Diffusion-weighted imaging of normal fibroglandular breast tissue: influence of microperfusion and fat suppression technique on the apparent diffusion coefficient

Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer

To compare diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) parameters of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in the differentiation of luminal vs. non-luminal breast cancer using histogram analysis. One hundred and sixty women with 111 luminal and 49 non-luminal breast lesions were enrolled in this study. All patients underwent ss-EPI and rs-EPI sequences on a 3.0T scanner. Histogram metrics were derived from mean kurtosis (MK), mean diffusion (MD) and the apparent diffusion coefficient (ADC) maps of two DWI sequences respectively. Student's t test or Mann-Whitney U test was performed for differentiating luminal subtype from non-luminal subtype. The ROC curves were plotted for evaluating the diagnostic performances of significant histogram metrics in differentiating luminal from non-luminal BC. The histogram metrics MKmean, MK50th, MK75th of luminal BC were significantly higher than those of non-luminal BC for both two DWI sequences (all P<0.05). Histogram metrics from rs-EPI sequence had better diagnostic performance in differentiating luminal from non-Luminal breast cancer compared to those from ss-EPI sequence. MK75th derived from rs-EPI sequence was the most valuable single metric (AUC, 0.891; sensitivity, 78.4%; specificity, 87.8%) for differentiating luminal from non-luminal BC among all the histogram metrics. Histogram metrics of MK derived from rs-EPI yielded better diagnostic performance for distinguishing luminal from non-luminal BC than that from ss-EPI. MK75th was the most valuable metric among all the histogram metrics.

Breast density is strongly associated with multiparametric magnetic resonance imaging biomarkers and pro-tumorigenic proteins in situ

BACKGROUND

High mammographic density is an independent risk factor for breast cancer by poorly understood molecular mechanisms. Women with dense breasts often undergo conventional magnetic resonance imaging (MRI) despite its limited specificity, which may be increased by diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) and contrast. How these modalities are affected by breast density per se and their association with the local microenvironment are undetermined.

METHODS

Healthy postmenopausal women attending mammography screen with extremely dense or entirely fatty breasts underwent multiparametric MRI for analyses of lean tissue fraction (LTF), ADC and perfusion dynamics. Microdialysis was used for extracellular proteomics in situ.

RESULTS

Significantly increased LTF and ADC and delayed perfusion were detected in dense breasts. In total, 270 proteins were quantified, whereof 124 related to inflammation, angiogenesis, and cellular growth were significantly upregulated in dense breasts. Most of these correlated significantly with LTF, ADC and the perfusion data.

CONCLUSIONS

ADC and perfusion characteristics depend on breast density, which should be considered during the implementation of thresholds for malignant lesions. Dense and nondense breasts are two essentially different biological entities, with a pro-tumorigenic microenvironment in dense breasts. Our data reveal several novel pathways that may be explored for breast cancer prevention strategies.

Simplified intravoxel incoherent motion DWI for differentiating malignant from benign breast lesions

Background

To evaluate simplified intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for differentiating malignant versus benign breast lesions as (i) stand-alone tool and (ii) add-on to dynamic contrast-enhanced magnetic resonance imaging.

Methods

1.5-T DWI data (b = 0, 50, 250, 800 s/mm²) were retrospectively analysed for 126 patients with malignant or benign breast lesions. Apparent diffusion coefficient (ADC) ADC (0, 800) and IVIM-based parameters D₁′ = ADC (50, 800), D₂′ = ADC (250, 800), f₁′ = f (0, 50, 800), f₂′ = f (0, 250, 800) and D*′ = D* (0, 50, 250, 800) were voxel-wise calculated without fitting procedures. Regions of interest were analysed in vital tumour and perfusion hot spots. Beside the single parameters, the combined use of D₁′ with f₁′ and D₂′ with f₂′ was evaluated. Lesion differentiation was investigated for lesions (i) with hyperintensity on DWI with b = 800 s/mm² (n = 191) and (ii) with suspicious contrast-enhancement (n = 135).

Results

All lesions with suspicious contrast-enhancement appeared also hyperintense on DWI with b = 800 s/mm². For task (i), best discrimination was reached for the combination of D₁′ and f₁′ using perfusion hot spot regions-of-interest (accuracy 93.7%), which was higher than that of ADC (86.9%, p = 0.003) and single IVIM parameters D₁′ (88.0%) and f₁′ (87.4%). For task (ii), best discrimination was reached for single parameter D₁′ using perfusion hot spot regions-of-interest (92.6%), which were slightly but not significantly better than that of ADC (91.1%) and D₂′ (88.1%). Adding f₁′ to D₁′ did not improve discrimination.

Conclusions

IVIM analysis yielded a higher accuracy than ADC. If stand-alone DWI is used, perfusion analysis is of special relevance.

Tri-Compartmental Restriction Spectrum Imaging Breast Model Distinguishes Malignant Lesions from Benign Lesions and Healthy Tissue on Diffusion-Weighted Imaging

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm²) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C₁ compartment and product of the restricted and intermediate diffusion compartments (C₁ and C₂). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.

Discrimination of Breast Cancer from Healthy Breast Tissue Using a Three-component Diffusion-weighted MRI Model

PURPOSE

Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model.

EXPERIMENTAL DESIGN

Patients with pathology-proven breast cancer from two datasets (n = 81 and n = 25) underwent multi-b-value DW-MRI. The three-component signal contributions C 1 and C 2 and their product, C 1 C 2, and signal fractions F 1, F 2, and F 1 F 2 were compared with the image defined on maximum b-value (DWI max), conventional apparent diffusion coefficient (ADC), and apparent diffusion kurtosis (K app). The ability to discriminate between cancer and healthy breast tissue was assessed by the false-positive rate given a sensitivity of 80% (FPR80) and ROC AUC.

RESULTS

Mean FPR80 for both datasets was 0.016 [95% confidence interval (CI), 0.008-0.024] for C 1 C 2, 0.136 (95% CI, 0.092-0.180) for C 1, 0.068 (95% CI, 0.049-0.087) for C 2, 0.462 (95% CI, 0.425-0.499) for F 1 F 2, 0.832 (95% CI, 0.797-0.868) for F 1, 0.176 (95% CI, 0.150-0.203) for F 2, 0.159 (95% CI, 0.114-0.204) for DWI max, 0.731 (95% CI, 0.692-0.770) for ADC, and 0.684 (95% CI, 0.660-0.709) for K app. Mean ROC AUC for C 1 C 2 was 0.984 (95% CI, 0.977-0.991).

CONCLUSIONS

The C 1 C 2 parameter of the three-component model yields a clinically useful discrimination between cancer and healthy breast tissue, superior to other DW-MRI methods and obliviating predefining lesions. This novel DW-MRI method may serve as noncontrast alternative to standard-of-care dynamic contrast-enhanced MRI.

Accelerating acquisition of readout-segmented echo planar imaging with a simultaneous multi-slice (SMS) technique for diagnosing breast lesions

OBJECTIVES

To investigate the feasibility and effectiveness of SMS rs-EPI for evaluating breast lesions.

METHODS

This prospective study was approved by IRB. Ninety-six patients had 102 histopathologically verified lesions (80 malignant and 22 benign) that were evaluated. Conventional rs-EPI and SMS rs-EPI data were acquired on a 3T scanner. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were quantitatively calculated for each lesion on both sequences. Images were qualitatively and quantitatively analyzed with respect to image sharpness, geometric distortion, lesion conspicuity, anatomic structure, overall image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Student's t test, Pearson correlation, receiver operating characteristic curve, Wilcoxon rank sum test, and paired-sample t tests were used for statistical analysis.

RESULTS

Compared to conventional rs-EPI, the acquisition time of SMS rs-EPI was markedly reduced (2:17 min vs 4:27 min). Pearson's correlations showed excellent linear relationships for each parameter between conventional rs-EPI and SMS rs-EPI (MK, r = 0.908; MD, r = 0.938; and ADC, r = 0.975; p < 0.01 for all). Furthermore, SMS rs-EPI had similar diagnostic performance compared with conventional rs-EPI. SMS rs-EPI had comparable visual image quality as conventional rs-EPI, with excellent inter-reader reliability (ICC = 0.851-0.940). No differences existed between conventional rs-EPI and SMS rs-EPI for either SNR or CNR (p > 0.05).

CONCLUSIONS

Applying the SMS technique can significantly reduce the acquisition time and produce similar diagnostic accuracy while generating comparable image quality as the conventional rs-EPI.

KEY POINTS

• SMS rs-EPI reduces scan time from 4:27 min to 2:17 min compared with conventional rs-EPI. • SMS rs-EPI has a comparable diagnostic performance to conventional rs-EPI in the differentiation between malignant and benign breast lesions. • SMS rs-EPI demonstrates comparable image quality to conventional rs-EPI with shorter scan time.

Influence of residual fat signal on diffusion kurtosis MRI of suspicious mammography findings

Recent studies showed the potential of diffusion kurtosis imaging (DKI) as a tool for improved classification of suspicious breast lesions. However, in diffusion-weighted imaging of the female breast, sufficient fat suppression is one of the main factors determining the success. In this study, the data of 198 patients examined in two study centres was analysed using standard diffusion and kurtosis evaluation methods and three DKI fitting approaches accounting phenomenologically for fat-related signal contamination of the lesions. Receiver operating characteristic curve analysis showed the highest area under the curve (AUC) for the method including fat correction terms (AUC = 0.85, p < 0.015) in comparison to the values obtained with the standard diffusion (AUC = 0.77) and kurtosis approach (AUC = 0.79). Comparing the two study centres, the AUC value improved from 0.77 to 0.86 (p = 0.036) using a fat correction term for the first centre, while no significant difference with no adverse effects was observed for the second centre (AUC 0.89 vs. 0.90, p = 0.95). Contamination of the signal in breast lesions with unsuppressed fat causing a reduction of diagnostic performance of diffusion kurtosis imaging may potentially be counteracted by proposed adapted evaluation methods.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

Measurement of apparent diffusion coefficient in discrimination of benign and malignant axillary lymph nodes

Purpose

We aimed to determine the contribution of the apparent diffusion coefficient (ADC) value in the detection of axillary lymph node metastasis.

Material and methods

Breast magnetic resonance of 58 patients, performed in the radiology clinic of our hospital between 2015 and 2017 were examined retrospectively, and 43 lymph nodes in 43 patients were included in the study. They were evaluated morphologically on T1W and T2W sequences, and the lymph nodes showing rounded shape, focal or diffuse cortical thickness of more than 3 mm, and partial or total effacement of fatty hilum were included in the study. Subsequently, their ADC values were measured.

Results

There were 43 lymph nodes, 20 of which were malignant and 23 of which were benign. While the mean ADC value of malignant axillary lymph nodes was 0.749 10^-3 mm²/s (0.48-1.342), it was 0.982 10^-3 mm²/s (0.552-1.986) for benign lymph nodes. When the ADC cut-off value was taken as ≤ 0.753 × 10^-3 mm²/s, its discrimination power between benign and malignant axillary lymph nodes was as follows: sensitivity - 60%; specificity - 91.3%; accuracy - 76.7%; positive predictive value - 85.7%; and negative predictive value - 72.4%.

Conclusions

There was no significant difference between mean ADC value of 12 lymphadenopathies (LAP) associated with inflammatory breast diseases (granulomatous mastitis and acute suppurative mastitis) and mean ADC value of metastatic lymph nodes. However, the ADC value of lymph nodes showing thickened cortex due to systemic inflammatory diseases was over 1, and there was a statistically significant difference when compared with metastatic lymph nodes.

Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas

Background

Dynamic-susceptibility contrast and diffusion-weighted imaging are promising techniques in diagnosing glioma grade.

Purpose

To compare the inter-observer reproducibility of multiple dynamic-susceptibility contrast and diffusion-weighted imaging parameters and to assess their potential in differentiating low- and high-grade gliomas.

Material and Methods

Thirty patients (16 men; mean age = 40.6 years) with low-grade (n = 13) and high-grade (n = 17) gliomas and known pathology, scanned with dynamic-susceptibility contrast and diffusion-weighted imaging were included retrospectively between March 2006 and March 2014. Three observers used three different methods to define the regions of interest: (i) circles at maximum perfusion and minimum apparent diffusion coefficient; (ii) freeform 2D encompassing the tumor at largest cross-section only; (iii) freeform 3D on all cross-sections. The dynamic-susceptibility contrast curve was analyzed voxelwise: maximum contrast enhancement; time-to-peak; wash-in rate; wash-out rate; and relative cerebral blood volume. The mean was calculated for all regions of interest. For 2D and 3D methods, histogram analysis yielded additional statistics: the minimum and maximum 5% and 10% pixel values of the tumor (min5%, min10%, max5%, max10%). Intraclass correlations coefficients (ICC) were calculated between observers. Low- and high-grade tumors were compared with independent t-tests or Mann–Whitney tests.

Results

ICCs were highest for 3D freeform (ICC = 0.836–0.986) followed by 2D freeform (ICC = 0.854–0.974) and circular regions of interest (0.141–0.641). High ICC and significant discrimination between low- and high-grade gliomas was found for the following optimized parameters: apparent diffusion coefficient (P < 0.001; ICC = 0.641; mean; circle); time-to-peak (P = 0.015; ICC = 0.986; mean; 3D); wash-in rate (P = 0.004; ICC = 0.826; min10%; 3D); wash-out rate (P < 0.001; ICC = 0.860; min10%; 2D); and relative cerebral blood volume (P ≤ 0.001; ICC = 0.961; mean; 3D).

Conclusion

Dynamic-susceptibility contrast perfusion parameters relative cerebral blood volume and time-to-peak yielded high inter-observer reproducibility and significant glioma grade differentiation for the means of 2D and 3D freeform regions of interest. Choosing a freeform 2D method optimizes observer agreement and differentiation in clinical practice, while a freeform 3D method provides no additional benefit.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Diffusion-Weighted Imaging of the Head and Neck: Influence of Fat-Suppression Technique and Multishot 2D Navigated Interleaved Acquisitions

BACKGROUND AND PURPOSE

DWI of the head and neck can reveal valuable information, but the effects of fat suppression and multishot acquisition on image quality have not been thoroughly investigated. We aimed to comprehensively compare the quality of head and neck DWI at 3T using 2 fat-suppression techniques, STIR, and spectral presaturation with inversion recovery, which were used with both single- and multishot EPI.

MATERIALS AND METHODS

Sixty-five study participants underwent 3 DWI sequences of single-shot EPI-STIR, single-shot EPI-spectral presaturation with inversion recovery, and multishot EPI-spectral presaturation with inversion recovery of the head and neck. In multiple anatomic regions, 2 independent readers assessed 5-point visual scores for fat-suppression uniformity and image distortion, and 1 reader measured the contrast-to-noise ratio and ADC.

RESULTS

The mean visual score for fat-suppression uniformity was higher in single-shot EPI-STIR than in other sequences (all regions except for the orbital region, P < .05). The mean visual score for image distortion was higher in multishot EPI-spectral presaturation with inversion recovery than in single-shot EPI sequences (all regions, P < .001). Contrast-to-noise ratio was mostly lower in single-shot EPI-STIR than in other sequences (P < .001), and ADC was significantly higher in multishot EPI-spectral presaturation with inversion recovery than in single-shot EPI sequences (P ≤ .001).

CONCLUSIONS

Overall, multishot EPI-spectral presaturation with inversion recovery provided the best image quality, with relatively homogeneous fat suppression, less image distortion than single-shot EPI sequences, and higher contrast-to-noise ratio than single-shot EPI-STIR. The measured ADC values can be higher in multishot EPI-spectral presaturation with inversion recovery, which necessitates cautious application of the previously reported ADC values to clinical settings.

Evaluation of T1/T2 ratios in a pilot study as a potential biomarker of biopsy: proven benign and malignant breast lesions in correlation with histopathological disease stage

Aim:

Early breast cancer detection is important for intervention and prognosis. Advances in treatment and outcome require diagnostic tools with highly positive predictive value.

Purpose:

To study the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions.

Methods:

A cross-sectional study of 69 women with 69 known or suspicious breast lesions were scanned with mixed-turbo spin echo pulse sequence. Patients were grouped according to histopathological assessment of disease stage: untreated malignant tumor, treated malignancy and benign disease.

Results & Discussion:

Elevated T1/T2 means were observed for biopsy-proven malignant lesions and for malignant lesions treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. The qMRI-obtained T1/T2 ratios correlated with histopathology. Analysis revealed correlation between elevated T1/T2 ratio and disease stage. This could provide valuable complementary information on tissue properties as an additional diagnostic tool.

Early detection is important for successful intervention in breast cancer. We studied the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions. Sixty nine women with breast lesions were scanned with qMRI. Elevated ratios were observed for biopsy-proven malignant lesions and for malignant lesions that were treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. With further studies, this approach could provide valuable information concerning tissue properties in addition to established breast imaging sequences and be an additional diagnostic tool.

Impact of Different Analytic Approaches on the Analysis of the Breast Fibroglandular Tissue Using Diffusion Weighted Imaging

Purpose. This study investigated the impact of the different region of interest (ROI) approaches on measurement of apparent diffusion coefficient (ADC) values in the breast firbroglandular tissue (FT). Methods. Breast MR images of 38 women diagnosed with unilateral breast cancer were studied. Percent density (PD) and ADC were measured from the contralateral normal breast. Four different ROIs were used for ADC measurement. The measured PD and ADC were correlated. Results. Among the four ROIs, the manually placed small ROI on FT gave the highest mean ADC (ADC = 1839 ± 343 [×10⁻⁶ mm²/s]), while measurement from the whole breast gave the lowest mean ADC (ADC = 933 ± 383 [×10⁻⁶ mm²/s]). The ADC measured from the whole breast was highly correlated with PD with r = 0.95. In slice-to-slice comparison, the central slices with more FT had higher ADC values than the peripheral slices did, presumably due to less partial volume effect from fat. Conclusions. Our results indicated that the measured ADC heavily depends on the composition of breast tissue contained in the ROI used for the ADC measurements. Women with low breast density showing lower ADC values were most likely due to the partial volume effect of fatty tissues.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Effects of magnetic field strength and b value on the sensitivity and specificity of quantitative breast diffusion-weighted MRI

BACKGROUND

To evaluate the effect of b value or the magnetic field strength (B0) on the sensitivity and specificity of quantitative breast diffusion-weighted imaging (DWI).

METHODS

A total of 126 patients underwent clinical breast MRI that included pre-contrast DWI imaging using b values of both 1,000 and 1,500 s/mm² at either 1.5 T (n=86) or 3.0 T (n=40). Quantitative apparent diffusion coefficients (ADC) were measured and compared for 18 benign, 33 malignant lesions, and 126 normal breast tissues. Optimal ADCmean threshold for differentiating benign and malignant lesions was estimated and the effect of b values and B0 were examined using a generalized estimating equations (GEE) model.

RESULTS

The optimal ADCmean threshold was 1.235×10^-3 mm²/s for b value of 1,000 and 0.934×10^-3 mm²/s for b value of 1,500. Using these thresholds, the sensitivities and specificities were 96% and 89% (b value =1,000, B0 =1.5 T), 89% and 98% (b value =1,000, B0 =3.0 T), 88% and 96% (b value =1,500, B0 =1.5 T), and 67% and 100% (b value =1,500, B0 =3.0 T). No significant difference was found between different B0 (P=0.26) or b values (P=0.28).

CONCLUSIONS

Better sensitivity is achieved with DWI of b value =1,000 than with b value =1,500. However, b value and B0 do not significantly impact diagnostic performance of DWI when using appropriate thresholds.

Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors

BACKGROUND

The purpose of this study is to statistically assess whether bi-exponential intravoxel incoherent motion (IVIM) model better characterizes diffusion weighted imaging (DWI) signal of malignant breast tumor than mono-exponential Gaussian diffusion model.

METHODS

3 T DWI data of 29 malignant breast tumors were retrospectively included. Linear least-square mono-exponential fitting and segmented least-square bi-exponential fitting were used for apparent diffusion coefficient (ADC) and IVIM parameter quantification, respectively. F-test and Akaike Information Criterion (AIC) were used to statistically assess the preference of mono-exponential and bi-exponential model using region-of-interests (ROI)-averaged and voxel-wise analysis.

RESULTS

For ROI-averaged analysis, 15 tumors were significantly better fitted by bi-exponential function and 14 tumors exhibited mono-exponential behavior. The calculated ADC, D (true diffusion coefficient) and f (pseudo-diffusion fraction) showed no significant differences between mono-exponential and bi-exponential preferable tumors. Voxel-wise analysis revealed that 27 tumors contained more voxels exhibiting mono-exponential DWI decay while only 2 tumors presented more bi-exponential decay voxels. ADC was consistently and significantly larger than D for both ROI-averaged and voxel-wise analysis.

CONCLUSIONS

Although the presence of IVIM effect in malignant breast tumors could be suggested, statistical assessment shows that bi-exponential fitting does not necessarily better represent the DWI signal decay in breast cancer under clinically typical acquisition protocol and signal-to-noise ratio (SNR). Our study indicates the importance to statistically examine the breast cancer DWI signal characteristics in practice.

Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility

There is considerable disparity in the published apparent diffusion coefficient (ADC) values across different anatomies. Institutions are increasingly assessing repeatability and reproducibility of the derived ADC to determine its variation, which could potentially be used as an indicator in determining tumour aggressiveness or assessing tumour response. In this manuscript, a review of selected articles published to date in healthy extra-cranial body diffusion-weighted magnetic resonance imaging is presented, detailing reported ADC values and discussing their variation across different studies. In total 115 studies were selected including 28 for liver parenchyma, 15 for kidney (renal parenchyma), 14 for spleen, 13 for pancreatic body, 6 for gallbladder, 13 for prostate, 13 for uterus (endometrium, myometrium, cervix) and 13 for fibroglandular breast tissue. Median ADC values in selected studies were found to be 1.28 × 10(-3) mm(2)/s in liver, 1.94 × 10(-3) mm(2)/s in kidney, 1.60 × 10(-3) mm(2)/s in pancreatic body, 0.85 × 10(-3) mm(2)/s in spleen, 2.73 × 10(-3) mm(2)/s in gallbladder, 1.64 × 10(-3) mm(2)/s and 1.31 × 10(-3) mm(2)/s in prostate peripheral zone and central gland respectively (combined median value of 1.54×10(-3) mm(2)/s), 1.44 × 10(-3) mm(2)/s in endometrium, 1.53 × 10(-3) mm(2)/s in myometrium, 1.71 × 10(-3) mm(2)/s in cervix and 1.92 × 10(-3) mm(2)/s in breast. In addition, six phantom studies and thirteen in vivo studies were summarized to compare repeatability and reproducibility of the measured ADC. All selected phantom studies demonstrated lower intra-scanner and inter-scanner variation compared to in vivo studies. Based on the findings of this manuscript, it is recommended that protocols need to be optimised for the body part studied and that system-induced variability must be established using a standardized phantom in any clinical study. Reproducibility of the measured ADC must also be assessed in a volunteer population, as variations are far more significant in vivo compared with phantom studies.

Qualitative and quantitative diffusion-weighted imaging of the breast at 3T - A useful adjunct to contrast-enhanced MRI in characterization of breast lesions

Objective:

To distinguish between benign and malignant breast lesions on the basis of their signal intensity on diffusion-weighted imaging and their apparent diffusion coefficient (ADC) values at 3 T MRI, along with histopathological correlation.

Materials and Methods:

A retrospective analysis of 500 patients who underwent 3 T MRI between August 2011 and May 2013 was done. Of these, 226 patients with 232 lesions that were proved by histopathology were included in the study. ADC values were calculated at b values of 0, 1000, and 1500 s/mm² after identification on contrast-enhanced images and appropriate ROI(Region of interest) placement. ADC value and histopathology correlation was analyzed.

Results:

Out of 232 lesions, 168 lesions were histologically malignant and 64 were histologically benign. With an ADC cut-off value of 1.1 ×10⁻³ mm²/s for malignant lesions, a sensitivity of 92.80% and specificity of 80.23% was obtained. Out of 12/232 false-negative lesions, 6 were mucinous carcinoma in which a high ADC value of 1.8-1.9 ×10⁻³ mm²/s was obtained. Purely DCIS (Ductal carcinoma in situ) lesions presenting as non-mass-like enhancement had a high ADC value of 1.2-1.5 ×10⁻³ mm²/s, thereby reducing specificity.

Conclusion:

Diffusion-weighted Imaging and quantitative assessment by ADC values may act as an effective parameter in increasing the diagnostic accuracy and specificity of contrast-enhanced breast MRI in characterization of breast lesions.

Contrast-optimized composite image derived from multigradient echo cardiac magnetic resonance imaging improves reproducibility of myocardial contours and T2* measurement

Objectives

Reproducibility of myocardial contour determination in cardiac magnetic resonance imaging is important, especially when determining T2* values per myocardial segment as a prognostic factor of heart failure or thalassemia. A method creating a composite image with contrasts optimized for drawing myocardial contours is introduced and compared with the standard method on a single image.

Materials and methods

A total of 36 short-axis slices from bright-blood multigradient echo (MGE) T2* scans of 21 patients were acquired at eight echo times. Four observers drew free-hand myocardial contours on one manually selected T2* image (method 1) and on one image composed by blending three images acquired at TEs providing optimum contrast-to-noise ratio between the myocardium and its surrounding regions (method 2).

Results

Myocardial contouring by method 2 met higher interobserver reproducibility than method 1 (P < 0.001) with smaller Coefficient of variance (CoV) of T2* values in the presence of myocardial iron accumulation (9.79 vs. 15.91 %) and in both global myocardial and mid-ventricular septum regions (12.29 vs. 16.88 and 5.76 vs. 8.16 %, respectively).

Conclusion

The use of contrast-optimized composite images in MGE data analysis improves reproducibility of myocardial contour determination, leading to increased consistency in the calculated T2* values enhancing the diagnostic impact of this measure of iron overload.

Evaluation of diffusion models in breast cancer

PURPOSE

The purpose of this study is to investigate whether the microvascular pseudodiffusion effects resulting with non-monoexponential behavior are present in breast cancer, taking into account tumor spatial heterogeneity. Additionally, methodological factors affecting the signal in low and high diffusion-sensitizing gradient ranges were explored in phantom studies.

METHODS

The effect of eddy currents and accuracy of b-value determination using a multiple b-value diffusion-weighted MR imaging sequence were investigated in test objects. Diffusion model selection and noise were then investigated in volunteers (n = 5) and breast tumor patients (n = 21) using the Bayesian information criterion.

RESULTS

54.3% of lesion voxels were best fitted by a monoexponential, 26.2% by a stretched-exponential, and 19.5% by a biexponential intravoxel incoherent motion (IVIM) model. High correlation (0.92) was observed between diffusion coefficients calculated using mono- and stretched-exponential models and moderate (0.59) between monoexponential and IVIM (medians: 0.96/0.84/0.72 × 10(-3) mm(2)/s, respectively). Distortion due to eddy currents depended on the direction of the diffusion gradient and displacement varied between 1 and 6 mm for high b-value images. Shift in the apparent diffusion coefficient due to intrinsic field gradients was compensated for by averaging diffusion data obtained from opposite directions.

CONCLUSIONS

Pseudodiffusion and intravoxel heterogeneity effects were not observed in approximately half of breast cancer and normal tissue voxels. This result indicates that stretched and IVIM models should be utilized in regional analysis rather than global tumor assessment. Cross terms between diffusion-sensitization gradients and other imaging or susceptibility-related gradients are relevant in clinical protocols, supporting the use of geometric averaging of diffusion-weighted images acquired with diffusion-sensitization gradients in opposite directions.

3-T breast diffusion-weighted MRI by echo-planar imaging with spectral spatial excitation or with additional spectral inversion recovery: an in vivo comparison of image quality

OBJECTIVE

To compare conventional diffusion-weighted imaging (DWI) with spectral spatial excitation (cDWI) and an enhanced DWI with additional adiabatic spectral inversion recovery (eDWI) for 3-T breast magnetic resonance imaging (MRI).

METHODS

Twenty-four patients were enrolled in the study with both cDWI and eDWI. Three breast radiologists scored cDWI and eDWI images of each patient for fat-suppression quality, geometric distortion, visibility of normal structure and biopsy-proven lesions, and overall image quality. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for evaluable tissues were measured. Statistical tests were performed for qualitative and quantitative comparisons.

RESULTS

Diffusion-weighted imaging with spectral spatial excitation yielded significantly higher CNR and SNR on a lesion basis, and higher glandular CNR and SNR and muscle SNR on a patient basis. Enhanced DWI also yielded significantly higher qualitative scores in all categories. No significant difference was found in ADC values.

CONCLUSIONS

Enhanced DWI provided superior image quality and higher CNR and SNR on a lesion basis. Enhanced DWI can replace cDWI for 3-T breast DWI.

Dynamic properties of water in breast pathology depend on the histological compounds: distinguishing tissue malignancy by water diffusion coefficients

Background

The parameters that characterize the intricate water diffusion in tumors may also reveal their distinct pathology. Specifically, characterization of breast cancer could be aided by diffusion magnetic resonance.

The present in vitro study aimed to discover connections between the NMR biexponential diffusion parameters [fast diffusion phase (D_FDP ), slow diffusion phase (D_SDP ), and spin population of fast diffusion phase (P₁)] and the histological constituents of nonmalignant (control) and malignant human breast tissue. It also investigates whether the diffusion coefficients indicate tissue status.

Methods

Post-surgical specimens of control (mastopathy and peritumoral tissues) and malignant human breast tissue were placed in an NMR spectrometer and diffusion sequences were applied. The resulting decay curves were analyzed by a biexponential model, and slow and fast diffusion parameters as well as percentage signal were identified. The same samples were also histologically examined and their percentage composition of several tissue constituents were measured: parenchyma (P), stroma (St), adipose tissue (AT), vessels (V) , pericellular edema (PCE), and perivascular edema (PVE). Correlations between the biexponential model parameters and tissue types were evaluated for different specimens. The effects of tissue composition on the biexponential model parameters, and the effects of histological and model parameters on cancer probability, were determined by non-linear regression.

Results

Meaningful relationships were found among the in vitro data. The dynamic parameters of water in breast tissue are stipulated by the histological constituents of the tissues (P, St, AT, PCE, and V). High coefficients of determination (R²) were obtained in the non-linear regression analysis: D_FDP (R² = 0.92), D_SDP (R² = 0.81), and P₁(R² = 0.93).

In the cancer probability analysis, the informative value (R²) of the obtained equations of cancer probability in distinguishing tissue malignancy depended on the parameters input to the model. In order of increasing value, these equations were: cancer probability (P, St, AT, PCE, V) (R² = 0.66), cancer probability (D_FDP, D_SDP)(R² = 0.69), cancer probability (D_FDP, D_SDP, P₁) (R² = 0.85).

Conclusion

Histological tissue components are related to the diffusion biexponential model parameters. From these parameters, the relative probability of cancer in a given specimen can be determined with some certainty.

Characterization of breast tumors using diffusion kurtosis imaging (DKI)

Aim

The aim of this study was to investigate and evaluate the role of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in characterizing breast lesions.

Materials and Methods

One hundred and twenty-four lesions in 103 patients (mean age: 57±14 years) were evaluated by MR DKI performed with 7 b-values of 0, 250, 500, 750, 1,000, 1,500, 2,000 s/mm² and dynamic contrast-enhanced (DCE) MR imaging. Breast lesions were histologically characterized and DKI related parameters—mean diffusivity (MD) and mean kurtosis (MK)—were measured. The MD and MK in normal fibroglandular breast tissue, benign and malignant lesions were compared by One-way analysis of variance (ANOVA) with Tukey's multiple comparison test. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of MD and MK in the diagnosis of breast lesions.

Results

The benign lesions (n = 42) and malignant lesions (n = 82) had mean diameters of 11.4±3.4 mm and 35.8±20.1 mm, respectively. The MK for malignant lesions (0.88±0.17) was significantly higher than that for benign lesions (0.47±0.14) (P<0.001), and, in contrast, MD for benign lesions (1.97±0.35 (10⁻³ mm²/s)) was higher than that for malignant lesions (1.20±0.31 (10⁻³ mm²/s)) (P<0.001). At a cutoff MD/MK 1.58 (10⁻³ mm²/s)/0.69, sensitivity and specificity of MD/MK for the diagnosis of malignant were 79.3%/84.2% and 92.9%/92.9%, respectively. The area under the curve (AUC) is 0.86/0.92 for MD/MK.

Conclusions

DKI could provide valuable information on the diffusion properties related to tumor microenvironment and increase diagnostic confidence of breast tumors.

[Diffusion-weighted breast imaging. Clinical implementation procedure]

Diffusion-weighted imaging (DWI) of the breast provides additional contrast information in breast magnetic resonance imaging (MRI). The DWI procedure can easily be implemented in the routine breast MRI protocol with little time expenditure regarding image acquisition and evaluation. Evaluation of the DW images can be performed with or without the routine breast MRI sequences (T2w and T1w with contrast material) but evaluation in combination with the routine program is highly recommended. Objective analysis of the tissue diffusion can be achieved by calculating the apparent diffusion coefficient (ADC) value with the scanner software. The choice of the DW sequence, evaluation and determination of the ADC threshold to differentiate between benign and malignant lesions should be scanner adapted. The use of DW imaging qualifies for routine use regarding the differentiation between malignant and benign breast lesions. Non-mass-like lesions and monitoring neoadjuvant chemotherapy can also be evaluated with DW sequences. The benefit of the additional information from DW-MR mammography to characterize non-mass-like lesions and in the course of neoadjuvant chemotherapy remains unclear to date.

Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis

OBJECTIVES

To evaluate the effect of the choice of b values and prior use of contrast medium on apparent diffusion coefficients (ADCs) of breast lesions derived from diffusion-weighted imaging (DWI), and on the discrimination between benign and malignant lesions.

METHODS

A literature search of relevant DWI studies was performed. The accuracy of DWI to characterize lesions by using b value ≤600 s/mm(2) and b value >600 s/mm(2) was presented as pooled sensitivity and specificity, and the ADC was calculated for both groups. Lesions were pooled as pre- or post-contrast DWI.

RESULTS

Of 198 articles, 26 met the inclusion criteria. Median ADCs were significantly higher (13.2-35.1 %, p < 0.001) for the group of b values ≤600 s/mm(2) compared to >600 s/mm(2). The sensitivity in both groups was similar (91 % and 89 %, p = 0.495) as well as the specificity (75 % and 84 %, p = 0.237). Contrast medium had no significant effects on the ADCs (p ≥ 0.08). The differentiation between benign and malignant lesions was optimal (58.4 %) for the combination of b = 0 and 1,000 s/mm(2).

CONCLUSIONS

The wide variety of b value combinations applied in different studies significantly affects the ADC of breast lesions and confounds quantitative DWI. If only a couple of b values are used, those of b = 0 and 1,000 s/mm(2) are recommended for the best improvement of differentiating between benign and malignant lesions.

KEY POINTS

• The choice of b values significantly affects the ADC of breast lesions. • Sensitivity and specificity are not affected by the choice of b values. • b values 0 and 1,000 s/mm (2) are recommended for optimal differentiation between benign and malignant lesions. • Contrast medium prior to DWI does not significantly affect the ADC.

Effects of gender, age, and body mass index on fat contents and apparent diffusion coefficients in healthy parotid glands: an MRI evaluation

OBJECTIVES

To establish standard apparent diffusion coefficient (ADC) and the fat content as a function of age, gender and body mass index (BMI) in healthy parotid glands, and to address the influences of fat suppression on ADC measurements.

METHODS

A total of 100 healthy adults (gender and age evenly distributed) were prospectively recruited, with parotid fat content measured from gradient-echo images with fat-water separated using iterative decomposition with echo asymmetry and least squares (IDEAL). The ADCs were estimated using both fat-saturated and non-fat-saturated diffusion-weighted imaging via a periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique.

RESULTS

Parotid fat content was larger in men than in women by about 10 percentage points (P < 0.005), and positively associated with BMI and age for both genders (mostly with P < 0.001). ADCs estimated with non-fat-saturated PROPELLER were significantly lower in men than in women (P < 0.005), but showed no gender difference if measured using fat-saturated PROPELLER (P = 0.840). The negative association between parotid ADC and age/BMI/fat (P < 0.001) showed greater regression slopes in non-fat-saturated PROPELLER than in fat-saturated data.

CONCLUSIONS

Parotid fat content in healthy adults correlates positively with both age and BMI; the correlation with age is gender-dependent. Parotid ADC measurements are strongly influenced by fat saturation.

KEY POINTS

Parotid fat content in healthy adults correlates positively with age and BMI. The rate of aging-related increase in fat contents is gender-dependent. Parotid ADC measurements are strongly influenced by fat saturation.

Diffusion-weighted MRI: association between patient characteristics and apparent diffusion coefficients of normal breast fibroglandular tissue at 3 T

OBJECTIVE

The purpose of this study is to assess associations between patient characteristics and apparent diffusion coefficient (ADC) values of normal breast fibroglandular tissue on diffusion-weighted imaging (DWI) at 3 T.

MATERIALS AND METHODS

The retrospective study included 103 women with negative bilateral findings on 3-T breast MRI examinations (BI-RADS category 1). DWI was acquired during clinical breast MRI scans using b = 0 and b = 800 s/mm(2). Mean ADC of normal breast fibroglandular tissue was calculated for each breast using a semiautomated software tool in which parenchyma pixels were selected by interactive thresholding of the b = 0 s/mm(2) image to exclude fat. Intrasubject right- and left-breast ADC values were compared and averaged together to evaluate the association of mean breast ADC with age, mammographic breast density, and background parenchymal enhancement.

RESULTS

Overall mean ± SD breast ADC was 1.62 ± 0.30 × 10(-3) mm(2)/s. Intrasubject right- and left-breast ADC measurements were highly correlated (R(2) = 0.89; p < 0.0001). Increased breast density was strongly associated with increased ADC (p ≤ 0.0001). Age and background parenchymal enhancement were not associated with ADC.

CONCLUSION

Normal breast parenchymal ADC values increase with mammographic density but are independent of age and background parenchymal enhancement. Because breast malignancies have been shown to have low ADC values, DWI may be particularly valuable in women with dense breasts owing to greater contrast between lesion and normal tissue.

Diffusion-weighted imaging: determination of the best pair of b-values to discriminate breast lesions

OBJECTIVE

In breast diffusion-weighted imaging (DWI), the apparent diffusion coefficient (ADC) is used to discriminate between malignant and benign lesions. As ADC estimates can be affected by the weighting factors, our goal was to determine the optimal pair of b-values for discriminating breast lesions at 3.0 T.

METHODS

152 females with 157 lesions (89 malignant and 68 benign) underwent breast MRI, including a DWI sequence sampling six b-values 50, 200, 400, 600, 800 and 1000 s mm(-2). ADC values were computed from different pairs of b-values and compared with ADC obtained by fitting the six b-values using a mono-exponential diffusion model (ADCall). Cut-off ADC values were determined and diagnostic performance evaluated by receiver operating characteristic analysis using Youden statistics. Mean ADCs were determined for normal tissue and lesions. Differences were evaluated by lesion and histological types.

RESULTS

Considering the cut-off values 1.46 and 1.49 × 10(3)mm(2) s(-1), the pairs 50, 1000 and 200, 800 s mm(-2) showed the highest accuracy, 77.5% and 75.4% with areas under the curve 84.4% and 84.2%, respectively. The best pair for ADC quantification was 50, 1000 s mm(-2) with 38/49 true-negative and 69/89 true-positive cases respectively; mean ADCs were 1.86 ± 0.46, 1.77 ± 0.37 and 1.15 ± 0.46 × 10(-3) mm(2) s(-1) for normal, benign and malignant lesions. There were no significant differences in these ADC values when compared with ADCall (ADC calculated from the full set of b - values) [difference = 0.0075 × 10(-3) mm(2) s(-1); confidence interval 95%: (-0.0036; 0.0186); p = 0.18].

CONCLUSION

The diagnostic performance in differentiating malignant and benign lesions was most accurate for the b-value pair 50, 1000 s mm(-2).

ADVANCES IN KNOWLEDGE

The best b-value pair for lesion discrimination and characterization through ADC quantification was 50, 1000 s mm(-2).

Clinical implications of non-steatotic hepatic fat fractions on quantitative diffusion-weighted imaging of the liver

Diffusion-weighted imaging (DWI) is an important diagnostic tool in the assessment of focal liver lesions and diffuse liver diseases such as cirrhosis and fibrosis. Quantitative DWI parameters such as molecular diffusion, microperfusion and their fractions, are known to be affected when hepatic fat fractions (HFF) are higher than 5.5% (steatosis). However, less is known about the effect on DWI for HFF in the normal non-steatotic range below 5.5%, which can be found in a large part of the population. The aim of this study was therefore to evaluate the diagnostic implications of non-steatotic HFF on quantitative DWI parameters in eight liver segments. For this purpose, eleven healthy volunteers (2 men, mean-age 31.0) were prospectively examined with DWI and three series of in-/out-of-phase dual-echo spoiled gradient-recalled MRI sequences to obtain the HFF and T₂*. DWI data were analyzed using the intravoxel incoherent motion (IVIM) model. Four circular regions (ø22.3 mm) were drawn in each of eight liver segments and averaged. Measurements were divided in group 1 (HFF≤2.75%), group 2 (2.75< HFF ≤5.5%) and group 3 (HFF>5.5%). DWI parameters and T₂* were compared between the three groups and between the segments. It was observed that the molecular diffusion (0.85, 0.72 and 0.49 ×10⁻³ mm²/s) and T₂* (32.2, 27.2 and 21.0 ms) differed significantly between the three groups of increasing HFF (2.18, 3.50 and 19.91%). Microperfusion and its fraction remained similar for different HFF. Correlations with HFF were observed for the molecular diffusion (r = −0.514, p<0.001) and T₂* (−0.714, p<0.001). Similar results were obtained for the majority of individual liver segments. It was concluded that fat significantly decreases molecular diffusion in the liver, also in absence of steatosis (HFF≤5.5%). Also, it was confirmed that fat influences T₂*. Determination of HFF prior to quantitative DWI is therefore crucial.

Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma

PURPOSE

To study the differentiation of malignant breast lesions from benign lesions and fibroglandular tissue (FGT) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters.

MATERIALS AND METHODS

This retrospective study included 26 malignant and 14 benign breast lesions in 35 patients who underwent diffusion-weighted MRI at 3.0T and nine b-values (0-1000 s/mm(2) ). ADC and IVIM parameters (perfusion fraction fp , pseudodiffusion coefficient Dp , and true diffusion coefficient Dd ) were determined in lesions and FGT. For comparison, IVIM was also measured in 16 high-risk normal patients. A predictive model was constructed using linear discriminant analysis. Lesion discrimination based on ADC and IVIM parameters was assessed using receiver operating characteristic (ROC) and area under the ROC curve (AUC).

RESULTS

In FGT of normal subjects, fp was 1.1 ± 1.1%. In malignant lesions, fp (6.4 ± 3.1%) was significantly higher than in benign lesions (3.1 ± 3.3%, P = 0.0025) or FGT (1.5 ± 1.2%, P < 0.001), and Dd ((1.29 ± 0.28) × 10(-3) mm(2) /s) was lower than in benign lesions ((1.56 ± 0.28) × 10(-3) mm(2) /s, P = 0.011) or FGT ((1.86 ± 0.34) × 10(-3) mm(2) /s, P < 0.001). A combination of Dd and fp provided higher AUC for discrimination between malignant and benign lesions (0.84) or FGT (0.97) than ADC (0.72 and 0.86, respectively).

CONCLUSION

The IVIM parameters provide accurate identification of malignant lesions.

Diffusion weighted magnetic resonance imaging of the breast: protocol optimization, interpretation, and clinical applications

Diffusion-weighted magnetic resonance (MR) imaging (DWI) has shown promise for improving the positive predictive value of breast MR imaging for detection of breast cancer, evaluating tumor response to neoadjuvant chemotherapy, and as a noncontrast alternative to MR imaging in screening for breast cancer. However, data quality varies widely. Before implementing DWI into clinical practice, one must understand the pertinent technical considerations and current evidence regarding clinical applications of breast DWI. This article provides an overview of basic principles of DWI, optimization of breast DWI protocols, imaging features of benign and malignant breast lesions, promising clinical applications, and potential future directions.

Quantitative analysis of diffusion-weighted magnetic resonance imaging in malignant breast lesions using different b value combinations

Objectives

To explore how apparent diffusion coefficients (ADCs) in malignant breast lesions are affected by selection of b values in the monoexponential model and to compare ADCs with diffusion coefficients (Ds) obtained from the biexponential model.

Methods

Twenty-four women (mean age 51.3 years) with locally advanced breast cancer were included in this study. Pre-treatment diffusion-weighted magnetic resonance imaging was performed using a 1.5-T system with b values of 0, 50, 100, 250 and 800 s/mm². Thirteen different b value combinations were used to derive individual monoexponential ADC maps. All b values were used in the biexponential model.

Results

Median ADC (including all b values) and D were 1.04 × 10^-3 mm²/s (range 0.82–1.61 × 10^-3 mm²/s) and 0.84 × 10^-3 mm²/s (range 0.17–1.56 × 10^-3 mm²/s), respectively. There was a strong positive correlation between ADCs and Ds. For clinically relevant b value combinations, maximum deviation between ADCs including and excluding low b values (<100 s/mm²) was 11.8 %.

Conclusion

Selection of b values strongly affects ADCs of malignant breast lesions. However, by excluding low b values, ADCs approach biexponential Ds, demonstrating that microperfusion influences the diffusion signal. Thus, care should be taken when ADC calculation includes low b values.

Key Points

• Diffusion-weighted sequences are increasingly used in breast magnetic resonance imaging

• Diffusion-weighting (b) values strongly influence apparent diffusion coefficients of malignant lesions

• Exclusion of low b values reduces the apparent diffusion coefficient

• Flow-insensitive monoexponential apparent diffusion coefficients approach biexponential diffusion coefficients

Fat deposition decreases diffusion parameters at MRI: a study in phantoms and patients with liver steatosis

PURPOSE

Assess the effect of fat deposition on the MRI diffusion coefficients in lipid emulsion-based phantoms and patients with proven isolated liver steatosis.

MATERIALS AND METHODS

Diffusion-weighted MRI with 11 b values from 0-500 s/mm(2) was performed in phantoms (fat fractions 0-18 %) with and without fat suppression and in 19 patients with normal liver (n = 14) or isolated liver steatosis (n = 5) proven by histopathology. The apparent, pure and perfusion-related diffusion coefficients and the perfusion fraction were measured. Spearman correlation coefficient and Mann-Whitney U test were used for comparisons.

RESULTS

A strong correlation between the apparent and pure diffusion coefficients and fat fractions was seen in phantoms. The pure diffusion coefficient decreased significantly in patients with liver steatosis (0.96 ± 0.16 × 10(-3) mm(2)/s versus 1.18 ± 0.09 × 10(-3) mm(2)/s in normal liver, P = 0.005), whereas the decrease in apparent diffusion coefficient did not reach statistical significance (1.26 ± 0.25 × 10(-3) mm(2)/s versus 1.41 ± 0.14 × 10(-3) mm(2)/s in normal liver, P = 0.298).

CONCLUSIONS

Fat deposition decreases the apparent and pure diffusion coefficients in lipid emulsion-based phantoms and patients with isolated liver steatosis proven by histopathology.

Diffusion weighted imaging of the normal breast: reproducibility of apparent diffusion coefficient measurements and variation with menstrual cycle and menopausal status

OBJECTIVES

To establish the reproducibility of apparent diffusion coefficient (ADC) measurements in normal fibroglandular breast tissue and to assess variation in ADC values with phase of the menstrual cycle and menopausal status.

METHODS

Thirty-one volunteers (13 premenopausal, 18 postmenopausal) underwent magnetic resonance twice (interval 11-22 days) using diffusion-weighted MRI. ADC(total) and a perfusion-insensitive ADC(high) (omitting b = 0) were calculated. Reproducibility and inter-observer variability of mean ADC values were assessed. The difference in mean ADC values between the two phases of the menstrual cycle and the postmenopausal breast were evaluated.

RESULTS

ADC(total) and ADC(high) showed good reproducibility (r% = 17.6, 22.4). ADC(high) showed very good inter-observer agreement (kappa = 0.83). The intraclass correlation coefficients (ICC) were 0.93 and 0.91. Mean ADC values were significantly lower in the postmenopausal breast (ADC(total) 1.46 ± 0.3 × 10(-3) mm(2)/s, ADC(high) 1.33 ± 0.3 × 10(-3) mm(2)/s) compared with the premenopausal breast (ADC(total) 1.84 ± 0.26 × 10(-3) mm(2)/s, ADC(high) 1.77 ± 0.26 × 10(-3) mm(2)/s; both P < 0.001). No significant difference was seen in ADC values in relation to menstrual cycle (ADC(total) P = 0.2, ADC(high) P = 0.24) or between postmenopausal women taking or not taking oestrogen supplements (ADC(total) P = 0.6, ADC(high) P = 0.46).

CONCLUSIONS

ADC values in fibroglandular breast tissue are reproducible. Lower ADC values within the postmenopausal breast may reduce diffusion-weighted contrast and have implications for accurately detecting tumours.

KEY POINTS

• ADC values from fibroglandular breast tissue are measured reproducibly by multiple observers. • Mean ADC values were significantly lower in postmenopausal than premenopausal breast tissue. • Mean ADC values did not vary significantly with menstrual cycle. • Low postmenopausal ADC values may hinder tumour detection on DW-MRI.

Effects of microperfusion in hepatic diffusion weighted imaging

OBJECTIVE

Clinical hepatic diffusion weighted imaging (DWI) generally relies on mono-exponential diffusion. The aim was to demonstrate that mono-exponential diffusion in the liver is contaminated by microperfusion and that the bi-exponential model is required.

METHODS

Nineteen fasting healthy volunteers were examined with DWI (seven b-values) using fat suppression and respiratory triggering (1.5 T). Five different regions in the liver were analysed regarding the mono-exponentially fitted apparent diffusion coefficient (ADC), and the bi-exponential model: molecular diffusion (D (slow)), microperfusion (D (fast)) and the respective fractions (f (slow/fast)). Data were compared using ANOVA and Kruskal-Wallis tests. Simulations were performed by repeating our data analyses, using just the DWI series acquired with b-values approximating those of previous studies.

RESULTS

Median mono-exponentially fitted ADCs varied significantly (P < 0.001) between 1.107 and 1.423 × 10(-3) mm(2)/s for the five regions. Bi-exponential fitted D(slow) varied between 0.923 and 1.062 × 10(-3) mm(2)/s without significant differences (P = 0.140). D (fast) varied significantly, between 17.8 and 46.8 × 10(-3) mm(2)/s (P < 0.001). F-tests showed that the diffusion data fitted the bi-exponential model significantly better than the mono-exponential model (F > 21.4, P < 0.010). These results were confirmed by the simulations.

CONCLUSION

ADCs of normal liver tissue are significantly dependent on the measurement location because of substantial microperfusion contamination; therefore the bi-exponential model should be used.

KEY POINTS

Diffusion weighted MR imaging helps clinicians to differentiate tumours by diffusion properties. Fast moving water molecules experience microperfusion, slow molecules diffusion. Hepatic diffusion should be measured by bi-exponential models to avoid microperfusion contamination. Mono-exponential models are contaminated with microperfusion, resulting in apparent regional diffusion differences. Bi-exponential models are necessary to measure diffusion and microperfusion in the liver.

Diffusion-weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements

Promising recent investigations have shown that breast malignancies exhibit restricted diffusion on diffusion-weighted imaging (DWI) and may be distinguished from normal tissue and benign lesions in the breast based on differences in apparent diffusion coefficient (ADC) values. In this study, we assessed the influence of intravoxel fat signal on breast diffusion measures by comparing ADC values obtained using a diffusion-weighted single shot fast spin-echo sequence with and without fat suppression. The influence of breast density on ADC measures was also evaluated. ADC values were calculated for both tumor and normal fibroglandular tissue in a group of 21 women with diagnosed breast cancer. There were systematic underestimations of ADC for both tumor and normal breast tissue due to intravoxel contribution from fat signal on non-fat-suppressed DWI. This ADC underestimation was more pronounced for normal tissue values (mean difference=40%) than for tumors (mean difference=27%, P<.001) and was worse in women with low breast tissue density vs. those with extremely dense breasts (P<.05 for both tumor and normal tissue). Tumor conspicuity measured by contrast-to-noise ratio was significantly higher on ADC maps created with fat suppression and was not significantly associated with breast density. In summary, robust fat suppression is important for accurate breast ADC measures and optimal lesion conspicuity on DWI.

Intravoxel incoherent motion imaging of tumor microenvironment in locally advanced breast cancer

Diffusion-weighted imaging plays important roles in cancer diagnosis, monitoring, and treatment. Although most applications measure restricted diffusion by tumor cellularity, diffusion-weighted imaging is also sensitive to vascularity through the intravoxel incoherent motion effect. Hypervascularity can confound apparent diffusion coefficient measurements in breast cancer. We acquired multiple b-value diffusion-weighted imaging at 3 T in a cohort of breast cancer patients and performed biexponential intravoxel incoherent motion analysis to extract tissue diffusivity (D(t)), perfusion fraction (f(p)), and pseudodiffusivity (D(p)). Results indicated significant differences between normal fibroglandular tissue and malignant lesions in apparent diffusion coefficient mean (±standard deviation) values (2.44 ± 0.30 vs. 1.34 ± 0.39 μm(2)/msec, P < 0.01) and D(t) (2.36 ± 0.38 vs. 1.15 ± 0.35 μm(2)/msec, P < 0.01). Lesion diffusion-weighted imaging signals demonstrated biexponential character in comparison to monoexponential normal tissue. There is some differentiation of lesion subtypes (invasive ductal carcinoma vs. other malignant lesions) with f(p) (10.5 ± 5.0% vs. 6.9 ± 2.9%, P = 0.06), but less so with D(t) (1.14 ± 0.32 μm(2)/msec vs. 1.18 ± 0.52 μm(2)/msec, P = 0.88) and D(p) (14.9 ± 11.4 μm(2)/msec vs. 16.1 ± 5.7 μm(2)/msec, P = 0.75). Comparison of intravoxel incoherent motion biomarkers with contrast enhancement suggests moderate correlations. These results suggest the potential of intravoxel incoherent motion vascular and cellular biomarkers for initial grading, progression monitoring, or treatment assessment of breast tumors.