Automatic quantification of multi-modal rigid registration accuracy using feature detectors.

In radiotherapy, the use of multi-modal images can improve tumor and target volume delineation. Images acquired at different times by different modalities need to be aligned into a single coordinate system by 3D/3D registration. State of the art methods for validation of registration are visual inspection by experts and fiducial-based evaluation. Visual inspection is a qualitative, subjective measure, while fiducial markers sometimes suffer from limited clinical acceptance. In this paper we present an automatic, non-invasive method for assessing the quality of intensity-based multi-modal rigid registration using feature detectors. After registration, interest points are identified on both image data sets using either speeded-up robust features or Harris feature detectors. The quality of the registration is defined by the mean Euclidean distance between matching interest point pairs. The method was evaluated on three multi-modal datasets: an ex vivo porcine skull (CT, CBCT, MR), seven in vivo brain cases (CT, MR) and 25 in vivo lung cases (CT, CBCT). Both a qualitative (visual inspection by radiation oncologist) and a quantitative (mean target registration error-mTRE-based on selected markers) method were employed. In the porcine skull dataset, the manual and Harris detectors give comparable results but both overestimated the gold standard mTRE based on fiducial markers. For instance, for CT-MR-T1 registration, the mTREman (based on manually annotated landmarks) was 2.2 mm whereas mTREHarris (based on landmarks found by the Harris detector) was 4.1 mm, and mTRESURF (based on landmarks found by the SURF detector) was 8 mm. In lung cases, the difference between mTREman and mTREHarris was less than 1 mm, while the difference between mTREman and mTRESURF was up to 3 mm. The Harris detector performed better than the SURF detector with a resulting estimated registration error close to the gold standard. Therefore the Harris detector was shown to be the more suitable method to automatically quantify the geometric accuracy of multimodal rigid registration.

Benign (BI-RADS 2) lesions in breast MRI.

AIM: To assess the frequency of malignancy in lesions characterized as benign [Breast Imaging-Reporting and Data System (BI-RADS) 2] on breast MRI. MATERIALS AND METHODS: In this institutional review board-approved retrospective single-centre study, 1265 consecutive patients (mean age 50 ± 13 years), undergoing dynamic contrast-enhanced MRI (1.5 T) of the breast during a 6 year time period, were eligible. This study investigated the MRI characteristics and frequency of malignancy in 192 of these patients with breast lesions classified as BI-RADS 2. Examinations were read during clinical practice and classified according to the MRI BI-RADS lexicon. Based on the patient's and referring physician's preferences, lesions were either histopathologically verified or were subjected to both clinical and imaging follow-up of at least 2 years (range 2-9 years). Descriptive statistical metrics were calculated. RESULTS: According to the standard of reference, 0 of 192 (0%) lesions classified as BI-RADS 2 were malignant. Histopathology was available in 67 (34.9%) lesions and revealed benign findings exclusively. The remaining 125 (65.1%) lesions did not exhibit changes during the follow-up period and were, therefore, considered negative for malignancy. CONCLUSIONS: The frequency of malignancy in breast lesions classified as BI-RADS 2 is zero. As a consequence, breast biopsies are unnecessary in these cases.