This study, in which lesions were randomly simulated into the liver tissue using clinical raw data from SPECT acquisitions, demonstrated that the detectability of the simulated lesions is highly dependent on the local activity estimate and the noise level. The lesions that were detectable for the observer had statistically significant higher TNC ratios and SNR. Using MC-based OSEM reconstruction, the spatial resolution was improved [18] and consequently the TNC ratios were statistically significantly improved compared to conventional reconstruction (fAC OSEM). Although, the noise level remained high, which influenced the SNR values.

For all reconstruction methods, the variation in liver activity concentration and high noise level resulted in disappearance of lesions when the randomly selected location was in an area of low activity concentration, about the size of the control VOI (Fig. 3). These lesions received a lower TNC ratio due to the low activity concentration of the control VOI, and there was a higher activity concentration outside of the control VOI. The volume of the lesions after reconstruction was, due to poor spatial resolution, approximately the same size as the control VOI and the partial volume effect consequently spread the activity in the lesion over the area with low activity concentration. These lesions had low TNC ratios and the visual retrospective assessment, with knowledge of the correct coordinates, resulted in the decision to consider these patients negative for lesions.

Since several lesions were considered not detectable after the retrospective analysis, we also performed an analysis of only the lesions possible to detect. Our viewpoint was that this scenario more fairly demonstrates the differences between the three reconstruction methods. The hidden lesions were naturally not detected by the observer in any of the reconstruction methods in scenario AL. Therefore, the difference between the reconstruction methods in scenarios AL and DL regarding the detection of lesions was only a reflection of the lower number of patients that are lesion positive (26 for each set in AL vs. 19 in DL). There were a larger number of misplaced lesions in scenario AL than in scenario DL (Table 6). Therefore, the ROC analysis differed more from the results of the detection of lesions and the accuracy of the assessments in scenario AL than in scenario DL. Furthermore, the misplaced lesions that had an erroneous positive impact in the ROC analysis did not influence the detection of lesions measure but did affect the accuracy in scenario AL. When 7 patients were changed to negative for lesion, some of these misplacements shifted (rightfully) to false positives, which explained the differences in accuracy between scenarios AL and DL. Another feature of ROC analysis is that it considers the confidence level of the observer. Figure 5 shows that the confidence level was higher in filtered images compared to unfiltered images and highest in fAC OSEM. This was possibly due to the observer’s previous experience with image appearance during evaluations.

According to a previous study [18], the spatial resolution is improved in images reconstructed with MC OSEM, which would consequently imply a higher degree of detail. The perception of homogeneity of the uptake of 111In-octreotide in the liver, which is usually what the observer expects in a SPECT/CT examination, can be challenged as the spatial resolution and image quality overall improve. The blurring effect of commonly used filters also contributes to the homogenous appearance. This resembles situations in which new gamma camera designs present higher sensitivity and/or resolution. Pathologic uptake will be more intensive, but this also applies for benign findings; for all reconstruction methods the TNC ratios of the false positive findings were similar to the TNC ratios of the detected lesions. This might explain the higher degree of false positive findings by the observer in fMC OSEM and MC OSEM in this study. Further, the images reconstructed with MC OSEM (specifically unfiltered) were very different visually from those most familiar to observers, which is a probable explanation for the lower confidence level reported by the observer. In this study, the observer was more confident with smoother images (Fig. 5). However, the confidence level of the observer regarding the detected lesions was highest with fMC OSEM (Fig. 6), a probable cause of higher TNC ratios combined with low noise levels. Still, the detection of lesions and the observer accuracy were improved with unfiltered MC OSEM compared to the filtered images (Table 3).

As stated before, the ROC analysis did not consider the coordinates of the lesion, so a misplaced lesion in a patient who has a lesion was considered an accurate assessment. It can be argued that this is a correct approach when evaluating a diagnostic test for a disease, since the underlying reason for a positive test is irrelevant and the goal is to find patients who require further evaluation. In this study, however, we aimed to distinguish differences in the visual appearance of small lesions depending on the different image processing techniques. From our perspective, a misplaced lesion is due to two successive errors: the first misses the real lesion and the second finds a lesion that is not there. To consider this to be an accurate assessment is therefore unsatisfactory. Consequently, the terms detection of lesions and accuracy were chosen instead of sensitivity and specificity. As there were misplaced lesions for all reconstruction methods, and in the absence of other comparison measures, the ROC analysis was still considered valuable. This was especially true for scenario DL, where there were not as many misplaced lesions as for scenario AL. There is an alternative to ROC, called Free response operating characteristic (FROC), that does consider the position of the lesion. [29, 30] However, our study was not designed as a FROC study which made it difficult to analyze the data. The assessment in FROC (the detection as well as the confidence level) is made on lesion level instead of case level. Also, using Jackknife FROC analysis (JAFROC and JAFROC1) we could still not handle the problem with misplaced lesions (where the observer found a lesion but with the wrong coordinates) as JAFROC only considers false positives in normal cases and JAFROC1 does but recommends to not include normal cases at all.

The CT scanners used for SPECT/CT imaging at Sahlgrenska University Hospital during the years between 2004 and 2011 were, from an image quality standpoint, far inferior to the CT scanners used today. The image quality of the CT images in this study was poor, and some examinations also suffer from severe metal artifacts; these will influence the performance of the MC simulations in the reconstruction algorithm. In conventional OSEM reconstruction, the CT images are used only for attenuation correction. Hence in this study, the image quality of the CT was of less importance for fAC OSEM than for fMC OSEM and MC OSEM. Furthermore, the radial positions of the detectors at each projection angle were not registered by the gamma cameras. Therefore, the distances, in order to correct for the CDR function, had to be manually estimated (based on the CT images), which might have influenced the accuracy of the MC simulations. Hence, higher quality CT images and registered radial distances by the gamma camera, both standard in SPECT/CT scanners today, might result in more accurate MC simulations, and consequently the image quality might be further improved.

The post filtered AC OSEM reconstruction used in this study includes all the parameters that were used when the examination was performed at Sahlgrenska University Hospital, it has not been optimized. Today, there are commercially available reconstruction methods that apply CDR correction. To include this correction in AC OSEM would have been an interesting comparison. At Sahlgrenska University Hospital, we have access to GE’s reconstruction application with CDR correction, called Evolution, but it did not accept our manipulated raw data, nor was there any information registered by the gamma cameras of the radial distances, which is required to perform the correction. However, we previously compared the spatial resolution in 111In octreotide imaging and showed a statistically significant improvement in images reconstructed with MC OSEM versus Evolution (8.2 mm vs. 9.3 mm, p < 0.001; 95% CI 0.6–1.5) [18]. The use of CDR correction in conventional reconstruction, fAC OSEM, might probably have improved these images but as the spatial resolution improvement in MC OSEM compared to Evolution was statistically significant, MC OSEM would still be expected to be superior. However, this had to be evaluated in a comparative study. Furthermore, the number of observers should ideally be more than one.

It has previously been shown that MC OSEM significantly improves the image quality in 177Lu-octreotate imaging [25] and the spatial resolution in 111In-octreotide imaging [18]. As MC simulations can be performed for all energies, the image quality in MC-based reconstruction is improved also for imaging with other radionuclides like the work horse 99mTc [23] and also 90Y [31]. MC-based reconstructions are also favorable for radionuclides with higher photon energy components, that cause problems with scatter, which has been shown in phantom studies with 123I and 131I [26, 27]. Hence, MC-based reconstruction is very promising. However, the noise level needs to be handled appropriately, and we aim to further investigate deep learning–generated synthetic intermediate projections (SIPs) in SPECT images, which have been demonstrated to more effectively reduce the noise level compared to post-filtering methods such as Gaussian filtering [32, 33]. This might improve SNR in images reconstructed with MC-based OSEM reconstruction.