Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation.

AIM: To compare magnetic resonance imaging (MRI)-derived breast density measurements using automatic segmentation algorithms with radiologist estimations using the Breast Imaging Reporting and Data Systems (BI-RADS) density classification. MATERIALS AND METHODS: Forty women undergoing mammography and dynamic breast MRI as part of their clinical management were recruited. Fat-water separated MRI images derived from a two-point Dixon technique, phase-sensitive reconstruction, and atlas-based segmentation were obtained before and after intravenous contrast medium administration. Breast density was assessed using software from Advanced MR Analytics (AMRA), Linköping, Sweden, with results compared to the widely used four-quartile quantitative BI-RADS scale. RESULTS: The proportion of glandular tissue in the breast on MRI was derived from the AMRA sequence. The mean unenhanced breast density was 0.31±0.22 (mean±SD; left) and 0.29±0.21 (right). Mean breast density on post-contrast images was 0.32±0.19 (left) and 0.32±0.2 (right). There was "almost perfect" correlation between pre- and post-contrast breast density quantification: Spearman's correlation rho=0.98 (95% confidence intervals [CI]: 0.97-0.99; left) and rho=0.99 (95% CI: 0.98-0.99; right). The 95% limits of agreement were -0.11-0.08 (left) and -0.08-0.03 (right). Interobserver reliability for BI-RADS was "substantial": weighted Kappa k=0.8 (95% CI: 0.74-0.87). The Spearman correlation coefficient between BI-RADS and MRI breast density was rho=0.73 (95% CI: 0.60-0.82; left) and rho=0.75 (95% CI: 0.63-0.83; right) which was also "substantial". CONCLUSION: The AMRA sequence provides a fully automated, reproducible, objective assessment of fibroglandular breast tissue proportion that correlates well with mammographic assessment of breast density with the added advantage of avoidance of ionising radiation.

Subchondral bone in osteoarthritis: association between MRI texture analysis and histomorphometry.

OBJECTIVE: Magnetic resonance imaging (MRI) texture analysis is a method of analyzing subchondral bone alterations in osteoarthritis (OA). The objective of this study was to evaluate the association between MR texture analysis and ground-truth subchondral bone histomorphometry at the tibial plateau. DESIGN: The local research ethics committee approved the study. All subjects provided written, informed consent. This was a cross-sectional study carried out at our institution between February and August 2014. Ten participants aged 57-84 with knee OA scheduled for total knee arthroplasty (TKA) underwent pre-operative MRI of the symptomatic knee at 3T using a high spatial-resolution coronal T1 weighted sequence. Tibial plateau explants obtained at the time of TKA underwent histological preparation to allow calculation of bone volume fraction (BV.TV), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp) and trabecular number (Tb.N). Texture analysis was performed on the tibial subchondral bone of MRI images matched to the histological sections. Regression models were created to assess the association of texture analysis features with BV.TV, Tb.Th, Tb.Sp and Tb.N. RESULTS: MRI texture features were significantly associated with BV.TV (R2 = 0.76), Tb.Th (R2 = 0.47), Tb.Sp (R2 = 0.75) and Tb.N (R2 = 0.60, all P < 0.001). Simple gray-value histogram based texture features demonstrated the highest standardized regression coefficients for each model. CONCLUSION: MRI texture analysis features were significantly associated with ground-truth subchondral bone histomorphometry at the tibial plateau.

Building an anonymized catalogued radiology museum in PACS: a feasibility study.

The aim of this study was to test the feasibility of a software application that would allow the anonymization and cataloguing of whole DICOM datasets in order to build searchable radiology museums within PACS. The application was developed on a dedicated networked PC, using C# and HL7 coding. Whole DICOM datasets were pushed from PACS to a networked PC on which the application, Museum Builder, was developed. Museum Builder works by replacing the patient specific data (the forename, surname and hospital number) within each header of each DICOM file with terms from anatomical and surgical sieve menus. The date of birth is anonymized to 1 January of the same year. Whole DICOM datasets comprising hundreds of images can be anonymized and catalogued in a single episode. Museum Builder primes PACS with an HL7 script to receive a "new" patient. DICOM datasets are then pushed back to PACS where they are added to the database as "new" cases. The museum cases can then be searched for, on PACS, by any combination of terms that correspond to appropriate anatomical units, surgical sieve headings or radiological specialty. New radiology reports containing clinical histories, radiological descriptions, differential diagnoses and discussion can be added through the report window. Our institution has developed and used this tool to generate a PACS based radiology museum containing not only full DICOM datasets, but also relevant histological and clinical photographs. In conclusion, this technique offers a mechanism for generating anonymized catalogued radiology museums in PACS. Museum Builder represents a working prototype that demonstrates some of the archiving functions that are expected by teaching institutions from PACS.