Lesion-mimicking DIXON swap artifact in contrast-enhanced subtraction breast MRI.

Breast cancer is the most common cancer in women; approximately 1 in 8 women is diagnosed with breast cancer in their lifetime. Some women are at significantly higher risk of developing breast cancer, including women carrying mutations in the BRCA1/2, TP53, or other genes and women with other risk factors. Women with a high lifetime risk for breast cancer are frequently offered annual breast magnetic resonance imaging (MRI) examinations for early breast cancer detection. Breast MRI is commonly performed using a multiparametric imaging protocol, including dynamic contrast-enhanced T1-weighted acquisitions. The dynamic contrast-enhanced T1-weighted acquisitions are frequently transformed into subtraction series, allowing the focused visualization of areas with high signal intensity and masses associated with elevated contrast agent uptake, which are among the hallmarks of suspicious findings. Here, we report a case in which a suspicious lesion-mimicking swap artifact occurred using a T1-weighted contrast-enhanced DIXON acquisition technique in a high-risk breast cancer screening MRI examination.

Detectability of Breast Cancer in Dedicated Breast CT Compared With Mammography Dependent on Breast Density.

OBJECTIVES: To evaluate the detectability of non-contrast-enhanced and contrast-enhanced spiral breast computed tomography ([non]-CE-SBCT) compared with mammography. Secondary objectives are to determine detectability depending on breast density and to evaluate appearance of breast malignancies according to BI-RADS descriptors. METHODS: This retrospective institutional review board-approved study included 90 women with 105 biopsy-proven malignant breast lesions. Breast density, BI-RADS descriptors, and detectability were evaluated by 2 independent readers. Diagnostic confidence was rated on a 4-point Likert scale. RESULTS: For readers 1 and 2, detectability was 83.8% and 80.0% for mammography, 99.1% and 99.1% for CE-SBCT ( P < 0.05), and 66.7% and 61.9% for non-CE-SBCT ( P < 0.05). With both readers, detectability in CE-SBCT was high for density A/B/C/D (both 100%/100%/100%/87.5%). Detectability of readers declined with increasing density for mammography (density A = 100%, B = 89.1% and 95.1%, C = 73.1%, D = 50.0% and 71.4%; P < 0.05) and for non-CE-SBCT (density A = 87.5% and 90.7%, B = 65.5% and 69.1%, C = 54.8% and 60.0%, D = 37.5%; P < 0.05). Mass lesions were detected with CT as often as with mammography, whereas architectural distortions and microcalcifications were detected less often with SBCT. Diagnostic confidence was very high or high in 97.2% for CE-SBCT, in 74.1% for non-CE-SBCT, and in 81.4% for mammography. CONCLUSIONS: Detectability and diagnostic confidence were very high in CE-SBCT, regardless of breast density. The detectability of non-CE-SBCT was lower than that of mammography and declined with increasing breast density.

Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions.

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Diffusion-Weighted Imaging for Skin Pathologies of the Breast-A Feasibility Study.

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget's disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model.

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions.

Accumulation of Iron Oxide-Based Contrast Agents in Rabbit Atherosclerotic Plaques in Relation to Plaque Age and Vulnerability Features.

Purpose: In this study, a detailed characterization of a rabbit model of atherosclerosis was performed to assess the optimal time frame for evaluating plaque vulnerability using superparamagnetic iron oxide nanoparticle (SPION)-enhanced magnetic resonance imaging (MRI). Methods: The progression of atherosclerosis induced by ballooning and a high-cholesterol diet was monitored using angiography, and the resulting plaques were characterized using immunohistochemistry and histology. Morphometric analyses were performed to evaluate plaque size and vulnerability features. The accumulation of SPIONs (novel dextran-coated SPIONDex and ferumoxytol) in atherosclerotic plaques was investigated by histology and MRI and correlated with plaque age and vulnerability. Toxicity of SPIONDex was evaluated in rats. Results: Weak positive correlations were detected between plaque age and intima thickness, and total macrophage load. A strong negative correlation was observed between the minimum fibrous cap thickness and plaque age as well as the mean macrophage load. The accumulation of SPION in the atherosclerotic plaques was detected by MRI 24 h after administration and was subsequently confirmed by Prussian blue staining of histological specimens. Positive correlations between Prussian blue signal in atherosclerotic plaques, plaque age, and macrophage load were detected. Very little iron was observed in the histological sections of the heart and kidney, whereas strong staining of SPIONDex and ferumoxytol was detected in the spleen and liver. In contrast to ferumoxytol, SPIONDex administration in rabbits was well tolerated without inducing hypersensitivity. The maximum tolerated dose in rat model was higher than 100 mg Fe/kg. Conclusion: Older atherosclerotic plaques with vulnerable features in rabbits are a useful tool for investigating iron oxide-based contrast agents for MRI. Based on the experimental data, SPIONDex particles constitute a promising candidate for further clinical translation as a safe formulation that offers the possibility of repeated administration free from the risks associated with other types of magnetic contrast agents.

Initial experience with a next-generation low-field MRI scanner: Potential for breast imaging?

PURPOSE: Broader clinical adoption of breast magnetic resonance imaging (MRI) faces challenges such as limited availability and high procedural costs. Low-field technology has shown promise in addressing these challenges. We report our initial experience using a next-generation scanner for low-field breast MRI at 0.55T. METHODS: This initial cases series was part of an institutional review board-approved prospective study using a 0.55T scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen/Germany: height < 2 m, weight < 3.2 tons, no quench pipe) equipped with a seven-channel breast coil (Noras, Höchberg/Germany). A multiparametric breast MRI protocol consisting of dynamic T1-weighted, T2-weighted, and diffusion-weighted sequences was optimized for 0.55T. Two radiologists with 12 and 20 years of experience in breast MRI evaluated the examinations. RESULTS: Twelve participants (mean age: 55.3 years, range: 36-78 years) were examined. The image quality was diagnostic in all examinations and not impaired by relevant artifacts. Typical imaging phenotypes were visualized. The scan time for a complete, non-abbreviated breast MRI protocol ranged from 10:30 to 18:40 min. CONCLUSION: This initial case series suggests that low-field breast MRI is feasible at diagnostic image quality within an acceptable examination time.

Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration.

OBJECTIVES: To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. MATERIALS AND METHODS: This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285). RESULTS: After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. CONCLUSION: This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. CLINICAL RELEVANCE STATEMENT: Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. KEY POINTS: • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches.

Feasibility of In Vivo Metal Artifact Reduction in Contrast-Enhanced Dedicated Spiral Breast Computed Tomography.

BACKGROUND: Radiopaque breast markers cause artifacts in dedicated spiral breast-computed tomography (SBCT). This study investigates the extent of artifacts in different marker types and the feasibility of reducing artifacts through a metal artifact reduction (MAR) algorithm. METHODS: The pilot study included 18 women who underwent contrast-enhanced SBCT. In total, 20 markers of 4 different types were analyzed for artifacts. The extent of artifacts with and without MAR was measured via the consensus of two readers. Image noise was quantitatively evaluated, and the effect of MAR on the detectability of breast lesions was evaluated on a 3-point Likert scale. RESULTS: Breast markers caused significant artifacts that impaired image quality and the detectability of lesions. MAR decreased artifact size in all analyzed cases, even in cases with multiple markers in a single slice. The median length of in-plain artifacts significantly decreased from 31 mm (range 11-51 mm) in uncorrected to 2 mm (range 1-5 mm) in corrected images (p ≤ 0.05). Artifact size was dependent on marker size. Image noise in slices affected by artifacts was significantly lower in corrected (13.6 ± 2.2 HU) than in uncorrected images (19.2 ± 6.8 HU, p ≤ 0.05). MAR improved the detectability of lesions affected by artifacts in 5 out of 11 cases. CONCLUSION: MAR is feasible in SBCT and improves the image quality and detectability of lesions.

Prevalence and influencing factors for artifact development in breast MRI-derived maximum intensity projections.

BACKGROUND: Magnetic resonance imaging (MRI) provides high diagnostic sensitivity for breast cancer. However, MRI artifacts may impede the diagnostic assessment. This is particularly important when evaluating maximum intensity projections (MIPs), such as in abbreviated MRI (AB-MRI) protocols, because high image quality is desired as a result of fewer sequences being available to compensate for problems. PURPOSE: To describe the prevalence of artifacts on dynamic contrast enhanced (DCE) MRI-derived MIPs and to investigate potentially associated attributes. MATERIAL AND METHODS: For this institutional review board approved retrospective analysis, MIPs were generated from subtraction series and cropped to represent the left and right breasts as regions of interest. These images were labeled by three independent raters regarding the presence of MRI artifacts. MRI artifact prevalence and associations with patient characteristics and technical attributes were analyzed using descriptive statistics and generalized linear models (GLMMs). RESULTS: The study included 2524 examinations from 1794 patients (median age 50 years), performed on 1.5 and 3.0 Tesla MRI systems. Overall inter-rater agreement was kappa = 0.54. Prevalence of significant unilateral artifacts was 29.2% (736/2524), whereas bilateral artifacts were present in 37.8% (953/2524) of all examinations. According to the GLMM, artifacts were significantly positive associated with age (odds ratio [OR] = 1.52) and magnetic field strength (OR = 1.55), whereas a negative effect could be shown for body mass index (OR = 0.95). CONCLUSION: MRI artifacts on DCE subtraction MIPs of the breast, as used in AB-MRI, are a relevant topic. Our results show that, besides the magnetic field strength, further associated attributes are patient age and body mass index, which can provide possible targets for artifact reduction.

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.

The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future.

Low-Risk Women with Suspicious Microcalcifications in Mammography-Can an Additional Breast MRI Reduce the Biopsy Rate?

BACKGROUND: In the German Mammography Screening Program, 62% of ductal carcinoma in situ (DCIS) and 38% of invasive breast cancers are associated with microcalcifications (MCs). Vacuum-assisted stereotactic breast biopsies are necessary to distinguish precancerous lesions from benign calcifications because mammographic discrimination is not possible. The aim of this study was to investigate if breast magnetic resonance imaging (MRM) could assist the evaluation of MCs and thus help reduce biopsy rates. METHODS: In this IRB-approved study, 58 women (mean age 58 +/- 24 years) with 59 suspicious MC clusters in the MG were eligible for this prospective single-center trial. Additional breast magnetic resonance imaging (MRI) was conducted before biopsy. RESULTS: The breast MRI showed a sensitivity of 86%, a specificity of 84%, a positive predictive value (PPV) of 75% and a negative predictive value (NPV) of 91% for the differentiation between benign and malignant in these 59 MCs found with MG. Breast MRI in addition to MG could increase the PPV from 36% to 75% compared to MG alone. The MRI examination led to nine additional suspicious classified lesions in the study cohort. A total of 55% (5/9) of them turned out to be malignant. A total of 32 of 59 (54 %) women with suspicious MCs and benign histology were classified as non-suspicious by MRI. CONCLUSION: An additionally performed breast MRI could have increased the diagnostic reliability in the assessment of MCs. Further, in our small cohort, a considerable number of malignant lesions without mammographically visible MCs were revealed.

Spiral breast computed tomography with a photon-counting detector (SBCT): The future of breast imaging?

Dedicated breast computed tomography (BCT) is an emerging breast imaging modality. The latest development has been the introduction of a spiral breast computed tomography scanner equipped with a photon-counting detector (SBCT). SBCT promises multiple advantages: Unlike conventional mammography, contrast enhanced spectral mammography (CESM: both 2D), and digital breast tomosynthesis (DBT: pseudo 3D), SBCT enables 3D breast imaging without tissue overlap. SBCT achieves high isotropic spatial resolution of breast tissue enabling the assessment of both soft tissue and microcalcifications. Similar to CESM and MRI, SBCT supports contrast-enhanced imaging, enabling the assessment of breast neovascularization. Unlike mammography and its derived methods (CESM, DBT), SBCT does not require compression of the breast. Accordingly, women consistently report significantly increased patient comfort compared to mammography in a previous investigation. Radiation safety is crucial in breast imaging. Studies showed different results in terms of dose, with some staying within the limits of two-view FFDM defined by the ACR and others exceeding the limit by up to 21%. Therefore, a higher radiation dose compared to state-of-the-art mammography and DBT systems has to be acknowledged. SBCT is currently under scientific investigation in multiple trials. Three major indications are currently explored: Whereas our colleagues in Zurich/Switzerland investigate the role of SBCT for opportunistic screening, in our department SBCT is mainly indicated for the work-up of equivocal lesions, and for preoperative staging. In this narrative review, we summarize the concepts of SBCT and potential implications for patient care. We report on our initial clinical experience with the technology and outline future developments of SBCT.

Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast.

OBJECTIVES: To automatically detect MRI artifacts on dynamic contrast-enhanced (DCE) maximum intensity projections (MIPs) of the breast using deep learning. METHODS: Women who underwent clinically indicated breast MRI between October 2015 and December 2019 were included in this IRB-approved retrospective study. We employed two convolutional neural network architectures (ResNet and DenseNet) to detect the presence of artifacts on DCE MIPs of the left and right breasts. Networks were trained on images acquired up to and including the year 2018 using a 5-fold cross-validation (CV). Ensemble classifiers were built with the resulting CV models and applied to an independent holdout test dataset, which was formed by images acquired in 2019. RESULTS: Our study sample contained 2265 examinations from 1794 patients (median age at first acquisition: 50 years [IQR: 17 years]), corresponding to 1827 examinations of 1378 individuals in the training dataset and 438 examinations of 416 individuals in the holdout test dataset with a prevalence of image-level artifacts of 53% (1951/3654 images) and 43% (381/876 images), respectively. On the holdout test dataset, the ResNet and DenseNet ensembles demonstrated an area under the ROC curve of 0.92 and 0.94, respectively. CONCLUSION: Neural networks are able to reliably detect artifacts that may impede the diagnostic assessment of MIPs derived from DCE subtraction series in breast MRI. Future studies need to further explore the potential of such neural networks to complement quality assurance and improve the application of DCE MIPs in a clinical setting, such as abbreviated protocols. KEY POINTS: • Deep learning classifiers are able to reliably detect MRI artifacts in dynamic contrast-enhanced protocol-derived maximum intensity projections of the breast. • Automated quality assurance of maximum intensity projections of the breast may be of special relevance for abbreviated breast MRI, e.g., in high-throughput settings, such as cancer screening programs.

Differentiating Benign and Malignant Breast Lesions in Diffusion Kurtosis MRI: Does the Averaging Procedure Matter?

BACKGROUND: Diffusion kurtosis imaging (DKI) is used to differentiate between benign and malignant breast lesions. DKI fits are performed either on voxel-by-voxel basis or using volume-averaged signal. PURPOSE: Investigate and compare DKI parameters' diagnostic performance using voxel-by-voxel and volume-averaged signal fit approach. STUDY TYPE: Retrospective. STUDY POPULATION: A total of 104 patients, aged 24.1-86.4 years. FIELD STRENGTH/SEQUENCE: A 3 T Spin-echo planar diffusion-weighted sequence with b-values: 50 s/mm2 , 750 s/mm2 , and 1500 s/mm2 . Dynamic contrast enhanced (DCE) sequence. ASSESSMENT: Lesions were manually segmented by M.P. under supervision of S.O. (2 and 5 years of experience in breast MRI). DKI fits were performed on voxel-by-voxel basis and with volume-averaged signal. Diagnostic performance of DKI parameters D K (kurtosis corrected diffusion coefficient) and kurtosis K was compared between both approaches. STATISTICAL TESTS: Receiver operating characteristics analysis and area under the curve (AUC) values were computed. Wilcoxon rank sum and Students t-test tested DKI parameters for significant (P <0.05) difference between benign and malignant lesions. DeLong test was used to test the DKI parameter performance for significant fit approach dependency. Correlation between parameters of the two approaches was determined by Pearson correlation coefficient. RESULTS: DKI parameters were significantly different between benign and malignant lesions for both fit approaches. Median benign vs. malignant values for voxel-by-voxel and volume-averaged approach were 2.00 vs. 1.28 ( D K in µm2 /msec), 2.03 vs. 1.26 ( D K in µm2 /msec), 0.54 vs. 0.90 ( K ), 0.55 vs. 0.99 ( K ). AUC for voxel-by-voxel and volume-averaged fit were 0.9494 and 0.9508 ( D K ); 0.9175 and 0.9298 ( K ). For both, AUC did not differ significantly (P = 0.20). Correlation of values between the two approaches was very high (r = 0.99 for D K and r = 0.97 for K ). DATA CONCLUSION: Voxel-by-voxel and volume-averaged signal fit approach are equally well suited for differentiating between benign and malignant breast lesions in DKI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Potential of spiral breast computed tomography to increase patient comfort compared to DM.

PURPOSE: To intra-individually compare patient comfort of spiral breast computed tomography (SBCT) versus digital mammography (DM). METHOD: This prospective IRB approved study included 79 patients undergoing both SBCT and DM for the assessment of BI-RADS 4 - 6 lesions. Following SBCT and DM patients answered a standardized questionnaire regarding "Overall patient comfort" and "Pain" on a 5-point Likert Scale. On the same Likert Scale, experienced radiologic technicians rated the workflow of the SBCT regarding patients' "Mobility", ease of patient "Positioning", patients' adherence to the examination ("Compliance") and expected image quality. Visibility of fibroglandular tissue in SBCT was independently rated by two breast radiologists on a 10-point Likert Scale. Subgroups stratified by menopausal status and body mass index (BMI) were analyzed. RESULTS: Patients reported significantly lower pain during SBCT (4.73 ± 0.57) compared to DM (4.09 ± 0.90; P < 0.01). This effect was independent from BMI. However, pain reduction by SBCT was most pronounced in premenopausal (SBCT vs. DM: 4.79 ± 0.50 vs. 3.89 ± 0.99) compared to postmenopausal patients (4.71 ± 0.77 vs. 4.20 ± 0.89). Overall patient comfort in premenopausal patients tended to be higher in SBCT compared to DM (P = 0.08). Radiologic technicians rated the SBCT procedure generally as positive (average: 4.62 ± 0.56). Coverage of fibroglandular tissue in SBCT was generally high (9.82 ± 0.43) and interrater agreement was good (κ = 0.77). CONCLUSIONS: Patients experience less pain during spiral breast computed tomography compared to DM, especially in premenopausal women. Imaging is feasible at a high level of anatomical breast coverage and without problems with the clinical workflow.

Ultra-High b-Value Diffusion-Weighted Imaging-Based Abbreviated Protocols for Breast Cancer Detection.

OBJECTIVES: Contrast-enhanced (CE) magnetic resonance imaging (MRI) is the most effective imaging modality for breast cancer detection. A contrast agent-free examination technique would be desirable for breast MRI screening. The purpose of this study was to evaluate the capability to detect and characterize suspicious breast lesions with an abbreviated, non-contrast-enhanced MRI protocol featuring ultra-high b-value diffusion-weighted imaging (DWI) compared with CE images. MATERIALS AND METHODS: The institutional review board-approved prospective study included 127 female subjects with different clinical indications for breast MRI. Magnetic resonance imaging examinations included DWI sequences with b-values of 1500 s/mm2 (b1500) and 2500 s/mm2 (b2500), native T1- and T2-weighted images, and CE sequences at 1.5 T and 3 T scanners. Two reading rounds were performed, including either the b1500 or the b2500 DWI in consecutive assessment steps: (A) maximum intensity projections (MIPs) of DWI, (B) DWI and apparent diffusion coefficient maps, (C) as (B) but with additional native T1- and T2-weighted images, and (D) as (C) but with additional CE images (full-length protocol). Two readers independently determined the presence of a suspicious lesion. Histological confirmation was obtained for conspicuous lesions, whereas the full MRI data set was obtained for inconspicuous and clearly benign lesions. Statistical analysis included calculation of diagnostic accuracy and interrater agreement via the intraclass correlation coefficient. RESULTS: The cohort comprised 116 cases with BI-RADS 1 findings and 138 cases with BI-RADS ≥2 findings, including 38 histologically confirmed malignancies. For (A), breasts without pathological findings could be recognized with high diagnostic accuracy (negative predictive value, ≥97.0%; sensitivity, ≥92.1% for both readers), but with a limited specificity (≥58.3%; positive predictive value, ≥28.6%). Within the native readings, approach (C) with b2500 performed best (negative predictive value, 99.5%; sensitivity, 97.4%; specificity, 88.4%). The intraclass correlation coefficient was between 0.683 (MIP b1500) and 0.996 (full protocol). CONCLUSIONS: A native abbreviated breast MRI protocol with advanced high b-value DWI might allow nearly equivalent diagnostic accuracy as CE breast MRI and seems to be well suited for lesion detection purposes.

Detection of Microcalcifications in Spiral Breast Computed Tomography with Photon-Counting Detector Is Feasible: A Specimen Study.

The primary objective of the study was to compare a spiral breast computed tomography system (SBCT) to digital breast tomosynthesis (DBT) for the detection of microcalcifications (MCs) in breast specimens. The secondary objective was to compare various reconstruction modes in SBCT. In total, 54 breast biopsy specimens were examined with mammography as a standard reference, with DBT, and with a dedicated SBCT containing a photon-counting detector. Three different reconstruction modes were applied for SBCT datasets (Recon1 = voxel size (0.15 mm)3, smooth kernel; Recon2 = voxel size (0.05 mm)3, smooth kernel; Recon3 = voxel size (0.05 mm)3, sharp kernel). Sensitivity and specificity of DBT and SBCT for the detection of suspicious MCs were analyzed, and the McNemar test was used for comparisons. Diagnostic confidence of the two readers (Likert Scale 1 = not confident; 5 = completely confident) was analyzed with ANOVA. Regarding detection of MCs, reader 1 had a higher sensitivity for DBT (94.3%) and Recon2 (94.9%) compared to Recon1 (88.5%; p < 0.05), while sensitivity for Recon3 was 92.4%. Respectively, reader 2 had a higher sensitivity for DBT (93.0%), Recon2 (92.4%), and Recon3 (93.0%) compared to Recon1 (86.0%; p < 0.05). Specificities ranged from 84.7-94.9% for both readers (p > 0.05). The diagnostic confidence of reader 1 was better with SBCT than with DBT (DBT 4.48 ± 0.88, Recon1 4.77 ± 0.66, Recon2 4.89 ± 0.44, and Recon3 4.75 ± 0.72; DBT vs. Recon1/2/3: p < 0.05), while reader 2 found no differences. Sensitivity and specificity for the detection of MCs in breast specimens is equal for DBT and SBCT when a small voxel size of (0.05 mm)3 is used with an equal or better diagnostic confidence for SBCT compared to DBT.

A mixed waveform protocol for reduction of the cardiac motion artifact in black-blood diffusion-weighted imaging of the liver.

OBJECTIVE: Diffusion-weighted imaging (DWI) in the liver suffers from signal loss due to the cardiac motion artifact, especially in the left liver lobe. The purpose of this work was to improve the image quality of liver DWI in terms of cardiac motion artifact reduction and achievement of black-blood images in low b-value images. MATERIAL AND METHODS: Ten healthy volunteers (age 20-31 years) underwent MRI examinations at 1.5 T with a prototype DWI sequence provided by the vendor. Two diffusion encodings (i.e. waveforms), monopolar and flow-compensated, and the b-values 0, 20, 50, 100, 150, 600 and 800 s/mm2 were used. Two Likert scales describing the severity of the pulsation artifact and the quality of the black-blood state were defined and evaluated by two experienced radiologists. Regions of interest (ROIs) were manually drawn in the right and left liver lobe in each slice and combined to a volume of interest (VOI). The mean and coefficient of variation were calculated for each normalized VOI-averaged signal to assess the severity of the cardiac motion artifact. The ADC was calculated using two b-values once for the monopolar data and once with mixed data, using the monopolar data for the small and the flow-compensated data for the high b-value. A Wilcoxon rank sum test was used to compare the Likert scores obtained for monopolar and flow-compensated data. RESULTS: At b-values from 20 to 150 s/mm2, unlike the flow-compensated diffusion encoding, the monopolar encoding yielded black blood in all images with a negligible signal loss due to the cardiac motion artifact. At the b-values 600 and 800 s/mm2, the flow-compensated encoding resulted in a significantly reduced cardiac motion artifact, especially in the left liver lobe, and in a black-blood state. The ADC calculated with monopolar data was significantly higher in the left than in the right liver lobe. CONCLUSION: It is recommendable to use the following mixed waveform protocol: Monopolar diffusion encodings at small b-values and flow-compensated diffusion encodings at high b-values.

Diffusion kurtosis imaging does not improve differentiation performance of breast lesions in a short clinical protocol.

BACKGROUND: Diffusion weighted magnetic resonance imaging (DWI) is known to differentiate between malignant and benign lesions via the apparent diffusion coefficient (ADC). Here, the value of diffusion kurtosis imaging (DKI) for differentiation and further characterization of benign and malignant breast lesions and their subtypes in a clinically feasible protocol is investigated. MATERIAL AND METHODS: This study included 85 patients (with 68 malignant and 73 benign lesions) who underwent 3 T breast DWI using three b values (50, 750, 1500 s/mm2), with a total measurement time < 5 min. ADC maps were calculated from b values 50, 750 s/mm2. The diffusion kurtosis model was fitted to the diffusion weighted images, yielding in each lesion the average kurtosis-corrected diffusion coefficient DK and mean kurtosis K. Histopathology was obtained of radiologically suspicious lesions; follow-up scans were used as a standard of reference for benign appearing lesions. Receiver operating characteristic curves were used to evaluate the parameters' diagnostic performance for differentiation of lesion types and grades. The difference in diffusion parameters between subgroups was analysed statistically using the Wilcoxon rank sum test and Kruskal-Wallis test, applying a Bonferroni correction for multiple testing where necessary. RESULTS: ADC, DK and K showed significant differences between malignant and benign lesions (p < 10-5). All parameters had similar areas under the curve (AUC) (ADC: 0.92, DK: 0.91, K: 0.89) for differentiation of malignant and benign lesions. Sensitivity was highest for ADC (ADC: 0.96, DK: 0.94, K: 0.93), as well as specificity (ADC: 0.85, DK: 0.82, K: 0.82). ADC and DK showed significant differences between tumor histologic grades (p = 6.8⋅10-4, p = 6.6 ·â€¯10-5, respectively), whereas K did not (p = 0.99). All three parameters differed significantly between subtypes of benign lesions (ADC: p < 10-5, DK: p< 10-5, K: p = 4.1·10-4), but not between subtypes of malignant lesions (ADC: p = 0.21, DK: p = 0.25, K: p = 0.08). CONCLUSION: DKI parameters and conventional ADC can differentiate between malignant and benign lesions. Differentiation performance was best for ADC. Different tumor grades were significantly different in ADC and DK, which may have an impact on therapy planning and monitoring. In our study, K did not add value to the diagnostic performance of DWI in a clinical setting.