized multi-coil shimming and multi-shot reconstruction approaches result in minimal EPI distortion, giving the sequence the potential to be used in rapid screening applications.

Preoperative evaluation of extramural venous invasion in rectal cancer using radiomics analysis of relaxation maps from synthetic MRI

OBJECTIVE

To establish a radiomics nomogram based on relaxation maps for predicting the extramural venous invasion (EMVI) of rectal cancer (RC) and compare the diagnostic efficacy of the nomogram and subjective assessment by radiologists.

MATERIAL AND METHODS

Among 94 RC patients receiving direct surgical resection, 65 were randomly allocated to the training cohort and 29 to the validation cohort. Radiomics features were extracted from synthetic magnetic resonance imaging including T1, T2, and proton density (PD) maps. The least absolute shrinkage and selection operator methods were used for dimension reduction, feature selection, and radiomics model building. Multivariable logistic regression analysis was used for nomogram development. The performance of the nomogram was assessed with respect to its calibration, receiver operating characteristics (ROC) curve, and decision curve analysis.

RESULTS

The radiomics model demonstrated good predictive efficacy for EMVI, with an area under the ROC curve (AUC), sensitivity, and specificity of 0.912 (95% confidence interval (CI), 0.837-0.986), 0.824, and 0.875 in the training cohort and 0.877 (95% CI 0.751-1.000), 0.833, and 0.826 in the validation cohort. The nomogram had good diagnostic performance, with AUCs of 0.925 (95% CI 0.862-0.988) and 0.899 (95% CI 0.782-1.000) in the training and validation cohort. Furthermore, the radiomics signature showed better diagnostic efficiency than the subjective assessment by both readers (AUC =0.912 vs. 0.732 and 0.763, P = 0.023 and 0.028, respectively).

CONCLUSION

A radiomics nomogram was developed to preoperatively predict EMVI in RC patients. The application of the radiomics model based on relaxation maps could improve the diagnostic efficacy of EMVI.

A multi-dimensional integration (MDI) strategy for MR T2 * mapping

MRI signals are intrinsically multi-dimensional, and signal behavior may be orthogonal among different dimensions. Such dimensional orthogonality can be utilized to eliminate unwanted effects and facilitate mathematical simplicity during image processing for improved outcomes. In this work, we will demonstrate and analyze the principles and performance of a newly developed multi-dimensional integration (MDI) strategy in MR T₂ * mapping. By constructing a complex signal function to extract the inter-echo signal changes, MDI solves an optimization problem by processing all signal dimensions (eg echoes, flip angles and coil channels) in one integrative step. MDI was compared with routine curve fitting methods on noise behavior, quantification accuracy and computational efficiency. All methods were tested and compared on simulation, phantom and knee data. Monte Carlo simulations were performed on simulation and all MRI data to investigate noise propagation from k space to T₂ * maps. For phantom tests, T₂ * values in regions of interest were extracted on a voxel-wise basis and analyzed using a paired t-test between scanning parameters and mapping methods, with p < 0.05 being significantly different. MDI facilitated a straightforward processing procedure, yielding homogeneous, high-signal-to-noise-ratio (SNR) and artifact-free T₂ * maps without explicit coil combination or additional measures. Compared with routine fitting methods, MDI offered significantly (p < 0.05) improved SNR and quantitative accuracy/robustness, with two to three orders higher computational efficiency. MDI also represented low-SNR signals with low T₂ * values, avoiding misinterpretation with long-T₂ * species.

Triple-negative breast cancer on contrast-enhanced MRI and synthetic MRI: A comparison with non-triple-negative breast carcinoma

PURPOSE

This study aimed to compare the characteristics of triple-negative breast cancer (TNBC) with non-TNBC on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and synthetic MRI.

METHOD

This retrospective study included 79 patients with histopathologically proven breast cancer (TNBC: 16, non-TNBC: 63) who underwent synthetic MRI. Using synthetic MR images, we obtained T1 and T2 relaxation times in breast lesions before (Pre-T1, Pre-T2, Pre-PD) and after (Gd-T1, Gd-T2, Gd-PD) contrast agent injection. Subsequently, we calculated the ΔT1 (Pre-T1 - Gd-T1), ΔT2 (Pre-T2 - Gd-T2), Pre-T1/T2, and Gd-T1/T2. We compared the aforementioned quantitative values, as well as several morphologic features between TNBCs and non-TNBCs that were identified on DCE-MRI.

RESULTS

The multivariate analyses revealed that the Pre-T2 (P = 0.037) and the presence of rim enhancement (P-RIM) (P = 0.034) were significant and independent predictors of TNBC. The area under the receiver operating characteristics curve for all breast cancers was greater when a combination of Pre-T2 and P-RIM (Pre-T2＋P-RIM; Method 3, AUC (area under the curve) = 0.858) was used to distinguish between TNBCs and non-TNBCs versus the use of either Pre-T2 alone (Method 1, AUC = 0.786) or P-RIM alone (Method 2, AUC = 0.747).

CONCLUSIONS

Pre-T2 obtained using synthetic MRI and P-RIM identified on DCE-MRI allowed the differentiation between TNBCs and non-TNBCs. However, these results are preliminary and need to be verified by further studies.

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence-Based Fat-Suppressed Imaging Feasible?

MATERIALS AND METHODS

This single-center study was approved by the institutional review board. Artificial intelligence-based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network-based U-Net that used a training set of 25,920 images and validation set of 16,416 images. Three musculoskeletal radiologists reviewed 88 knee MR studies in 2 sessions, the original (proton density [PD] + FSPD) and the synthetic (PD + AFSMRI). Readers recorded AFSMRI quality (diagnostic/nondiagnostic) and the presence or absence of meniscal, ligament, and tendon tears; cartilage defects; and bone marrow abnormalities. Contrast-to-noise rate measurements were made among subcutaneous fat, fluid, bone marrow, cartilage, and muscle. The original MRI sequences were used as the reference standard to determine the diagnostic performance of AFSMRI (combined with the original PD sequence). This is a fully balanced study design, where all readers read all images the same number of times, which allowed the determination of the interchangeability of the original and synthetic protocols. Descriptive statistics, intermethod agreement, interobserver concordance, and interchangeability tests were applied. A P value less than 0.01 was considered statistically significant for the likelihood ratio testing, and P value less than 0.05 for all other statistical analyses.

RESULTS

Artificial intelligence-based FS MRI quality was rated as diagnostic (98.9% [87/88] to 100% [88/88], all readers). Diagnostic performance (sensitivity/specificity) of the synthetic protocol was high, for tears of the menisci (91% [71/78], 86% [84/98]), cruciate ligaments (92% [12/13], 98% [160/163]), collateral ligaments (80% [16/20], 100% [156/156]), and tendons (90% [9/10], 100% [166/166]). For cartilage defects and bone marrow abnormalities, the synthetic protocol offered an overall sensitivity/specificity of 77% (170/221)/93% (287/307) and 76% (95/125)/90% (443/491), respectively. Intermethod agreement ranged from moderate to substantial for almost all evaluated structures (menisci, cruciate ligaments, collateral ligaments, and bone marrow abnormalities). No significant difference was observed between methods for all structural abnormalities by all readers (P > 0.05), except for cartilage assessment. Interobserver agreement ranged from moderate to substantial for almost all evaluated structures. Original and synthetic protocols were interchangeable for the diagnosis of all evaluated structures. There was no significant difference for the common exact match proportions for all combinations (P > 0.01). The conspicuity of all tissues assessed through contrast-to-noise rate was higher on AFSMRI than on original FSPD images (P < 0.05).

CONCLUSIONS

Artificial intelligence-based FS MRI (3D AFSMRI) is feasible and offers a method for fast imaging, with similar detection rates for structural abnormalities of the knee, compared with original 3D MR sequences.

Magnetic resonance fingerprinting: an overview

Magnetic resonance fingerprinting (MRF) is an evolving quantitative MRI framework consisting of unique data acquisition, processing, visualization, and interpretation steps. MRF is capable of simultaneously producing multiple high-resolution property maps including T1, T2, M0, ADC, and T2* measurements. While a relatively new technology, MRF has undergone rapid development for a variety of clinical applications from brain tumor characterization and epilepsy imaging to characterization of prostate cancer, cardiac imaging, among others. This paper will provide a brief overview of current state of MRF technology including highlights of technical and clinical advances. We will conclude with a brief discussion of the challenges that need to be overcome to establish MRF as a quantitative imaging biomarker.

Simultaneous Multislice for Accelerating Diffusion MRI in Clinical Neuroradiology Protocols

BACKGROUND AND PURPOSE

Diffusion MR imaging sequences essential for clinical neuroradiology imaging protocols may be accelerated with simultaneous multislice acquisitions. We tested whether simultaneous multislice-accelerated diffusion data were clinically equivalent to standard acquisitions.

MATERIALS AND METHODS

In this retrospective study, clinical diffusion sequences obtained before and after implementation of 2-slice simultaneous multislice acceleration and an altered diffusion gradient sampling scheme using the same 3T MRI scanner and 20-channel coil (n = 25 per group) were independently and blindly evaluated by 2 neuroradiologists for perceived quality, artifacts, and overall diagnostic utility. Diffusion tractography was performed in 13 patients both with and without 2-slice simultaneous multislice acceleration (b = 0, 1000, 2000 s/mm²; 60 directions). The corticospinal tract and arcuate fasciculus ipsilateral to the lesion were generated using the same ROIs and then blindly assessed by a neurosurgeon for anatomic fidelity, perceived quality, and impact on surgical management. Tract volumes were compared for spatial overlap.

RESULTS

Two-slice simultaneous multislice diffusion reduced acquisition times from 141 to 45 seconds for routine diffusion and from 7.5 to 5.9 minutes for diffusion tractography using 3T MR imaging. The simultaneous multislice-accelerated diffusion sequence was rated equivalent for diagnostic utility despite reductions to perceived image quality. Simultaneous multislice-accelerated diffusion tractography was rated clinically equivalent. Dice similarity coefficients between routine and simultaneous multislice-generated corticospinal tract and arcuate fasciculus tract volumes were 0.78 (SD, 0.03) and 0.71 (SD, 0.05), respectively.

CONCLUSIONS

Two-slice simultaneous multislice diffusion appeared clinically equivalent for standard acquisitions and diffusion tractography. Simultaneous multislice makes it feasible to acquire higher angular and q-space-resolution diffusion acquisitions required for translating advanced diffusion models into clinical practice.

Application of T1 Map Information Based on Synthetic MRI for Dynamic Contrast-Enhanced Imaging: A Comparison Study with the Fixed Baseline T1 Value Method

Objective

For an accurate dynamic contrast-enhanced (DCE) MRI analysis, exact baseline T1 mapping is critical. The purpose of this study was to compare the pharmacokinetic parameters of DCE MRI using synthetic MRI with those using fixed baseline T1 values.

Materials and Methods

This retrospective study included 102 patients who underwent both DCE and synthetic brain MRI. Two methods were set for the baseline T1: one using the fixed value and the other using the T1 map from synthetic MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and the volume of the extravascular extracellular space (v_e) were compared between the two methods. The interclass correlation coefficients and the Bland-Altman method were used to assess the reliability.

Results

In normal-appearing frontal white matter (WM), the mean values of K^trans, v_e, and v_p were significantly higher in the fixed value method than in the T1 map method. In the normal-appearing occipital WM, the mean values of v_e and v_p were significantly higher in the fixed value method. In the putamen and head of the caudate nucleus, the mean values of K^trans, v_e, and v_p were significantly lower in the fixed value method. In addition, the T1 map method showed comparable interobserver agreements with the fixed baseline T1 value method.

Conclusion

The T1 map method using synthetic MRI may be useful for reflecting individual differences and reliable measurements in clinical applications of DCE MRI.

Histogram analysis of quantitative parameters from synthetic MRI: Correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer

PURPOSE

To evaluate intra-tumoral heterogeneity through a histogram analysis of quantitative parameters obtained from synthetic MRI (magnetic resonance imaging), and determine correlations of these histogram characteristics with prognostic factors and molecular subtypes of invasive ductal carcinoma (IDC).

METHODS

A total of 122 IDC from 122 women who underwent preoperative synthetic MRI and DCE (dynamic contrast enhancement)-MRI were investigated. The synthetic MRI parameters (T1, T2, and PD (proton density)) were obtained. For each parameter, the minimum, 10th percentile, mean, median, 90th percentile, maximum, skewness, and kurtosis values of tumor were calculated, and correlations with prognostic factors and subtypes were assessed. The Mann-Whitney U test or the Student^'s t test were utilized to analyze the association between the histogram features of synthetic MRI parameters and prognostic factors. The Kruskal-Wallis test followed by the post-hoc test was used to analyze differences of synthetic MRI parameters among molecular subtypes.

RESULTS

IDC with high histopathologic grade showed statistically higher PD_maxium, T1_mean and T1_median values than those with low grade (p = 0.003, p = 0.007, p = 0.003). The T1_10th were significantly higher in cancers with PR (progesterone receptor) negativity than those with PR positivity (p = 0.005). ER-negative cancers had significant higher values of T2_10th, T2_mean, and T2_median than ER-positive cancers (p = 0.006, 0.002, and 0.006, respectively). The values of PD_median were significantly higher in IDC with HER2 (human epidermal growth factor receptor 2) positivity than those with HER2 negativity (p = 0.001). When discriminating molecular subtypes of IDC, the T2_mean achieved the highest performance. The T2_mean values of TN (triple-negative), luminal B and luminal A types are arranged in descending order (p <  0.0001).

CONCLUSIONS

Histogram features derived from synthetic MRI quantifies the distributions of tissue relaxation time and proton density, and may serve as a potential biomarker for discriminating histopathological grade, hormone receptor status, HER2 expression status and breast cancer subtypes.

Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and reproducibility study

PURPOSE

To compare the bias and inherent reliability of the quantitative (T₁ and T₂ ) imaging metrics generated from the magnetic resonance fingerprinting (MRF) technique using the ISMRM/NIST system phantom in an international multicenter setting.

METHOD

ISMRM/NIST MRI system phantom provides standard reference T₁ and T₂ relaxation values (vendor-provided) for each of the 14 vials in T₁ and T₂ arrays. MRF-SSFP scans repeated over 30 days on GE 1.5 and 3.0 T scanners at three collaborative centers. MRF estimated T_1, and T₂ values averaged over 30 days were compared with the phantom vendor-provided and spin-echo (SE) based convention gold standard (GS) method. Repeatability and reproducibility were characterized by the within-case coefficient of variation (wCV) of the MRF data acquired over 30 days, along with the biases.

RESULT

For the wide ranges of MRF estimated T₁ values, vials #1-8 (T₁ relaxation time between 2033 and 184 ms) exhibited a wCV less than 3% and 4%, respectively, on 3.0 and 1.5 T scanners. T₂ values in vials #1-8 (T₂ relaxation, 1044-45 ms) have shown wCV to be <7% on both 3.0 and 1.5 T scanners. A stronger linear correlation overall for T₁ (R²  = 0.9960 and 0.9963 at center-1 and center-2 on 3.0 T scanner, and R²  = 0.9951 and 0.9988 at center-1 and center-3 on 1.5 T scanner) compared to T₂ (R²  = 0.9971 and 0.9972 at center-1 and center-2 on 3.0 T scanner, and R²  = 0.9815 and 0.9754 at center-1 and center-3 on 1.5 T scanner). Bland-Altman (BA) analysis showed MRF based T₁ and T₂ values were within the limit of agreement (LOA) except for one data point. The mean difference or bias and 95% lower bound (LB) and upper bound (UB) LOA are reported in the format; mean bias: 95% LB LOA: 95% UB LOA. The biases for T₁ values were 21.34: -50.00: 92.69, 21.32: -47.29: 89.94 ms, and for T₂ values were -19.88: -42.37: 2.61, -19.06: -43.58: 5.45 ms on 3.0 T scanner at center-1 and center-2, respectively. Similarly, on 1.5 T scanner biases for T₁ values were 26.54: -53.41: 106.50, 9.997: -51.94: 71.94 ms, and for T₂ values were -23.84: -135.40: 87.76, -37.30: 134.30: 59.73 ms at center-1 and center-3, respectively. Additionally, the correlation between the SE based GS and MRF estimated T₁ and T₂ values (R²  = 0.9969 and 0.9977) showed a similar trend as we observed between vendor-provided and MRF estimated T₁ and T₂ values (R²  = 0.9963 and 0.9972). In addition to correlation, BA analysis showed that all the vials are within the LOA between the GS and vendor-provided for the T₁ values and except one vial for T₂ . All the vials are within the LOA between GS and MRF except one vial in T₁ and T₂ array. The wCV for reproducibility was <3% for both T₁ and T₂ values in vials #1-8, for all the 14 vials, wCV calculated for reproducibility was <4% for T₁ values and <5% for T₂ .

CONCLUSION

This study shows that MRF is highly repeatable (wCV <4% for T₁ and <7% for T₂ ) and reproducible (wCV < 3% for both T₁ and T₂ ) in certain vials (vials #1-8).

Simultaneous quantification of SPIO and gadolinium contrast agents using MR fingerprinting

PURPOSE

Develop a magnetic resonance fingerprinting (MRF) methodology with R₂^∗ quantification, intended for use with simultaneous contrast agent concentration mapping, particularly gadolinium (Gd) and iron labelled CD8+ T cells.

METHODS

Variable-density spiral SSFP MRF was used, modified to allow variable TE, and with an exp.(-TE·R₂^∗) dictionary modulation. In vitro phantoms containing SPIO labelled cells and/or gadolinium were used to validate parameter maps, probe undersampling capacity, and verify dual quantification capabilities. A C57BL/6 mouse was imaged using MRF to demonstrate acceptable in vivo resolution and signal at 8× undersampling necessary for a 25-min scan.

RESULTS

Strong agreement was found between conventional and MRF-derived values for R₁, R₂, and R₂^∗. Expanded MRF allowed quantification of iron-loaded CD8+ T cells. Results were robust to 8× undersampling and enabled recreation of relaxation profiles for both a Gd agent and iron labelled cells simultaneously. In vivo data demonstrated sufficient SNR in undersampled data for parameter mapping to visualise key features.

CONCLUSION

MRF can be expanded to include R₁, R₂, and R₂^∗ mapping required for simultaneous quantification of gadolinium and SPIO in vitro, allowing for potential implementation of a variety of future in vivo studies using dual MR contrast agents, including molecular imaging of labelled cells.

Prediction of pathological prognostic factors of rectal cancer by relaxation maps from synthetic magnetic resonance imaging

PURPOSE

To explore the feasibility of relaxation maps from synthetic MRI for predicting pathological prognostic factors of rectal cancer (RC) and to compare the predictive performance of quantitative values and conventional subjective evaluation.

MATERIAL AND METHODS

A total of 94 patients with pathologically proven RC who underwent direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI including synthetic MRI was performed. The mean T₁, T₂, and PD value of the whole tumor was obtained to preoperatively assess the pathological T stage, N stage, extramural venous invasion (EMVI), differentiation, and perineural invasion. Receiver operating characteristic curves were used to explore the predictive performance for assessing the prognostic factors. The T stage, N stage and EMVI status on conventional T2WI were evaluated and compared with the quantitative values.

RESULTS

The T₂ value decreased significantly in patients with positive perineural invasion, lymph node metastasis (LNM), EMVI, and higher T stage RC (p =  0.007 and < 0.001). The T₁ value of LNM and EMVI positive groups was significantly lower than those of the negative groups (p =  0.034 and 0.011). For predicting N stage and EMVI, the T₂ value demonstrated good performance with an AUC of 0.883 (95 % confidence interval, CI, 0.801-0.940) and 0.821 (95 % CI, 0.729-0.893); the T₂ value was superior to the T₁ value and subjective evaluation of radiologists (all p < 0.05).

CONCLUSION

Synthetic MRI is a promising tool for noninvasive evaluation of prognostic factors of RC by generating relaxation maps.