A Practical, Automated Quality Assurance Method for Measuring Spatial Resolution in PET

Lodge, Martin A.; Rahmim, Arman; Wahl, Richard L.

doi:10.2967/jnumed.108.060079

Cited by 28 publications

(27 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more realistic solution is to consider a known phantom, and to extract the effective resolution from knowledge of the original object and from the measured blurred image (Lodge et al ., 2009). In the present work, to estimate the effective resolution of the system, we performed reconstructions of noise-free MP images without and with the defect, followed by calculation of the difference image (representing the blurred object), then estimating the FWHM width of a symmetric Gaussian needed to convolve with the known object to best match the blurred image.…”

Section: Experimental Design and Noise Propagation Calculationsmentioning

confidence: 99%

Noise propagation in resolution modeled PET imaging and its impact on detectability

Rahmim

Tang

2013

Phys. Med. Biol.

Self Cite

View full text Add to dashboard Cite

PET imaging is affected by a number of resolution degrading phenomena, including positron range, photon non-collinearity and inter-crystal blurring. An approach to this issue is to model some or all of these effects within the image reconstruction task, referred to as resolution modeling (RM). This approach is commonly observed to yield images of higher resolution and subsequently contrast, and can be thought of as improving the modulation transfer function (MTF). Nonetheless, RM can substantially alter the noise distribution. In this work, we utilize noise propagation models in order to accurately characterize the noise texture of reconstructed images in the presence of RM. Furthermore we consider the task of lesion or defect detection, which is highly determined by the noise distribution as quantified using the noise power spectrum (NPS). Ultimately, we use this framework to demonstrate why conventional trade-off analyses (e.g. contrast vs. noise, using simplistic noise metrics) do not provide a complete picture of the impact of RM and that improved performance of RM according to such analyses does not necessarily translate to the superiority of RM in detection task performance.”

show abstract

Section: Experimental Design and Noise Propagation Calculationsmentioning

confidence: 99%

Noise propagation in resolution modeled PET imaging and its impact on detectability

Rahmim

Tang

2013

Phys. Med. Biol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the FBP algorithm is linear, the measured spatial resolution is independent of any activity distribution in the background. Apart from a point source, other phantoms like a line source (DeGrado et al 1994), a cylinder phantom (Lodge et al 2009) can also be used to measure the spatial resolution of a PET system.…”

Section: Introductionmentioning

confidence: 99%

On the assessment of spatial resolution of PET systems with iterative image reconstruction

Gong

Cherry

2016

Phys. Med. Biol.

View full text Add to dashboard Cite

Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.

show abstract

“…To that end, a number of "task-independent" metrics have been defined to evaluate a great range of factors which may affect the quality of a medical image: noise [1,2], contrast resolution [1], and spatial resolution [3], to mention just a few. However, medical images are inherently task-specific rather than task-independent.…”

Section: Osa Published Bymentioning

confidence: 99%

Channelized Hotelling observers for the assessment of volumetric imaging data sets

Goossens²,

et al. 2011

View full text Add to dashboard Cite

OSA Published byCurrent clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For 3D images, however, these models have been little explored so far. In this work, first, two novel designs of a multi-slice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multi-slice models and a volumetric model. Four different random background statistics are considered, both Gaussian (non-correlated and correlated Gaussian noise) and non-Gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multi-slice models, the second proposed CHO could most closely approach the volumetric model whereas the first new CHO seems to be least affected by the number of training samples.

show abstract

A Practical, Automated Quality Assurance Method for Measuring Spatial Resolution in PET

Cited by 28 publications

References 15 publications

Noise propagation in resolution modeled PET imaging and its impact on detectability

Noise propagation in resolution modeled PET imaging and its impact on detectability

On the assessment of spatial resolution of PET systems with iterative image reconstruction

Channelized Hotelling observers for the assessment of volumetric imaging data sets

Contact Info

Product

Resources

About