As the basis of dynamic, time-resolved CT imaging and its acquisition protocols are discussed at length in previous chapters, this section will lay its focus on the clinical implications of the semi-quantitative data generated.

Due to technical advances, including higher temporal resolution, broader detectors, and acquisition techniques that allowed for whole heart coverage, dynamic perfusion imaging has become feasible and the focus of ongoing investigations (Fig. 2). Preliminary animal studies in pigs were able to quantify MBF during rest and adenosine induced hyperemia using the “shuttle mode” at 2 alternating table positions. MBF was shown to be significantly reduced in pigs with an induced 80 % coronary stenosis underlining the potential of CT to assess functional parameters as well (Mahnken et al. 2010). In an initial clinical experience, Bamberg et al. (2010) demonstrated the feasibility of a model-based semi-quantitative determination of MBF using dynamic myocardial stress CTP imaging in a female patient with myocardial ischemia. These results were further strengthened by Bastarrika’s working group, who were able to recruit 10 patients and showed comparable results (Bastarrika et al. 2010). Larger, single-center cohorts followed and confirmed the prospect of using dynamic CTP to expand existing CT capabilities (Fig. 3). Dynamic CTP has been evaluated against FFR, and an MBF cut of point of 75 mL × 100 mL⁻¹ min⁻¹ has been supported as the best discriminatory value to detect a significant coronary lesion (Bamberg et al. 2011). A recent study by Wang et al. showed dynamic CTP in good correlation to SPECT and catheter angiography; this study also documented an improvement in diagnostic accuracy for identifying flow-obstructive coronary stenosis when compared to CTA alone (Wang et al. 2012). However, in this study, an MBF cut-off value of 90 mL × 100 mL⁻¹ min⁻¹ was introduced, underlining that caution is in order when solely relying on quantitative values. Larger clinical trials are warranted to observe the influence of factors such as gender, race, BMI, and severity of coronary disease. If scientifically supported properly, quantitative values hold a certain attractiveness. Given the ever-growing emphasis on evidence-based medicine, the clinical arena would surely appreciate quantitative values for guiding decision-making and therapy.

A current draw-back of quantitative assessments is the time consuming post processing of myocardial perfusion data sets, which relies on two-dimensional region-of-interest measurements. This laborious evaluation regimen is hardly compatible with clinical work-flow. However, recently introduced semi-automated evaluation software shows a significant time saving potential when compared to manual assessments and could integrate quantitative measurements into clinical practice (Fig. 4).