Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient's history: A review.

OBJECTIVE: Breast cancer is the most common malignancy in women. Mammography and ultrasound are commonly used in a clinical environment as the first choice for breast cancer detection. Magnetic Resonance Imaging (MRI) has been reported to reveal additional information. In the following review MRI, Ultrasound (US) and Mammography (MM) are all compared in terms of their diagnostic performance on breast cancer detection, depending on tumor type, breast density and patient's history. KEY FINDINGS: Evaluating each modality alone, MRI provided an overall sensitivity and specificity of 94.6% (range 85.7%-100%) and 74.2% (range 25%-100%) respectively, while mammography showed that the overall sensitivity was at 54.5% (range 27%-86.8%) and specificity was 85.5% (range 62.9%-98.8%). The overall sensitivity and specificity of ultrasound was 67.2% (range 26.9%-87.5%) and 76.8% (range 18.8%-96.9%). When combining the results of all three techniques, it resulted in a sensitivity of 97.7% (range 95%-100%) and a specificity of 63.3% (range 37.1%-87.5%). In addition, contrast-enhanced mammography (CE-MM) and MRI (CE-MRI) illustrated an overall sensitivity and specificity for CE-MM was 90.5% (range 80.9%-100%) and 52.6% (range 15%-76.1%) and for CE-MRI, the overall sensitivity and specificity was 91.5% (range 89.1%-93.8%) and 64.7% (range 43.7%-85.7%). CONCLUSION: As modalities alone, the highest sensitivity has been observed for MRI and the lowest sensitivity for mammography regardless breast type, density, and history. Sensitivity is even more increased from the combination of US + MRI or MM + MRI or MRI + MM + US. The specificity seems to be affected by the size, type of the tumor and patient's history, however based on breast density, the highest specificity was observed by US alone. IMPLICATIONS FOR PRACTICE: Breast cancer screening is of outmost importance and identifying the best technique will improve cancer management. Combining techniques increases diagnostic ability compared with using modalities alone. CE-MM can be a viable option in dense breast tissue when there are contraindications to MRI as it also has high sensitivity based on the type of breast cancer.

The effect of regularization in motion compensated PET image reconstruction: a realistic numerical 4D simulation study.

Following continuous improvement in PET spatial resolution, respiratory motion correction has become an important task. Two of the most common approaches that utilize all detected PET events to motion-correct PET data are the reconstruct-transform-average method (RTA) and motion-compensated image reconstruction (MCIR). In RTA, separate images are reconstructed for each respiratory frame, subsequently transformed to one reference frame and finally averaged to produce a motion-corrected image. In MCIR, the projection data from all frames are reconstructed by including motion information in the system matrix so that a motion-corrected image is reconstructed directly. Previous theoretical analyses have explained why MCIR is expected to outperform RTA. It has been suggested that MCIR creates less noise than RTA because the images for each separate respiratory frame will be severely affected by noise. However, recent investigations have shown that in the unregularized case RTA images can have fewer noise artefacts, while MCIR images are more quantitatively accurate but have the common salt-and-pepper noise. In this paper, we perform a realistic numerical 4D simulation study to compare the advantages gained by including regularization within reconstruction for RTA and MCIR, in particular using the median-root-prior incorporated in the ordered subsets maximum a posteriori one-step-late algorithm. In this investigation we have demonstrated that MCIR with proper regularization parameters reconstructs lesions with less bias and root mean square error and similar CNR and standard deviation to regularized RTA. This finding is reproducible for a variety of noise levels (25, 50, 100 million counts), lesion sizes (8 mm, 14 mm diameter) and iterations. Nevertheless, regularized RTA can also be a practical solution for motion compensation as a proper level of regularization reduces both bias and mean square error.

Analysis and comparison of two methods for motion correction in PET imaging.

PURPOSE: Although there have been various proposed methods for positron emission tomography (PET) motion correction, there is not sufficient evidence to answer which method is better in practice. This investigation aims to characterize the behavior of the two main motion-correction approaches in terms of convergence and image properties. METHODS: For the first method, reconstruct-transform-average (RTA), reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), motion information is incorporated within the reconstruction. Both techniques studied were based on the ordered subsets expectation maximization algorithm. Motion information was obtained from a dynamic MR acquisition performed on a human volunteer and concurrent PET data were simulated from the dynamic MR data. The two approaches were assessed statistically using multiple realizations to accurately define the noise properties of the reconstructed images. RESULTS: MCIR successfully recovers the true values of all regions, whereas RTA has high bias due to the limited count-statistics and interpolation errors during the transformation step. In addition, RTA noise is very small and stabilized, whereas in MCIR noise becomes progressively greater with the number of iterations and therefore MCIR outperforms RTA in terms of MSE only if noise is treated. For example, MCIR with postfiltering results in MSE up to 42% lower than RTA. CONCLUSIONS: This study indicates that MCIR may provide superior performance overall to RTA if noise is minimized. However, in applications where quantification is not the main objective RTA can be a practical and simple method to correct for motion.