The Impact of Total Variation Regularized Expectation Maximization Reconstruction on 68Ga-DOTA-TATE PET/CT Images in Patients With Neuroendocrine Tumor

Liu, Lin; Liu, Hanxiang; Xu, Shijie; Zhang, Shumao; Yi, Tao; Mok, Greta S. P.; Chen, Yue

doi:10.3389/fmed.2022.845806

Cited by 5 publications

(13 citation statements)

References 22 publications

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“…Two different approaches were identified for reducing the noise in 68 Ga PET images and enabling low-count 68 Ga PET measurements. The first approach reduces the noise during the image reconstruction process (reconstruction-based noise reduction approaches, n = 11) [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], whereas the second approach is based on neural networks (deep learning approaches for noise reduction, n = 5) [ 37 , 38 , 39 , 40 , 41 ], as seen in Figure 2 .…”

Section: Resultsmentioning

confidence: 99%

“…In addition to Q.Clear, United Imaging Healthcare introduces a BPL iterative reconstruction algorithm based on total variation regularized expectation maximization TVREM (HYPER Iterative) with a penalization factor between 0 and 1 to adjust the total variation penalization of voxels of corresponding neighborhoods. TVREM was used for PET phantom and patient scanning with 68 Ga-PSMA (20 patients) [ 35 ] and 68 Ga-DOTATATE (17 patients) [ 36 ]. As the penalization factor α increased, image noise [ 35 , 36 ], CR [ 35 ], SUV max [ 35 , 36 ], and TBR [ 36 ] were reduced, while SNR increased [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

“…TVREM was used for PET phantom and patient scanning with 68 Ga-PSMA (20 patients) [ 35 ] and 68 Ga-DOTATATE (17 patients) [ 36 ]. As the penalization factor α increased, image noise [ 35 , 36 ], CR [ 35 ], SUV max [ 35 , 36 ], and TBR [ 36 ] were reduced, while SNR increased [ 36 ]. SUV mean remained similar when the penalization factor increased [ 35 , 36 ].…”

Section: Resultsmentioning

confidence: 99%

“…As the penalization factor α increased, image noise [ 35 , 36 ], CR [ 35 ], SUV max [ 35 , 36 ], and TBR [ 36 ] were reduced, while SNR increased [ 36 ]. SUV mean remained similar when the penalization factor increased [ 35 , 36 ]. Additionally, it was observed that the penalization factor α had a greater effect as the size of the lesions decreased.…”

Section: Resultsmentioning

confidence: 99%

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Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

et al. 2022

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Gallium-68 (68Ga) is characterized by relatively high positron energy compared to Fluorine-18 (18F), causing substantial image quality degradation. Furthermore, the presence of statistical noise can further degrade image quality. The aim of this literature review is to identify the recently developed positron range correction techniques for 68Ga, as well as noise reduction methods to enhance the image quality of low count 68Ga PET imaging. The search engines PubMed and Scopus were employed, and we limited our research to published results from January 2010 until 1 August 2022. Positron range correction was achieved by using either deblurring or deep learning approaches. The proposed techniques improved the image quality and, in some cases, achieved an image quality comparable to 18F PET. However, none of these techniques was validated in clinical studies. PET denoising for 68Ga-labeled radiotracers was reported using either reconstruction-based techniques or deep learning approaches. It was demonstrated that both approaches can substantially enhance the image quality by reducing the noise levels of low count 68Ga PET imaging. The combination of 68Ga-specific positron range correction techniques and image denoising approaches may enable the application of low-count, high-quality 68Ga PET imaging in a clinical setting.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

et al. 2022

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“…Moreover, experimental results show that the algorithms based on the reconstructed points discretization model and its geometric symmetry structure can effectively improve the imaging speed as well as the imaging precision. This methodology is the foundation for many popular iterative methods for images reconstruction [ 30 , 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy

Chacón

García

Bosch-Bayard

et al. 2022

Behavioral Sciences

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To explore the role of the interictal and ictal SPECT to identity functional neuroimaging biomarkers for SUDEP risk stratification in patients with drug-resistant focal epilepsy (DRFE). Twenty-nine interictal-ictal Single photon emission computed tomography (SPECT) scans were obtained from nine DRFE patients. A methodology for the relative quantification of cerebral blood flow of 74 cortical and sub-cortical structures was employed. The optimal number of clusters (K) was estimated using a modified v-fold cross-validation for the use of K means algorithm. The two regions of interest (ROIs) that represent the hypoperfused and hyperperfused areas were identified. To select the structures related to the SUDEP-7 inventory score, a data mining method that computes an automatic feature selection was used. During the interictal and ictal state, the hyperperfused ROIs in the largest part of patients were the bilateral rectus gyrus, putamen as well as globus pallidus ipsilateral to the seizure onset zone. The hypoperfused ROIs included the red nucleus, substantia nigra, medulla, and entorhinal area. The findings indicated that the nearly invariability in the perfusion pattern during the interictal to ictal transition observed in the ipsi-lateral putamen F = 12.60, p = 0.03, entorhinal area F = 25.80, p = 0.01, and temporal middle gyrus F = 12.60, p = 0.03 is a potential biomarker of SUDEP risk. The results presented in this paper allowed identifying hypo- and hyperperfused brain regions during the ictal and interictal state potentially related to SUDEP risk stratification.

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Phantom and clinical evaluation of the effect of a new Bayesian penalized likelihood reconstruction algorithm (HYPER Iterative) on 68Ga-DOTA-NOC PET/CT image quality

Cui

et al. 2022

EJNMMI Res

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Background Bayesian penalized likelihood (BPL) algorithm is an effective way to suppress noise in the process of positron emission tomography (PET) image reconstruction by incorporating a smooth penalty. The strength of the smooth penalty is controlled by the penalization factor. The aim was to investigate the impact of different penalization factors and acquisition times in a new BPL algorithm, HYPER Iterative, on the quality of 68Ga-DOTA-NOC PET/CT images. A phantom and 25 patients with neuroendocrine neoplasms who underwent 68Ga-DOTA-NOC PET/CT were included. The PET data were acquired in a list-mode with a digital PET/CT scanner and reconstructed by ordered subset expectation maximization (OSEM) and the HYPER Iterative algorithm with seven penalization factors between 0.03 and 0.5 for acquisitions of 2 and 3 min per bed position (m/b), both including time-of-flight and point of spread function recovery. The contrast recovery (CR), background variability (BV) and radioactivity concentration ratio (RCR) of the phantom; The SUVmean and coefficient of variation (CV) of the liver; and the SUVmax of the lesions were measured. Image quality was rated by two radiologists using a five-point Likert scale. Results The CR, BV, and RCR decreased with increasing penalization factors for four “hot” spheres, and the HYPER Iterative 2 m/b groups with penalization factors of 0.07 to 0.2 had equivalent CR and superior BV performance compared to the OSEM 3 m/b group. The liver SUVmean values were approximately equal in all reconstruction groups (range 5.95–5.97), and the liver CVs of the HYPER Iterative 2 m/b and 3 m/b groups with the penalization factors of 0.1 to 0.2 were equivalent to those of the OSEM 3 m/b group (p = 0.113–0.711 and p = 0.079–0.287, respectively), while the lesion SUVmax significantly increased by 19–22% and 25%, respectively (all p < 0.001). The highest qualitative score was attained at a penalization factor of 0.2 for the HYPER Iterative 2 m/b group (3.20 ± 0.52) and 3 m/b group (3.70 ± 0.36); those scores were comparable to or greater than that of the OSEM 3 m/b group (3.09 ± 0.36, p = 0.388 and p < 0.001, respectively). Conclusions The HYPER Iterative algorithm with a penalization factor of 0.2 resulted in higher lesion contrast and lower image noise than OSEM for 68Ga-DOTA-NOC PET/CT, allowing the same image quality to be achieved with less injected radioactivity and a shorter acquisition time.

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The Impact of Total Variation Regularized Expectation Maximization Reconstruction on 68Ga-DOTA-TATE PET/CT Images in Patients With Neuroendocrine Tumor

Cited by 5 publications

References 22 publications

Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review

Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy

Phantom and clinical evaluation of the effect of a new Bayesian penalized likelihood reconstruction algorithm (HYPER Iterative) on 68Ga-DOTA-NOC PET/CT image quality

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