Usefulness of IDEAL T2 imaging for homogeneous fat suppression and reducing susceptibility artefacts in brachial plexus MRI at 3.0 T.

PURPOSE: To quantitatively and qualitatively compare fat-suppressed MR imaging quality using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with that using frequency-selective fat-suppressed (FSFS) T2 images of the brachial plexus at 3.0 T. MATERIALS AND METHODS: Prospective MR image analysis was performed in 40 volunteers and 40 patients at a single centre. Oblique-sagittal and coronal IDEAL fat-suppressed T2 images and FSFS T2 images were compared. Visual assessment was performed by two independent musculoskeletal radiologists with respect to: (1) susceptibility artefacts around the neck, (2) homogeneity of fat suppression, (3) image sharpness and (4) tissue resolution contrast of pathologies. The signal-to-noise ratios (SNR) for each image sequence were assessed. RESULTS: Compared to FSFS sequences, IDEAL fat-suppressed T2 images significantly reduced artefacts around the brachial plexus and significantly improved homogeneous fat suppression (p < 0.05). IDEAL significantly improved sharpness and lesion-to-tissue contrast (p < 0.05). The mean SNRs were significantly improved on T2-weighted IDEAL images (p < 0.05). CONCLUSION: IDEAL technique improved image quality by reducing artefacts around the brachial plexus while maintaining a high SNR and provided superior homogeneous fat suppression than FSFS sequences.

Quantitative evaluation of background parenchymal enhancement (BPE) on breast MRI. A feasibility study with a semi-automatic and automatic software compared to observer-based scores.

OBJECTIVE: To evaluate quantitative measurements of background parenchymal enhancement (BPE) on breast MRI and compare them with observer-based scores. METHODS: BPE of 48 patients (mean age: 48 years; age range: 36-66 years) referred to 3.0-T breast MRI between 2012 and 2014 was evaluated independently and blindly to each other by two radiologists. BPE was estimated qualitatively with the standard Breast Imaging Reporting and Data System (BI-RADS) scale and quantitatively with a semi-automatic and an automatic software interface. To assess intrareader agreement, MRIs were re-read after a 4-month interval by the same two readers. The Pearson correlation coefficient (r) and the Bland-Altman method were used to compare the methods used to estimate BPE. p-value <0.05 was considered significant. RESULTS: The mean value of BPE with the semi-automatic software evaluated by each reader was 14% (range: 2-79%) for Reader 1 and 16% (range: 1-61%) for Reader 2 (p > 0.05). Mean values of BPE percentages for the automatic software were 17.5 ± 13.1 (p > 0.05 vs semi-automatic). The automatic software was unable to produce BPE values for 2 of 48 (4%) patients. With BI-RADS, interreader and intrareader values were κ = 0.70 [95% confidence interval (CI) 0.49-0.91] and κ = 0.69 (95% CI 0.46-0.93), respectively. With semi-automated software, interreader and intrareader values were κ = 0.81 (95% CI 0.59-0.99) and κ = 0.85 (95% CI 0.43-0.99), respectively. BI-RADS scores correlated with the automatic (r = 0.55, p < 0.001) and semi-automatic scores (r = 0.60, p < 0.001). Automatic scores correlated with the semi-automatic scores (r = 0.77, p < 0.001). The mean percentage difference between automatic and semi-automatic scores was 3.5% (95% CI 1.5-5.2). CONCLUSION: BPE quantitative evaluation is feasible with both semi-automatic and automatic software and correlates with radiologists' estimation. ADVANCES IN KNOWLEDGE: Computerized BPE quantitative evaluation is feasible with both semi-automatic and automatic software. Computerized BPE quantitative scores correlate with radiologists' estimation.

High-resolution ultrasound of peripheral nerves in systemic sclerosis: a pilot study of computer-aided quantitative assessment of nerve density.

OBJECTIVES: To quantitatively evaluate and compare nerve density in patients with limited cutaneous systemic sclerosis (lcSSc) and control subjects using high-resolution ultrasound (US) with a computer-aided assessment. METHODS: Forty patients and 40 age- and sex-matched control subjects were prospectively enrolled. Ultrasound (US) examination (17-5 MHz probe) of the median nerve at the elbow was performed bilaterally by one radiologist. A software quantified the ratio between the hypoechoic and hyperechoic areas of peripheral nerves on ultrasound. Two observers set the threshold in the images acquired, and three observers performed the digital analysis of nerve density. Statistical analysis included Mann-Whitney U-test of patients versus control subjects and subgroup analysis of symptomatic and non-symptomatic patients. Intra and inter-observer agreement of the three observers were assessed with the kappa statistic. RESULTS: In all, 160 median nerves were evaluated. According to the US, nerve density was significantly reduced in lcSSc patients compared to control subjects (mean and standard deviation: 41 ± 3 vs 56 ± 4, p < 0.01). Subgroup analysis showed that symptomatic patients (n = 15) had reduced nerve density compared to non-symptomatic (n = 25) patients (39 ± 5 vs 43 ± 4, p < 0.01). Intra-observer agreement was very good (K = 0.82). Inter-observer agreements were good: reader 1 vs reader 2: k = 0.78 (95% confidence interval 0.65 to 0.91); reader 2 vs reader 3: k = 0.72 (95% confidence interval 0.65 to 0.82); reader 3 vs reader 1: k = 0.71 (95% confidence interval 0.64-0.81). CONCLUSIONS: In lcSSc patients, nerve density was reduced, especially in the symptomatic group, compared to control subjects.

Peripheral nerve MRI: precision and reproducibility of T2*-derived measurements at 3.0-T : a feasibility study.

OBJECTIVE: To prospectively evaluate the precision and reproducibility of T2*-derived measurements of the peripheral nerves. MATERIALS AND METHODS: The study was approved by the local ethics committee and written informed consent was obtained. Bilateral upper and lower limb MRI examination was performed in 40 healthy subjects on a 3.0-T scanner. MRI protocol included T1-turbo spin-echo, T2-turbo spin-echo with fat suppression, and multiecho gradient recalled echo. Measurements of T2* times on T2* maps at different anatomical levels were performed. Three authors measured independently and in different sessions at baseline and after 4 weeks. Non-parametric tests and Bland-Altman statistics were used. RESULTS: Minimum and maximum percentage variability were 10 % and 19 % for T2* (84-91 % of reproducibility). Maximum values of minimum detectable differences between limbs was 16 % (with 95 % CI: 2-37). Intra- and inter-observer agreement of the three radiologists for T2* was considered good. Evaluating the combined influence of the observer and of the repeated measurements the reproducibility was 87-98 %. CONCLUSIONS: T2* measurement of the peripheral nerves is precise and reproducible. The healthy contralateral side can be used as an internal control. Variations in T2* values up to 16 % have to be considered.

Characterisation of microcalcification clusters on 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT): does DBT underestimate microcalcification clusters? Results of a multicentre study.

OBJECTIVES: To compare DBT and FFDM in the classification of microcalcification clusters (MCs) using BI-RADS. METHODS: This Institutional Review Board-approved study was undertaken in three centres. A total of 107 MCs evaluated with both DBT and FFDM were randomised for prospective reading by six experienced breast radiologists and classified using BI-RADS. RESULTS: The benign/malignant ratio of MC was 66/41. Of 11/107 discordant results, DBT classified MCs as R2 whereas FFDM classified them as R3 in 9 and R4 in 2. Three of these (3/107 = 2.8%) were malignant; 8 (7.5%) were nonmalignant and were correctly classified as R2 on DBT but incorrectly classified as R3 on FFDM. Estimated sensitivity and specificity, respectively, were 100% (95% CI: 91% to 100%) and 94.6% (95% CI: 86.7% to 98.5%) for FFDM and 91.1% (95% CI: 78.8% to 97.5%) and 100% (95% CI: 94.8% to 100%) for DBT. Overall intra- and interobserver agreements were 0.75 (95% CI: 0.61-0.84) and 0.73 (95% CI: 0.62-0.78). CONCLUSIONS: Most MCs are scored similarly on FFDM and DBT. Although a minority (11/107) of MCs are classified differently on FFDM (benign MC classified as R3) and DBT (malignant MC classified as R2), this may have clinical relevance. KEY POINTS: • The BI-RADS classification of MC differs for FFDM and DBT in 11/107 cases • DBT assigned lower BI-RADS classes compared to FFDM in 11 clusters • In 4/107 DBT may have missed some malignant and high-risk lesions • In 7/107 the 'underclassification' on DBT was correct, potentially avoiding unnecessary biopsies • DBT may miss a small proportion of malignant lesions.

Fascicular ratio: a new parameter to evaluate peripheral nerve pathology on magnetic resonance imaging: a feasibility study on a 3T MRI system.

The objective of the study was to define and quantitatively evaluate the fascicular ratio (FR) on magnetic resonance imaging (MRI) in patients with peripheral neuropathies compared with healthy controls. Forty control subjects (20 women, 20 men; age, 44.6 ± 13.4 years) and 40 patients with peripheral neuropathy (22 women, 18 men; age, 50.3 ± 10.2 years) were examined with a standard 3T MRI protocol. With customized software (with semiautomatic and automatic interface), the hypointense and hyperintense areas of the peripheral nerves corresponding to fascicular and nonfascicular tissue were examined on T1-weighted sequences. The ratio of fascicular pixels to total pixels was called FR. Correlation with FR calculated on high-resolution ultrasound was performed. The statistical analysis included the Mann-Whitney U test of controls versus patients, the receiver operating characteristic (ROC) analysis, and the subgroup analysis of patients according to etiologies of neuropathy. Intraobserver and interobserver agreement was calculated based on the evaluation made by 3 readers. Finally, a complete automatic evaluation was performed. On MRI, FRs were significantly increased in patients compared with controls (FR, 76.7 ± 15.1 vs 56 ± 12.3; P < 0.0001 for the semiautomatic interface; and FR 66.3 ± 17.5 vs 47.8 ± 18.4; P < 0.0001 for the automatic interface). The increase in FR was caused mainly by an increase in the hypointense part of the nerve. This observation was valid for all causes of neuropathies. ROC analysis found an area under the curve of 0.75 (95% confidence interval, 0.44-0.81) for FR to discriminate neuropathy from control. The correlation coefficient between MRI and ultrasound was significant (r = 0.49; 95% confidence interval for r, 0.21-0.70; P = 0.012). With the semiautomated evaluation, the mean intraobserver agreement was good (K = 0.86). The interobserver agreements were also good (reader 1 vs reader 2, k = 0.71; reader 2 vs reader 3, k = 0.78; reader 3 vs reader 1, k = 0.71). There were no statistically significant differences between the results obtained using the 2 methods. FR calculation on MRI is feasible, and it may be used in adjunct to standard MRI evaluation in peripheral nerve disorders.

Breast density assessment using a 3T MRI system: comparison among different sequences.

PURPOSE: To compare MRI sequences for breast density measurements on a 3T MRI system using IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation) as possible physiology-like reference. MATERIALS AND METHODS: MRI examination was performed in 48 consecutive patients (mean age 41, years; range, 35-67 years) on a 3.0T scanner and 46 were included. All (fertile) women, were examined between days 5 and 15 of their menstrual cycle. MRI protocol included: T1-turbo spin-echo (T1-tSE), T2-turbo spin-echo (T2-tSE), VIBRANT (Volume Imaging for Breast Assessment) before and after injection of contrast media and IDEAL. Breast density was calculated with semi-automated software. Statistical analysis was performed with non-parametric tests. RESULTS: Mean percentage of breast density calculated in each sequence was: T1-tSE = 56%; T2-tSE  = 52%; IDEAL FatOnly  = 55%; IDEAL WaterOnly  = 53%, VIBRANT = 55%. Significant differences were observed between T2-tSE and both T1-tSE (p<0.001), VIBRANT sequences (p = 0.009), T1-tSE and both IDEAL WaterOnly (p = 0.007) and IDEAL FatOnly (p = 0.047). Breast density percentage showed a positive linear correlation among different sequences: r ≥ 0.93. CONCLUSIONS: Differences exist between MRI sequences used to assess breast density percentage. T1-weighted sequences values were similar to IDEAL sequences.

Comparative estimation of percentage breast tissue density for digital mammography, digital breast tomosynthesis, and magnetic resonance imaging.

Given the increasingly important role of breast density as an independent risk factor for breast cancer, and the variable breast imaging tests that potentially provide measures for density. We compared breast tissue density on digital mammography (FFDM), digital breast tomosynthesis (DBT), and magnetic resonance imaging (MRI) using semi-automated automated software. These three imaging modalities have not been previously directly compared for estimating breast tissue density. Following informed consent from all participating women, FFDM, DBT, and MRI were performed. Breast percentage density was calculated with semi-automated software, and compared, for all three imaging modalities. 48 patients (mean age, 41 years; range, 35-67 years) underwent FFDM, DBT, and MRI. Percent FFDM, DBT, and MRI breast density measures showed a positive linear correlation, (r = 0.95 for MRI and DBT, P < 0.0001; r = 0.97, P < 0.0001 for FFDM and DBT; r = 0.87 for FFDM and MRI). Linear regression analysis related to MRI and DBT had a high r (2) = 0.89 (95 % CI = 0.88-0.99, P < 0.001). FFDM overestimated breast density in 15.1 % in comparison to DBT and in 16.2 % in comparison to MRI, or conversely each of DBT and MRI underestimated density (relative to FFDM) by 15.1 or 16.2 %, respectively. Differences in percentage breast density between FFDM and DBT, and between FFDM and MRI, were highly significant (P < 0.0001). Differences in percentage breast density between DBT and MRI were not significant (P > 0.05). Breast density measures using FFDM, DBT, or MRI were generally well-correlated, although differences were noted between estimates when comparing FFDM and DBT, and for estimates comparing FFDM and MRI. No signficant differences in percentage density were observed when comparing DBT and MRI. Our work highlight that differences between FFDM, DBT, and MRI should be considered when measuring percentage breast density.

Mammographic density estimation: one-to-one comparison of digital mammography and digital breast tomosynthesis using fully automated software.

OBJECTIVE: To compare breast density on digital mammography and digital breast tomosynthesis using fully automated software. METHODS: Following institutional approval and written informed consent from all participating women, both digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) were obtained. Breast percentage density was calculated with software on DBT and FFDM. RESULTS: Fifty consecutive patients (mean age, 51 years; range, 35-83 years) underwent both FFDM and DBT. Using a method based on the integral curve, breast density showed higher results on FFDM (68.1 ± 12.1 for FFDM and 51.9 ± 6.5 for DBT). FFDM overestimated breast density in 16.2% (P < 0.0001). Using a method based on maximum entropy thresholding, breast density showed higher results on FFDM (68.1 ± 12.1 for FFDM and 51.9 ± 6.5 for DBT). FFDM overestimated breast density in 11.4% (P < 0.0001). There was a good correlation among BI-RADS categories on a four-grade scale and the density evaluated with DBT and FFDM (r = 0.54, P < 0.01 and r = 0.44, P < 0.01). CONCLUSION: Breast density appeared to be significantly underestimated on digital breast tomosynthesis. KEY POINTS: Breast density is considered to be an independent risk factor for cancer Density can be assessed on full-field digital mammography and digital breast tomosynthesis Objective automated estimation of breast density eliminates subjectivity Automated estimation is more accurate than BI-RADS quantitative evaluation Breast density may be significantly underestimated on digital breast tomosynthesis.

Increased mammographic breast density in acromegaly: quantitative and qualitative assessment.

CONTEXT: Mammographic density is a strong independent risk factor for breast cancer, whose prevalence in acromegaly is still controversial. OBJECTIVE: To compare breast density in premenopausal acromegalic patients and controls and to determine whether density correlated with disease duration, GH, and IGF1 levels. DESIGN, SETTING AND PARTICIPANTS: A prospective study involving 30 patients and 60 controls matched for age and body mass index. INTERVENTIONS: A quantitative computer-aided mammographic density estimation (MDEST) and a qualitative blind evaluation by two experienced radiologists using the breast imaging reporting and data system (BI-RADS) was performed. Totally, 60 (acromegaly) and 120 (controls) craniocaudal and mediolateral oblique mammograms were evaluated in both patients and controls. MAIN OUTCOME MEASURES: Breast density. RESULTS: Patients showed a significantly (P<0.01) increased mammographic breast density with both methods (MDEST: 0.33 ± 0.21% and BI-RADS category: 2.81 ± 0.78) in comparison with controls (MDEST: 0.26 ± 0.19% and BI-RADS category: 2.35 ± 0.61). The agreement between the two methods and inter-observer agreement between the two radiologists were excellent (k=0.63 and k=0.85). In patients grouped according to disease activity (17 controlled and 13 uncontrolled) and medical therapy (15 treated and 15 untreated), no differences were found. All these groups had significantly increased mammographic breast density compared with controls (P<0.01). A positive correlation was found between mammographic breast density, IGF1 values and disease duration (r=0.29 and r=0.39), whereas it was not found with GH (r=-0.02). CONCLUSIONS: Mammographic breast density in premenopausal acromegalic patients is significantly higher than controls and positively correlated with IGF1 and disease duration.

Nerve density: a new parameter to evaluate peripheral nerve pathology on ultrasound. Preliminary study.

The possibility to realize a quantitative evaluation of nerve density on ultrasound is clinically important to enhance the evaluation of peripheral nerve disorders. We developed software that quantifies the ratio between the hypoechoic and hyperechoic areas of peripheral nerves on ultrasound. Nerve density was defined as (hypoechoic pixels)/(total pixels) and the purpose of our study was to asses if nerve density can be used to differentiate pathologic conditions affecting peripheral nerves. Ultrasound images of peripheral nerves were obtained with a high-frequency probe (17-5 MHz, 288 elements). Sixty-five different patients and (n = 65) controls (age range, 35-81 years; mean 55 years) were prospectively evaluated. Thirty-five patients had carpal tunnel syndrome and 30 patients had neurofibromas. Three radiologists performed a semiautomated evaluation with intra and interobserver agreement. A complete automatic evaluation was performed with no need of intra and interobserver evaluation. With the semiautomated evaluation, mean intraobserver agreement was good (K = 0.85). Interobserver agreements was good as well (reader 1 vs reader 2: k = 0.72; reader 2 vs reader 3: k = 0.80; reader 3 vs reader 1: k = 0.72). Differences among value of nerve density in normal nerves, CTS and neurofibromas were statistically significant (p < 0.0001). There were no statistically significant differences between the results obtained using the automatic or the semiautomatic method. Nerve density is capable of discriminating between normal and pathologic nerves of patients affected by carpal tunnel syndrome or neurofibromas. Moreover, nerve density measure is useful to discriminate between patients with mild and severe CTS.

Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one.

Although breast density is considered a strong predictor of breast cancer risk, its quantitative assessment is difficult. The aim of this study is to demonstrate that breast density assessment with a fully automated software is feasible and correlates with the semi-automated evaluation and the quantitative BI-RADS standards. A data set of 160 mammograms was evaluated by three blinded radiologists. Intra-observer (reader 1: k=0.71; reader 2: k=0.76; reader 3: k=0.62) and inter-observer (reader 1 vs reader 2: k=0.72; reader 2 vs reader 3: k=0.80; reader 3 vs reader 1: k=0.72) variability for the semi-automated software were good on a four-grade scale (D1/D2/D3/D4) and correlated with BI-RADS evaluation made by other two blinded radiologists (r=0.65, p<0.01). Inter-observer (reader 1 vs reader 2: k=0.85; reader 2 vs reader 3: k=0.91; reader 3 vs reader 1: k=0.85) variability for the semi-automated software was very good on a two-grade scale (D1-D2/D3-D4). The use of the fully automated software eliminated intra- and inter-observer differences, correlated with BI-RADS categories (r=0.62, p<0.01) and can replace the semi-automated one (Bland-Altman statistics). Our study demonstrates that automated estimation of breast density is feasible and eliminates subjectivity. Furthermore both the semi-automated and the fully automated density estimation are more accurate than BI-RADS quantitative evaluation and could also be used in the daily clinical practice.