Image quality of the CAIPIRINHA-Dixon-TWIST-VIBE technique for ultra-fast breast DCE-MRI: Comparison with the conventional GRE technique

Evaluation of Middle Cerebral Artery Culprit Plaque Inflammation in Ischemic Stroke Using CAIPIRINHA-Dixon-TWIST Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

Middle cerebral artery (MCA) plaques are a leading cause of ischemic stroke (IS). Plaque inflammation is crucial for plaque stability and urgently needs quantitative detection.

PURPOSE

To explore the utility of Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA)-Dixon-Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) (CDT) dynamic contrast-enhanced MRI (DCE-MRI) for evaluating MCA culprit plaque inflammation changes over stroke time and with diabetes mellitus (DM).

STUDY TYPE

Prospective.

POPULATION

Ninety-four patients (51.6 ± 12.23 years, 32 females, 23 DM) with acute IS (AIS; N = 43) and non-acute IS (non-AIS; 14 days < stroke time ≤ 3 months; N = 51).

FIELD STRENGTH/SEQUENCE

3-T, CDT DCE-MRI and three-dimensional (3D) Sampling Perfection with Application optimized Contrast using different flip angle Evolution (3D-SPACE) T1-weighted imaging (T1WI).

ASSESSMENT

Stroke time (from initial IS symptoms to MRI) and DM were registered. For 94 MCA culprit plaques, Ktrans from CDT DCE-MRI and enhancement ratio (ER) from 3D-SPACE T1WI were compared between groups with and without AIS and DM.

STATISTICAL TESTS

Shapiro-Wilk test, Bland-Altman analysis, Passing and Bablok test, independent t-test, Mann-Whitney U test, Chi-squared test, Fisher's exact test, receiver operating characteristics (ROC) with the area under the curve (AUC), DeLong's test, and Spearman rank correlation test with the P-value significance level of 0.05.

RESULTS

Ktrans and ER of MCA culprit plaques were significantly higher in AIS than non-AIS patients (Ktrans = 0.098 s-1 vs. 0.037 s-1; ER = 0.86 vs. 0.55). Ktrans showed better AUC for distinguishing AIS from non-AIS patients (0.87 vs. 0.75) and stronger negative correlation with stroke time than ER (r = -0.60 vs. -0.34). DM patients had significantly higher Ktrans and ER than non-DM patients in IS and AIS groups.

DATA CONCLUSION

Imaging by CDT DCE-MRI may allow to quantitatively evaluate MCA culprit plaques over stroke time and DM.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

The Diagnostic Value of Conventional MRI Combined With Diffusion-Weighted Imaging in Microprolactinomas

BACKGROUND

Turbo spin-echo (TSE) diffusion-weighted imaging (DWI) sequences may reduce susceptibility artifacts and image distortion in sellar region, allowing better visualization of small pituitary lesions, and may be used to assist in the diagnosis of pituitary microadenomas.

PURPOSE

To explore the application value of conventional MRI combined with DWI sequences in the diagnosis of microprolactinomas.

STUDY TYPE

Prospective.

POPULATION

Thirty-four patients in microprolactinomas with high signal on T2WI (HT2-PRL) group (34 females, 34 ± 7 years), 26 patients in microprolactinomas with equal or low signal on T2WI (ELT2-PRL) group (21 females, 34 ± 7 years), 35 patients with hyperprolactinemia (33 females, 32 ± 8 years), and 30 normal controls (25 females, 31 ± 7 years).

FIELD STRENGTH/SEQUENCE

TSE sequence at 3 T.

ASSESSMENT

Pituitary morphological parameters (such as length and volume), dynamic contrast-enhanced parameters (such as time to peak) and the apparent diffusion coefficients (ADCs) were measured in each group.

STATISTICAL TESTS

ANOVA and Mann-Whitney U test were used to compare parameters among groups. Spearman's coefficient was used to evaluate the correlation between variables. ROC analysis was used to assess the performance of the parameters. A P-value <0.05 was considered statistically significant.

RESULTS

The pituitary volume of patients in HT2-PRL, ELT2-PRL, and hyperprolactinemia group were 831.00 (747.60, 887.60), 923.63 ± 219.34, and 737.20 (606.40, 836.80) mm3. The pituitary maximum height in these three groups were 7.03 (6.43, 8.63), 8.03 ± 1.41, and 6.63 ± 1.28 mm, respectively. The lesion ADC value was significantly correlated with T2 relative signal intensity (the ratio of signal intensity of microprolactinoma or anterior pituitary to left temporal cortex) (r = 0.821). Compared with patients with hyperprolactinemia, the diagnostic efficacy of T2 relative signal intensity was higher in HT2-PRL group, with an AUC of 0.954, whereas the ADC value was the highest in ELT2-PRL group, with an AUC of 0.924.

CONCLUSION

DWI sequences can be used to assist in the diagnosis of pituitary microadenomas.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PURPOSE

To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer.

METHODS

This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses.

RESULTS

kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944).

CONCLUSION

The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Detection of arterial phase hyperenhancement of small hepatocellular carcinoma with MRI: Comparison between single arterial and multi-arterial phases and between extracellular and hepatospecific contrast agents

PURPOSE

The purpose of this study was to compare the detection rate of arterial phase hyperenhancement (APHE) in small hepatocellular carcinoma (HCC) between single arterial phase (single-AP) and triple hepatic arterial (triple-AP) phase MRI and between extracellular (ECA) and hepato-specific (HBA) contrast agents.

MATERIALS AND METHODS

A total of 109 cirrhotic patients with 136 HCCs from seven centers were included. There were 93 men and 16 women, with a mean age of 64.0 ± 8.9 (standard deviation) years (range: 42-82 years). Each patient underwent both ECA-MRI and HBA (gadoxetic acid)-MRI examination within one month of each other. Each MRI examination was retrospectively reviewed by two readers blinded to the second MRI examination. The sensitivities of triple- and single-AP for the detection of APHE were compared, and each phase of the triple-AP sequence was compared with the other two.

RESULTS

No differences in APHE detection were found between single-AP (97.2%; 69/71) and triple-AP (98.5%; 64/65) (P > 0.99) at ECA-MRI. No differences in APHE detection were found between single-AP (93%; 66/71) and triple-AP (100%; 65/65) at HBA-MRI (P = 0.12). Patient age, size of the nodules, use of automatic triggering, type of contrast agent, and type of sequence were not significantly associated with APHE detection. The reader was the single variable significantly associated with APHE detection. For triple-AP, best APHE detection rate was found for early and middle-AP images compared to late-AP images (P = 0.001 and P = 0.003). All APHEs were detected with the combination of early-AP and middle-AP images, except one that was detected on late-AP images by one reader.

CONCLUSION

Our study suggests that both single- and triple-AP can be used in liver MRI for the detection of small HCC especially when using ECA. Early AP and middle-AP are the most efficient phases and should be preferred for detecting APHE, regardless of the contrast agent used.

Quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging to predict lymphovascular invasion and survival outcome in breast cancer

BACKGROUND

Lymphovascular invasion (LVI) predicts a poor outcome of breast cancer (BC), but LVI can only be postoperatively diagnosed by histopathology. We aimed to determine whether quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can preoperatively predict LVI and clinical outcome of BC patients.

METHODS

A total of 189 consecutive BC patients who underwent multiparametric MRI scans were retrospectively evaluated. Quantitative (K^trans, V_e, K_ep) and semiquantitative DCE-MRI parameters (W_- in, W_- out, TTP), and clinicopathological features were compared between LVI-positive and LVI-negative groups. All variables were calculated by using univariate logistic regression analysis to determine the predictors for LVI. Multivariate logistic regression was used to build a combined-predicted model for LVI-positive status. Receiver operating characteristic (ROC) curves evaluated the diagnostic efficiency of the model and Kaplan-Meier curves showed the relationships with the clinical outcomes. Multivariate analyses with a Cox proportional hazard model were used to analyze the hazard ratio (HR) for recurrence-free survival (RFS) and overall survival (OS).

RESULTS

LVI-positive patients had a higher K_ep value than LVI-negative patients (0.92 ± 0.30 vs. 0.81 ± 0.23, P = 0.012). N2 stage [odds ratio (OR) = 3.75, P = 0.018], N3 stage (OR = 4.28, P = 0.044), and K_ep value (OR = 5.52, P = 0.016) were associated with LVI positivity. The combined-predicted LVI model that incorporated the N stage and K_ep yielded an accuracy of 0.735 and a specificity of 0.801. The median RFS was significantly different between the LVI-positive and LVI-negative groups (31.5 vs. 34.0 months, P = 0.010) and between the combined-predicted LVI-positive and LVI-negative groups (31.8 vs. 32.0 months, P = 0.007). The median OS was not significantly different between the LVI-positive and LVI-negative groups (41.5 vs. 44.0 months, P = 0.270) and between the combined-predicted LVI-positive and LVI-negative groups (42.8 vs. 43.5 months, P = 0.970). LVI status (HR = 2.40), N2 (HR = 3.35), and the combined-predicted LVI model (HR = 1.61) were independently associated with disease recurrence.

CONCLUSION

The quantitative parameter of K_ep could predict LVI. LVI status, N stage, and the combined-predicted LVI model were predictors of a poor RFS but not OS.

A guideline for 3D printing terminology in biomedical research utilizing ISO/ASTM standards

First patented in 1986, three-dimensional (3D) printing, also known as additive manufacturing or rapid prototyping, now encompasses a variety of distinct technology types where material is deposited, joined, or solidified layer by layer to create a physical object from a digital file. As 3D printing technologies continue to evolve, and as more manuscripts describing these technologies are published in the medical literature, it is imperative that standardized terminology for 3D printing is utilized. The purpose of this manuscript is to provide recommendations for standardized lexicons for 3D printing technologies described in the medical literature. For all 3D printing methods, standard general ISO/ASTM terms for 3D printing should be utilized. Additional, non-standard terms should be included to facilitate communication and reproducibility when the ISO/ASTM terms are insufficient in describing expository details. By aligning to these guidelines, the use of uniform terms for 3D printing and the associated technologies will lead to improved clarity and reproducibility of published work which will ultimately increase the impact of publications, facilitate quality improvement, and promote the dissemination and adoption of 3D printing in the medical community.

Clinical Feasibility of Reduced Field-of-View Diffusion-Weighted Magnetic Resonance Imaging with Computed Diffusion-Weighted Imaging Technique in Breast Cancer Patients

Background: We evaluated the feasibility of the reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with computed DWI technique by comparison and analysis of the inter-method agreement among acquired rFOV DWI (rFOVA), rFOV DWI with computed DWI technique (rFOVS), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in patients with breast cancer. Methods: A total of 130 patients with biopsy-proven breast cancers who underwent breast MRI from April 2017 to December 2017 were included in this study. The rFOVS were reformatted by calculation of the apparent diffusion coefficient curve obtained from rFOVA b = 0 s/mm² and b = 500 s/mm². Visual assessment of the image quality of rFOVA b = 1000 s/mm², rFOVS, and DCE MRI was performed using a four-point grading system. Morphologic analyses of the index cancer was performed on rFOVA, rFOVS, and DCE MRI. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and contrast of tumor-to-parenchyma (TPC) were calculated. Results: Image quality scores with rFOVA, rFOVS, and DCE MRI were not significantly different (p = 0.357). Lesion analysis of shape, margin, and size of the index cancer also did not show significant differences among the three sequences (p = 0.858, p = 0.242, and p = 0.858, respectively). SNR, CNR, and TPC of DCE MRI were significantly higher than those of rFOVA and rFOVS (p < 0.001, p = 0.001, and p = 0.016, respectively). Significant differences were not found between the SNR, CNR, and TPC of rFOVA and those of rFOVS (p > 0.999, p > 0.999, and p > 0.999, respectively). Conclusion: The rFOVA and rFOVS showed nearly equivalent levels of image quality required for morphological analysis of the tumors and for lesion conspicuity compared with DCE MRI.