Task-based assessment of binned and list-mode SPECT systems

Rahman, Ashequr; Jha, Abhinav K.

doi:10.1117/12.2582266

Cited by 4 publications

(2 citation statements)

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“…Another future direction is extending the method to estimate activity uptake directly from list-mode SPECT projections. Recent studies are demonstrating that list-mode data may contain more information than binned projection data [48], [49], and may provide improved quantification [19], [50]- [52]. These studies motivate advancing the proposed method to directly operate on list-mode data.…”

Section: Discussionmentioning

confidence: 97%

A projection-domain low-count quantitative SPECT method for alpha-particle emitting radiopharmaceutical therapy

Li¹,

Benabdallah²,

Abou³

et al. 2021

Preprint

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Quantitative single-photon emission computed tomography (SPECT) provides a mechanism to measure radiation-absorbed doses in lesions and normal organs after administration of alpha-particle emitting radiopharmaceutical therapies (α-RPTs). However, reliable (accurate and precise) quantification of dose requires reliable absolute quantification of regional activity uptake. This is especially challenging for α-RPT due to the complex emission spectra, the very low number of detected counts (up to 20 times lower than those detected in conventional SPECT), the impact of stray-radiation-related noise at these low counts, and other image-degrading processes in SPECT. The conventional reconstruction-based quantification methods are observed to be erroneous for α-RPT SPECT. To address these challenges, we developed a low-count quantitative SPECT (LC-QSPECT) method that incorporates multiple strategies to perform reliable quantification. First, the method directly estimates the regional activity uptake from the projection data, obviating the reconstruction step. Next, the method compensates for radioisotope and SPECT physics, including the isotope spectra, scatter, attenuation, and collimator-detector response, using a Monte Carlo-based approach. The method was validated in the context of three-dimensional SPECT with 223 Ra, a commonly used radionuclide for α-RPT. Validation was performed using both realistic simulation studies, including a virtual clinical trial, as well as synthetic and anthropomorphic physical-phantom studies. Across all studies, the LC-QSPECT method yielded reliable estimates of regional uptake and outperformed conventional ordered subset expectation maximization (OSEM)based reconstruction and geometric transfer matrix (GTM)based partial-volume compensation methods. Besides, the method yielded reliable uptake across different lesion sizes and lesion-to-bone uptake ratios. Further, the method yielded reliable estimates of mean uptake in lesions with varying intra-lesion heterogeneity in uptake. Additionally, the variance of the LC-QSPECT-method-estimated uptake

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Section: Discussionmentioning

confidence: 97%

A projection-domain low-count quantitative SPECT method for alpha-particle emitting radiopharmaceutical therapy

Li¹,

Benabdallah²,

Abou³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies have shown that processing data in list-mode format in SPECT can yield improved performance on clinical tasks compared to processing data in binned format. [49][50][51][52][53] More specifically, it has been quantitatively shown that list-mode data contains more information compared to binned data for the task of estimating attenuation coefficients. 27 Thus, advancing the method to process data in list-mode format directly may lead to even more improved performance.…”

Section: Discussionmentioning

confidence: 99%

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT

Yu,

Rahman,

Abbey

et al. 2023

Preprint

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Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images. Further, many SPECT systems do not include a CT component. To address these issues, we developed a Scatter-window projection and deep Learning-based AC (SLAC) method to perform AC without a separate transmission scan. To investigate the clinical efficacy of this method, we then objectively evaluated the performance of this method on the clinical task of detecting perfusion defects on MPI in a retrospective study with anonymized clinical SPECT/CT stress MPI images. The proposed method was compared with CT-based AC (CTAC) and no-AC (NAC) methods. Our results showed that the SLAC method yielded an almost overlapping receiver operating characteristic (ROC) plot and a similar area under the ROC (AUC) to the CTAC method on this task. These results demonstrate the capability of the SLAC method for transmission-less AC in SPECT and motivate further clinical evaluation.

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A physics and learning-based transmission-less attenuation compensation method for SPECT

Rahman

Schindler

et al. 2021

Medical Imaging 2021: Physics of Medical Imaging

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Attenuation compensation (AC) is a pre-requisite for reliable quantification and beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT). Typical AC methods require the availability of an attenuation map, which is obtained using a transmission scan, such as a CT scan. This has several disadvantages such as increased radiation dose, higher costs, and possible misalignment between SPECT and CT scans. Also, often a CT scan is unavailable. In this context, we and others are showing that scattered photons in SPECT contain information to estimate the attenuation distribution. To exploit this observation, we propose a physics and learning-based method that uses the SPECT emission data in the photopeak and scatter windows to perform transmission-less AC in SPECT. The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physicsbased approach. A convolutional neural network is then trained to segment this initial estimate into different regions. Predefined attenuation coefficients are assigned to these regions, yielding the reconstructed attenuation map, which is then used to reconstruct the activity distribution using an ordered subsets expectation maximization (OSEM)-based reconstruction approach. We objectively evaluated the performance of this method using highly realistic simulation studies conducted on the clinically relevant task of detecting perfusion defects in myocardial perfusion SPECT. Our results showed no statistically significant differences between the performance achieved using the proposed method and that with the true attenuation maps. Visually, the images reconstructed using the proposed method looked similar to those with the true attenuation map. Overall, these results provide evidence of the capability of the proposed method to perform transmissionless AC and motivate further evaluation.

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Task-based assessment of binned and list-mode SPECT systems

Cited by 4 publications

References 0 publications

A projection-domain low-count quantitative SPECT method for alpha-particle emitting radiopharmaceutical therapy

A projection-domain low-count quantitative SPECT method for alpha-particle emitting radiopharmaceutical therapy

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT

A physics and learning-based transmission-less attenuation compensation method for SPECT

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