Assessment of bootstrap resampling performance for PET data

Markiewicz, Paweł; Reader, Andrew J.; Matthews, Julian C.

doi:10.1088/0031-9155/60/1/279

Cited by 17 publications

(16 citation statements)

References 26 publications

(44 reference statements)

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“…In addition, we got very similar results with bootstrap resampling [24, 25], which was considered as a better method to produce independent realizations. For example, the fitted function for the SNR 2 in the liver for the images reconstructed with fewer than 1 million true counts y = 2.85 x + 0.19, R 2 = 0.81, which is close to that function ( y = 2.9 x + 0.20, R 2 = 0.79) with the current simulation strategy.…”

Section: Discussionmentioning

confidence: 57%

A method to assess image quality for Low-dose PET: analysis of SNR, CNR, bias and image noise

et al. 2016

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BackgroundLowering injected dose will have an effect on PET image quality. In this article, we aim to investigate this effect in terms of signal-to-noise ratio (SNR) in the liver, contrast-to-noise ratio (CNR) in the lesion, bias and ensemble image noise.MethodsWe present here our method and preliminary results using tuberculosis (TB) cases. Sixteen patients who underwent 18F-FDG PET/MR scans covering the whole lung and portion of the liver were selected for the study. Reduced doses were simulated by randomly discarding events in the PET list mode data stream, and ten realizations at each simulated dose were generated and reconstructed. The volumes of interest (VOI) were delineated on the image reconstructed from the original full statistics data for each patient. Four thresholds (20, 40, 60 and 80 % of SUVmax) were used to quantify the effect of the threshold on CNR at the different count level. Image metrics were calculated for each VOI. This experiment allowed us to quantify the loss of SNR and CNR as a function of the counts in the scan, in turn related to dose injected. Reproducibility of mean and maximum standardized uptake value (SUVmean and SUVmax) measurement in the lesions was studied as standard deviation across 10 realizations.ResultsAt 5 × 106 counts in the scan, the average SNR in the liver in the observed samples is about 3, and the CNR is reduced to 60 % of the full statistics value. The CNR in the lesion and SNR in the liver decreased with reducing count data. The variation of CNR across the four thresholds does not significantly change until the count level of 5 × 106. After correcting the factor related to subject’s weight, the square of the SNR in the liver was found to have a very good linear relationship with detected counts. Some quantitative bias appears with count reduction. At the count level of 5 × 106, bias and noise in terms of SUVmean and SUVmax are up to 10 and 20 %, respectively. To keep both bias and noise less than 10 %, 5 × 106 counts and 20 × 106 counts were required for SUVmean and SUVmax, respectively.ConclusionsInitial results with the given data of 16 patients diagnosed as TB demonstrated that 5 × 106 counts in the scan could be sufficient to yield good images in terms of SNR, CNR, bias and noise. In the future, more work needs to be done to validate the proposed method with a larger population and lung cancer patient data.

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Section: Discussionmentioning

confidence: 57%

A method to assess image quality for Low-dose PET: analysis of SNR, CNR, bias and image noise

et al. 2016

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“…The mean true count rates for the SD and LD scans were 132.7 AE 57.6 and 16.4 AE 7.2 million per bed position, respectively, and the number of independent realizations per bed was 8 (6)(7)(8)(9)(10)(11)(12). The detection rates of random coincident FIG.…”

Section: Resultsmentioning

confidence: 94%

“…7,8 Different approaches have been proposed for creating emulated realizations, like parametric and non-parametric bootstrapping [9][10][11] and fully independent data realizations. 12 These methods have been validated and compared, in particular from the point of view of the noise properties, with phantom data, but not with data from patient scans. This is an important consideration since analyses of tracer distribution may be more problematic in complex, physiological systems.…”

Section: Introductionmentioning

confidence: 99%

Low dose positron emission tomography emulation from decimated high statistics: A clinical validation study

et al. 2019

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Purpose The fundamental nature of positron emission tomography (PET), as an event detection system, provides some flexibility for data handling, including retrospective data manipulation. The reorganization of acquisition data allows the emulation of new scans arising from identical radiotracer spatial distributions, but with different statistical compositions, and is especially useful for evaluating the stability and reproducibility of reconstruction algorithms or when investigating extremely low count conditions. This approach is ubiquitous in the research literature but has only been validated, from the point of view of the noise properties, with numerical simulations and phantom data. We present here the first experiment comparing PET images of the same human subjects generated with two separate injections of radiotracer, using actual low dose (LD) data to validate a randomly decimated emulation from a standard dose scan. A key point of the work is focused on the randoms fractions, which scale differently than the trues at varying activity levels. Methods Eleven patients with non‐small cell lung cancer were enrolled in the study. Each imaging session consisted of two independent FDG‐PET/CT scans: a LD scan followed by a standard dose (SD) scan. Images were first reconstructed, using filtered back‐projection (FBP) and OSEM incorporating time‐of‐flight information and point‐spread function modeling (PSFTOF), from the LD and SD datasets comprising all counts from each scanned bed position. The number of true counts was recorded for all LD scans, and independent, count‐matched emulations (ELD) were reconstructed from the SD data. Noise distribution within the liver and standardized uptake value reproducibility within a population of contoured, tracer‐avid lesion volumes were evaluated across scans and statistics. Results The randoms fraction estimates were 17.4 ± 1.6% (14.9‐19.4) in the LD data and 42 ± 2.3% (37.1‐45.5) in the SD data. Eleven lesions were identified and volumes of interest were generated with a 50% threshold isocontour for each lesion, in every image. The distributions of metabolic volumes, means and maxima defined by the contoured volumes‐of‐interest (VOIs) were similar between the LD and SD sets. A two‐tailed, matched t‐test was performed on the populations of region statistics for both LD and ELD reconstructions, and the t‐statistics were 1.1 (P = 0.267) and ‐0.22 (P = 0.828) for the background liver VOIs and ‐0.54 (P = 0.603) and 0.23 (P = 0.821) for the lesion VOIs, for FBP and PSFTOF respectively. In every test, the null hypothesis that the two populations had the same mean could not be rejected at the 5% significance level. Conclusions Our results demonstrate that clinical LD PET scans can indeed be accurately emulated by the statistical decimation of standard dose scans, and this was achieved through validation by images generated with unbiased random coincidence estimations.

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“…where CRC n is the CRC obtained from the n th realization, N is the total number of realizations, and CRC R is the reference CRC value. 50 independent realizations of the experimental phantom data were then generated using the bootstrap resampling method (Lartizien et al 2010, Markiewicz et al 2015 for two different count levels: one is approximately equivalent to a dynamic frame with a high number of counts (~213 million) from a carbon-11 [ 11 C]PK11195 study, while the other corresponds to a dynamic frame with a low number of counts (~20 million) for the HRRT.…”

Section: N Crc Crcmentioning

confidence: 99%

Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM)

et al. 2017

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HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies conducted in this work.

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Assessment of bootstrap resampling performance for PET data

Cited by 17 publications

References 26 publications

A method to assess image quality for Low-dose PET: analysis of SNR, CNR, bias and image noise

A method to assess image quality for Low-dose PET: analysis of SNR, CNR, bias and image noise

Low dose positron emission tomography emulation from decimated high statistics: A clinical validation study

Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM)

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