Sangtae Ahn

Zero TE MR bone imaging in the head

To investigate proton density (PD)‐weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures.

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET.

Ordered subset expectation maximization (OSEM) is the most widely used algorithm for clinical PET image reconstruction. OSEM is usually stopped early and post-filtered to control image noise and does not necessarily achieve optimal quantitation accuracy. As an alternative to OSEM, we have recently implemented a penalized likelihood (PL) image reconstruction algorithm for clinical PET using the relative difference penalty with the aim of improving quantitation accuracy without compromising visual image quality. Preliminary clinical studies have demonstrated visual image quality including lesion conspicuity in images reconstructed by the PL algorithm is better than or at least as good as that in OSEM images. In this paper we evaluate lesion quantitation accuracy of the PL algorithm with the relative difference penalty compared to OSEM by using various data sets including phantom data acquired with an anthropomorphic torso phantom, an extended oval phantom and the NEMA image quality phantom; clinical data; and hybrid clinical data generated by adding simulated lesion data to clinical data. We focus on mean standardized uptake values and compare them for PL and OSEM using both time-of-flight (TOF) and non-TOF data. The results demonstrate improvements of PL in lesion quantitation accuracy compared to OSEM with a particular improvement in cold background regions such as lungs.

Clinical evaluation of TOF versus non-TOF on PET artifacts in simultaneous PET/MR: a dual centre experience

PurposeOur objective was to determine clinically the value of time-of-flight (TOF) information in reducing PET artifacts and improving PET image quality and accuracy in simultaneous TOF PET/MR scanning.MethodsA total 65 patients who underwent a comparative scan in a simultaneous TOF PET/MR scanner were included. TOF and non-TOF PET images were reconstructed, clinically examined, compared and scored. PET imaging artifacts were categorized as large or small implant-related artifacts, as dental implant-related artifacts, and as implant-unrelated artifacts. Differences in image quality, especially those related to (implant) artifacts, were assessed using a scale ranging from 0 (no artifact) to 4 (severe artifact).ResultsA total of 87 image artifacts were found and evaluated. Four patients had large and eight patients small implant-related artifacts, 27 patients had dental implants/fillings, and 48 patients had implant-unrelated artifacts. The average score was 1.14 ± 0.82 for non-TOF PET images and 0.53 ± 0.66 for TOF images (p < 0.01) indicating that artifacts were less noticeable when TOF information was included.ConclusionOur study indicates that PET image artifacts are significantly mitigated with integration of TOF information in simultaneous PET/MR. The impact is predominantly seen in patients with significant artifacts due to metal implants.

Joint reconstruction of activity and attenuation using MR-based priors: Application to clinical TOF PET/MR

Attenuation correction is critical to accurate PET quantitation. However, it is challenging to extract accurate attenuation from MR data because of distinct physics of MR and PET. The goal of this study is to achieve robust and accurate attenuation correction in PET/MR. We combine an MR-segmentation based approach and a joint estimation approach synergistically by using an MR-segmentation based attenuation map as an MR-based prior for joint estimation. We evaluate the joint estimation algorithm with MR-based priors using time-of-flight (TOF) PET/MR clinical data. It is demonstrated that the joint estimation method using MR-based priors can recover the attenuation of metal implants, internal air cavities and bones in a robust way.

Improving lesion detectability in PET imaging with a penalized likelihood reconstruction algorithm

Ordered Subset Expectation Maximization (OSEM) is currently the most widely used image reconstruction algorithm for clinical PET. However, OSEM does not necessarily provide optimal image quality, and a number of alternative algorithms have been explored. We have recently shown that a penalized likelihood image reconstruction algorithm using the relative difference penalty, block sequential regularized expectation maximization (BSREM), achieves more accurate lesion quantitation than OSEM, and importantly, maintains acceptable visual image quality in clinical wholebody PET. The goal of this work was to evaluate lesion detectability with BSREM versus OSEM. We performed a twoalternative forced choice study using 81 patient datasets with lesions of varying contrast inserted into the liver and lung. At matched imaging noise, BSREM and OSEM showed equivalent detectability in the lungs, and BSREM outperformed OSEM in the liver. These results suggest that BSREM provides not only improved quantitation and clinically acceptable visual image quality as previously shown but also improved lesion detectability compared to OSEM. We then modeled this detectability study, applying both nonprewhitening (NPW) and channelized Hotelling (CHO) model observers to the reconstructed images. The CHO model observer showed good agreement with the human observers, suggesting that we can apply this model to future studies with varying simulation and reconstruction parameters.

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers

Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as 68Ga-DOTATOC and 18F-Fluoride.

Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning

To describe a method for converting Zero TE (ZTE) MR images into X‐ray attenuation information in the form of pseudo‐CT images and demonstrate its performance for (1) attenuation correction (AC) in PET/MR and (2) dose planning in MR‐guided radiation therapy planning (RTP).

Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method

Abstract. We have previously developed a convergent penalized likelihood (PL) image reconstruction algorithm using the relative difference prior (RDP) and showed that it achieves more accurate lesion quantitation compared to ordered subsets expectation maximization (OSEM). We evaluated the detectability of low-contrast liver and lung lesions using the PL-RDP algorithm compared to OSEM. We performed a two-alternative forced choice study using a channelized Hotelling observer model that was previously validated against human observers. Lesion detectability showed a stronger dependence on lesion size for PL-RDP than OSEM. Lesion detectability was improved using time-of-flight (TOF) reconstruction, with greater benefit for the liver compared to the lung and with increasing benefit for decreasing lesion size and contrast. PL detectability was statistically significantly higher than OSEM for 20 mm liver lesions when contrast was ≥0.5 (p<0.05), and TOF PL detectability was statistically significantly higher than TOF OSEM for 15 and 20 mm liver lesions with contrast ≥0.5 and ≥0.25, respectively. For all other cases, there was no statistically significant difference between PL and OSEM (p>0.05). For the range of studied lesion properties, lesion detectability using PL-RDP was equivalent or improved compared to using OSEM.

Performance improvement and validation of a new MAP reconstruction algorithm

We previously proposed a fast maximum a posteriori (MAP) algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constrains (LBFGS-B-PC), combining LBFGS-B with diagonal preconditioning. Previous results have shown in simulations that it converges using around 40 projections independent of many factors. The aim of this study is to improve the algorithm further by using a better initial image and a modified preconditioner that is less sensitive to noise and data scale. By initializing the algorithm with the best initial image (one full iteration of OSEM with 35 subsets), ROI values can converge almost twice as fast for the same computation time. Moreover, the new preconditioner makes the performance more consistent between high and low count data sets. In addition, we have found a means to choose the stopping criteria to reach a desired level of quantitative accuracy in the reconstructed image. Based on the results with patient data, the optimized LBFGS-B-PC shows promise for clinical imaging.

Joint estimation of activity and attenuation: Application to non-FDG TOF PET/MR clinical data

Attenuation correction is critical to accurate PET quantitation. Accurate and robust attenuation correction remains challenging in hybrid PET/MR because it is difficult to segment bones, internal air and lungs accurately in MR images and MR often suffers from artifacts due to metal implants and signal shading. Joint estimation (JE) of activity and attenuation from time-of-flight (TOF) PET data has recently gained substantial interest because the JE approach shows a promise to address the challenges in MR based attenuation correction. We have previously implemented a JE algorithm with MR-based priors and demonstrated its feasibility using FDG PET/MR clinical data. However, JE algorithms have rarely been evaluated on non-FDG tracers which often have spatially more specific uptake patterns with little activity in background tissue, and hence may pose challenges to JE. In this study, we evaluate the JE algorithm using non-FDG PET/MR clinical data. We demonstrate that the JE algorithm improves the accuracy and robustness of MR-based attenuation correction not only for FDG as previously shown but also for non-FDG tracers such as 68Ga-DOTATOC and Fluoride. In addition, we demonstrate the critical role of TOF information in JE algorithms.