Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice.

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF-DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time-intensity curve. Because kinetic information from UF-DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper-vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF-DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF-DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF-DCE MRI, which offers better lesion-to-noise contrast. The excellent contrast of early-enhancing vessels provides a key to understanding the detailed pathological structure of tumor-associated vessels. UF-DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence-based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF-DCE MRI are summarized. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer.

BACKGROUND: Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. METHODS: Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant. RESULTS: Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001). CONCLUSIONS: Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

Diagnostic performance of different perfusion algorithms for the detection of angiographical spasm.

PURPOSE: To assess the diagnostic utility of different perfusion algorithms for the detection of angiographical terial spasm. METHOD: During a 2-year period, 45 datasets from 29 patients (54.2±10,75y, 20F) with suspected cerebral vasospasm after aneurysmal subarachnoid hemorrhage were included. Volume Perfusion CT (VPCT), Non-enhanced CT (NCT) and angiography were performed within 6hours post-ictus. Perfusion maps were generated using a maximum slope (MS) and a deconvolution-based approach (DC). Two blinded neuroradiologists independently evaluated MS and DC maps regarding vasospasm-related perfusion impairment on a 3-point Likert-scale (0=no impairment, 1=impairment affecting <50%, 2=impairment affecting >50% of vascular territory). A third independent neuroradiologist assessed angiography for presence and severity of arterial narrowing on a 3-point Likert scale (0=no narrowing, 1=narrowing affecting <50%, 2=narrowing affecting>50% of artery diameter). MS and DC perfusion maps were evaluated regarding diagnostic accuracy for angiographical arterial spasm with angiography as reference standard. Correlation analysis of angiography findings with both MS and DC perfusion maps was additionally performed. Furthermor, the agreement between MS and DC and inter-reader agreement was assessed. RESULTS: DC maps yielded significantly higher diagnostic accuracy than MS perfusion maps (DC:AUC=.870; MS:AUC=.805; P=0.007) with higher sensitivity for DC compared to MS (DC:sensitivity=.758; MS:sensitivity=.625). DC maps revealed stronger correlation with angiography than MS (DC: R=.788; MS: R=694;=<0.001). MS and DC showed substantial agreement (Kappa=.626). Regarding inter-reader analysis, (almost) perfect inter-reader agreement was observed for both MS and DC maps (Kappa≥981). CONCLUSION: DC yields significantly higher diagnostic accuracy for the detection of angiographic arterial spasm and higher correlation with angiographic findings compared to MS.

Renal versus splenic maximum slope based perfusion CT modelling in patients with portal-hypertension.

PURPOSE: To assess liver perfusion-CT (P-CT) parameters derived from peak-splenic (PSE) versus peak-renal enhancement (PRE) maximum slope-based modelling in different levels of portal-venous hypertension (PVH). MATERIAL AND METHODS: Twenty-four patients (16 men; mean age 68 ± 10 years) who underwent dynamic P-CT for detection of hepatocellular carcinoma (HCC) were retrospectively divided into three groups: (1) without PVH (n = 8), (2) with PVH (n = 8), (3) with PVH and thrombosis (n = 8). Time to PSE and PRE and arterial liver perfusion (ALP), portal-venous liver perfusion (PLP) and hepatic perfusion-index (HPI) of the liver and HCC derived from PSE- versus PRE-based modelling were compared between the groups. RESULTS: Time to PSE was significantly longer in PVH groups 2 and 3 (P = 0.02), whereas PRE was similar in groups 1, 2 and 3 (P > 0.05). In group 1, liver and HCC perfusion parameters were similar for PSE- and PRE-based modelling (all P > 0.05), whereas significant differences were seen for PLP and HPI (liver only) in group 2 and ALP in group 3 (all P < 0.05). CONCLUSION: PSE is delayed in patients with PVH, resulting in a miscalculation of PSE-based P-CT parameters. Maximum slope-based P-CT might be improved by replacing PSE with PRE-modelling, whereas the difference between PSE and PRE might serve as a non-invasive biomarker of PVH. KEY POINTS: • Peak-splenic enhancement is decreased and delayed in patients with portal-venous hypertension • The maximum-slope method uses PSE to calculate arterial and portal-venous liver perfusion • Peak-renal enhancement (PRE) is insensitive to PVH and might improve perfusion modelling • The difference between PSE and PRE might serve as a non-invasive PVH biomarker.

Maximum slope of ultrafast dynamic contrast-enhanced MRI of the breast: Comparisons with prognostic factors of breast cancer.

PURPOSE: To determine the relationship between the maximum slope (MS) of ultrafast dynamic contrast-enhanced (DCE)-MRI and prognostic factors of breast cancer. METHODS: One hundred thirteen patients with 118 breast cancers were included in this study. The ultrafast DCE sequence was acquired using a higher parallel imaging factor. Its spatial resolution was 0.9 × 0.9 × 2.5 mm and its temporal resolution was 8.3 s/phase. Each lesion was automatically segmented, and the ROI of highest enhancement in the lesion was identified. In this ROI, the MS was calculated. The MS of each lesion was compared with various prognostic factors of breast cancer. RESULTS: The MS of invasive cancer (median: 9.81%/sec) was significantly higher than that of ductal carcinoma in situ (median: 7.26%/sec) (p = 0.001). In the ROC analysis, the area under the ROC curve (AUC) was 0.7295. The MS of invasive cancer with axillary lymph node (LN) metastasis (median: 11.97%/sec) was significantly higher than that without axillary LN metastasis (median: 9.425%/sec) (p = 0.0024). In the ROC analysis, the AUC was 0.7177. In addition, the MS became significantly higher as the level of the proliferation marker ki-67 increased (correlation coefficient: 0.3317) (p = 0.0009). CONCLUSIONS: MS of ultrafast DCE-MRI is useful for predicting the prognostic factors of breast cancer. Higher maximum slope (MS) is significantly associated with an invasive breast cancer component. Higher MS is significantly associated with an axillary lymph node metastasis. MS becomes significantly higher with increasing ki-67 (a proliferation marker). Ultrafast MRI is useful for predicting the prognostic factors of breast cancer.

Comparison of Renal Blood Flow Using Maximum Slope-Based Computed Tomography Perfusion and Ultrasound Flow Probe in Healthy Dogs.

Computed tomography (CT) perfusion can analyze tissue perfusion and quantitative parameters, including blood flow, blood volume, and transit time. CT perfusion has been used for evaluating split renal function. However, its applicability in veterinary medicine was not validated. This study aimed to evaluate the correlation of renal blood flow (RBF) derived by maximum slope-based CT perfusion and an ultrasonic flow probe and assess the effect of the presence of a pre-existing contrast medium on CT perfusion in the kidneys. In five healthy purpose-bred beagles, CT perfusion was performed at the level of the left renal hila after injection of 1 mg/kg iohexol, during measuring RBF with an ultrasonic flow probe placed on the left renal artery. After post-contrast CT scan with injection of 2 mg/kg iohexol, CT perfusion scan was repeated with the same protocol used in the first perfusion study. The CT perfusion derived RBF was analyzed based on the maximum slope and was compared with the true RBF obtained using an ultrasonic flow probe. Results indicated that CT perfusion derived RBF was significantly correlated with true RBF, although CT perfusion derived RBF did not match the absolute value of the true RBF. It was correlated with the true RBF, even in the presence of a pre-existing contrast medium in the kidney. CT perfusion can estimate the change in individual renal perfusion non-invasively, and this method can be used supplementary to the conventional CT protocol in clinic.

A multiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI.

PURPOSE: To evaluate the diagnostic performance of a multiparametric approach to breast lesions including apparent diffusion coefficient (ADC) from diffusion-weighted images (DWI), maximum slope (MS) from ultrafast dynamic contrast enhanced (UF-DCE) MRI, lesion size, and patient's age. MATERIALS AND METHODS: In total, 96 lesions (73 malignant, 23 benign) were evaluated. UF-DCE MRI was acquired using a prototype 3D-gradient-echo volumetric interpolated breath-hold examination (VIBE) with compressed sensing. Images were obtained up to 1 min after gadolinium injection. MS was calculated as the percentage relative enhancement/s. An ADC map was automatically generated from DWI at b = 0 and b = 1000 s/mm2. MS and ADC values were measured by two radiologists independently. Interrater agreement was evaluated using intraclass correlation coefficients. Univariate and multivariate logistic regression analyses were performed using MS, ADC, lesion size, and the patient's age. The parameters of the prediction model were generated from the results of the multivariate logistic regression analysis. Area under the curve (AUC) was used to compare diagnostic performance of the prediction model and each parameter. RESULTS: Interrater agreements on MS and ADC were excellent (ICC 0.99 and 0.88, respectively). MS, ADC, and patient's age remained as significant parameters after univariate and multivariate logistic regression analysis. The prediction model using these significant parameters yielded an AUC of 0.90, significantly higher than that of MS (AUC 0.74, p = 0.01). The AUCs of ADC, MS, patient's age were 0.87, 0.74 and 0.73, respectively. CONCLUSIONS: A multiparametric model using ADC from DWI, MS from UF-DCE MRI, and patient's age showed excellent diagnostic performance, with greater contribution of ADC. Combining DWI and UF-DCE MRI might reduce scanning time while preserving diagnostic performance.

Diagnostic performance of maximum slope: A kinetic parameter obtained from ultrafast dynamic contrast-enhanced magnetic resonance imaging of the breast using k-space weighted image contrast (KWIC).

PURPOSE: To compare the diagnostic performance of the kinetic parameter maximum slope (MS) in breast lesions obtained by ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) of the contrast wash-in period with that of the washout index (WI) derived from standard DCE MRI and that of the Breast Imaging Reporting and Data System (BI-RADS) category. MATERIALS AND METHODS: In total, 138 contrast enhanced lesions (90 malignant, 48 benign) were evaluated. Ultrafast DCE MRI images were acquired using a k-space-weighted image contrast (KWIC), obtained 0-1 min after gadolinium injection (3.75 s/frame; 16 frames) and followed by standard DCE MRI (60 s/frame, 3 frames). MS was calculated for the KWIC time series as percentage relative enhancement per second (%/s). As a semi-quantitative parameter for the standard DCE MRI time series, WI was evaluated using the change in signal intensity between early and delayed phases. The diagnostic performance (malignant/benign differentiation) of MS, WI, and BI-RADS category was compared by ROC analysis using the area under the curve (AUC). RESULTS: The AUC of MS was as good as that of WI (0.81 vs. 0.79, respectively; P = 0.81), yet inferior to the BI-RADS category (0.81 vs. 0.96, respectively; <0.001). MS tended to have higher sensitivity (91.1% [82/90]) compared with WI (87.8% [79/90]) with same specificity (62.5% [30/48]). CONCLUSIONS: MS obtained by ultrafast DCE MRI of the breast is a promising kinetic parameter in the differential diagnosis of malignant and benign breast lesions with decreased scanning time.

Optimization using the gradient and simplex methods.

Traditionally optimization of analytical methods has been conducted using a univariate method, varying each parameter one-by-one holding fixed the remaining. This means in many cases to reach only local minima and not get the real optimum. Among the various options for multivariate optimization, this paper highlights the gradient method, which involves the ability to perform the partial derivatives of a mathematical model, as well as the simplex method that does not require that condition. The advantages and disadvantages of those two multivariate optimization methods are discussed, indicating when they can be applied and the different forms that have been introduced. Different cases are described on the applications of these methods in analytical chemistry.

Reproducibility of VPCT parameters in the normal pancreas: comparison of two different kinetic calculation models.

RATIONALE AND OBJECTIVES: To assess the reproducibility of volume computed tomographic perfusion (VPCT) measurements in normal pancreatic tissue using two different kinetic perfusion calculation models at three different time points. MATERIALS AND METHODS: Institutional ethical board approval was obtained for retrospective analysis of pancreas perfusion data sets generated by our prospective study for liver response monitoring to local therapy in patients experiencing unresectable hepatocellular carcinoma, which was approved by the institutional review board. VPCT of the entire pancreas was performed in 41 patients (mean age, 64.8 years) using 26 consecutive volume measurements and intravenous injection of 50 mL of iodinated contrast at a flow rate of 5 mL/s. Blood volume(BV) and blood flow (BF) were calculated using two mathematical methods: maximum slope + Patlak analysis versus deconvolution method. Pancreas perfusion was calculated using two volume of interests. Median interval between the first and the second VPCT was 2 days and between the second and the third VPCT 82 days. Variability was assessed with within-patient coefficients of variation (CVs) and Bland-Altman analyses. Interobserver agreement for all perfusion parameters was calculated using intraclass correlation coefficients (ICCs). RESULTS: BF and BV values varied widely by method of analysis as did within-patient CVs for BF and BV at the second versus the first VPCT by 22.4%/50.4% (method 1) and 24.6%/24.0% (method 2) measured in the pancreatic head and 18.4%/62.6% (method 1) and 23.8%/28.1% (method 2) measured in the pancreatic corpus and at the third versus the first VPCT by 21.7%/61.8% (method 1) and 25.7%/34.5% (method 2) measured also in the pancreatic head and 19.1%/66.1% (method 1) and 22.0%/31.8% (method 2) measured in the pancreatic corpus, respectively. Interobserver agreement measured with ICC shows fair-to-good reproducibility. CONCLUSIONS: VPCT performed with the presented examinational protocol is reproducible and can be used for monitoring purposes. Best reproducibility was obtained with both methods for BF and with method 2 also for BV data for both follow-up studies.