Tracking a therapeutic response: How reliable are serial measurements of LV perfusion and function?

Why are serial measurements of cardiac perfusion and function important? Simply put, because they allow us to make a precise assessment of the worsening of patient conditions due to disease progression, or the improvements brought about by treatment. Our ultimate goal is that of reducing the random “noise” present in the measurements, so that even small differences found between successive studies can be considered significant, and can guide informed decisions about the patients’ clinical pathways. Before examining and discussing the specifics of the issue, it is appropriate to make an important distinction between reproducibility and repeatability. We consider reproducibility of measurements of perfusion and function from gated SPECT and gated PET images to refer to applying a computer algorithm twice to the same image set, and therefore to be proportional to the degree to which the algorithm is automated. Measurements from automated, “push-button” algorithms that operate in a deterministic manner are, by definition, perfectly reproducible, regardless of when or by whom the button is pushed. On the other hand, algorithms that require some degree of operator interaction will have less than perfect intra-operator and inter-operator reproducibility. (1) Conversely, repeatability of measurements is related to applying a computer algorithm to two separately acquired image sets belonging to the same patient (images ideally acquired without changes in the acquisition protocol or patient condition), and measuring the difference between the quantitative results. Low repeatability can be due to either a) changes in the acquisition setup (the patient moved, the radiopharmaceutical uptake pattern changed, gating abnormalities occurred), b) changes in the reconstruction and reorientation parameters used to generate the tomographic images input to the algorithm, c) changes in the way the quantitative algorithm operated, or was applied to the data, and/or d) true physiologic variation in the patient’s state at imaging. In other words, repeatability is a measure of the combined “stability” of the quantitative algorithm, the acquisition protocol itself, and the patient conditions (fig. 1). Fig. 1 While serial measurements fall obviously under the repeatability category, examining the potential limitations of reproducibility offers us a way to determine, in a more controlled environment, the effect of operator interaction on quantitative measurements. Specifically, we can ascribe imperfect reproducibility to variabilities in processing (by which tomographic images are produced from the acquired “raw” projection images) as well as variabilities in quantification Δrepro=Δproc+Δquant The two contributions are obviously correlated, as Δproc will cause changes in the images on which quantification is performed, which may in turn cause changes in the quantitative software’s output. For example, a paper by Knollman et al. investigated the variation in quantitative LV function measurements resulting from a simple 15 degrees change in the reorientation angles during processing of 59 gated SPECT datasets. The patient population comprised a good range of LVEFs (20%–80%) end-systolic volumes (ESVs) and end-diastolic volumes (EDVs), which were quantified using three different software algorithms - and while correlation coefficients were generally high, the most reproducible algorithm still produced differences of 2.8%, 7.5ml and 9ml for LVEF, ESV and EDV, respectively, at the 95% confidence interval level (2). In layman’s terms, 5% of the time one should expect quantitative differences greater than those listed above as a consequence of just altering the reorientation angle by 15 degrees during reconstruction. Changing the type of reconstruction applied to a dataset can impact reproducibility in an even more severe way, as reported by DePuey et al for filtered backprojection vs. different implementations of iterative reconstruction (3). In the extreme, this may require the need for reconstruction-specific normal limits for parameters of cardiac function, and possibly for cardiac perfusion as well. With respect to repeatability, we can consider it as comprising the above-described Δrepro component plus an acquisition-related component Δacq, further composed of an undesirable component Δacq_tech (variabilities related to technical causes), and a desirable component Δacq_clin (variabilities related to actual clinical changes). Δrepeat=Δrepro+Δacq=Δrepro+Δacq_tech+Δacq_clin It is apparent that, while reducing Δrepro is always beneficial in terms of reliability of the measurements, special care is needed to reduce Δrepeat without compromising the sensitivity to detect meaningful clinical changes. In order to investigate the typical Δrepeat in this context, a study was conducted on a population of 100 patients, who were imaged back-to-back with gated SPECT, twice at rest and twice after exercise or pharmacologic stress. Since a) the patients’ status was not expected to have changed between the consecutive rest or the consecutive stress acquisitions, b) each consecutive study pair shared the same radioisotope injection and c) the same cameras and protocols were used for every study pair, this is close to a best-case scenario for repeatability, and the differences measured ought to be directly related to the effect of manual processing and successive, operator-adjusted quantification (4). Not surprisingly, it was found that Δrepeat was generally higher (worse) than the Δrepro reported by Knollman, reflecting the greater variability associated with separate acquisitions. Perhaps more interestingly, repeatability was better (Δrepeat was lower) for stress studies than for rest studies, as a consequence of the formers’ higher statistical quality. Of note, when a 65 patients subsample was considered in which no operator adjustment to the automated quantification algorithm was necessary, repeatability significantly improved for the rest, but not for the stress studies – suggesting a clear benefit of fully automated analysis in the context of lower count images (Table 1). TABLE 1 Repeatability (Δrepeat, 95% confidence interval) of cardiac function measurements from back-to-back gated SPECT acquisitions. Standard processing and quantification were used (4). Repeatability of perfusion SPECT measurements were analyzed in the same 100-patient population using the “total perfusion deficit” (TPD) parameter previously defined by Slomka et al (5), producing 95% confidence intervals of 3.3%, 1.8% and 3.2% for stress TPD, rest TPD and ischemic TPD, respectively (4). Another study by Mahmarian et al investigated the change in “perfusion defect size” (PDS), a parameter similar to the TPD, in a population of 260 patients who underwent serial adenosine stress SPECT within 4 weeks of each other, without changes in clinical status or medications. This test of repeatability is obviously more challenging compared to the single-injection, back to back scenario in Xu’s study, and consequently the mean serial difference in PDS was reported to be (−0.13+/−4.2%), corresponding approximately to an 8.4% variation at the 95% confidence interval level - a result comparable to what was reported by Berman et al in serial studies repeated within 9–22 months (6), and significantly better than that previously reported for visual agreement (7). Summarizing what we know about optimizing repeatability, it is essential that serial studies be required to differ as little as possible in the way of acquisition (same camera, radioisotope, acquisition protocol, comparable count statistics, etc), processing (same reconstruction type, filter type and cutoff, reorientation software, etc) and quantification (same algorithm), while also minimizing the extent of operator intervention throughout the process. Even with these precautions, published data indicates that the best achievable repeatability using conventional approaches can be expected to be about 2–3% for TPD, 5–6% for LVEF, and 10 ml for EDVs and ESVs, all at the 95% confidence interval level.