The Enhancing Quality Using the Inspection Program (EQUIP) augments the FDA/MQSA program to ensure image quality review and implementation of corrective processes. In our screening mammography program, we compared technical recalls between digital breast tomosynthesis (DBT) and 2D full-field digital mammography (FFDM). This HIPAA-compliant study was exempt from IRB review. In consecutive screening mammograms (October 2013 through December 2017), prospectively recorded technical recalls were compared for imaging modality (FFDM, DBT+FFDM, DBT+synthesized mammography (SynM)), images requested, and indication(s) for technical recall (motion, positioning, technical/artifact). Chi-squared tests evaluated statistical significance between proportions. Of 48,324 screening mammograms, 277 (0.57%) patients were recalled for 360 indications with 371 repeated views. There were significantly less recalls among DBT exams compared to FFDM (X<sup>2</sup> = 25.239; p=0<0.001). Overall 98 (27.2%) technical recalls were for motion, 192 (53.3%) positioning, and 70 (19.4%) technique/artifacts. Of these, 91 (31.1%) FFDM indications were for motion, 138 (47.1%) positioning, and 64 (21.8%) technique/artifacts. For DBT+FFDM there were 7 (15.6%) for motion, 35 (77.8%) positioning, and 3 (6.7%) technique/artifacts, compared to DBT+SynM with 0 (0%) indications for motion, 19 (86.4%) positioning, and 3 (13.6%) technique/artifacts. There were significant differences in the indications for technical recall prior to and after implementing DBT+SynM (X<sup>2</sup> = 18.719; p<0.001). Technical recalls declined significantly with the inclusion of DBT (SynM/FFDM) as compared to FFDM alone; with recalls for motion demonstrating the greatest decrease. Positioning remains a dominant factor for technical recall regardless of modality, supporting the opportunity for continued technologist education in positioning to decrease technical recalls.
The ability to correlate anatomical knowledge and medical imaging is crucial to radiology and as such, should be a critical component of medical education. However, we are hindered in our ability to teach this skill because we know very little about what expert practice looks like, and even less about novices’ understanding. Using a unique simulation tool, this research conducted cognitive clinical interviews with experts and novices to explore differences in how they engage in this correlation and the underlying cognitive processes involved in doing so. This research supported what has been known in the literature, that experts are significantly faster at making decisions on medical imaging than novices. It also offers insight into the spatial ability and reasoning that is involved in the correlation of anatomy to medical imaging. There are differences in the cognitive processing of experts and novices with respect to meaningful patterns, organized content knowledge, and the flexibility of retrieval. Presented are some novice–expert similarities and differences in image processing. This study investigated extremes, opening an opportunity to investigate the sequential knowledge acquisition from student to resident to expert, and where educators can help intervene in this learning process.
Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.