Performance Evaluation of a PET of 7T Bruker Micro-PET/MR Based on NEMA NU 4-2008 Standards

Doss, Kishore Krishnagiri Manoj; Mion, Pei En; Kao, Yu-Chieh Jill; Kuo, Tsung-Ter; Chen, Jyh-Cheng

doi:10.3390/electronics11142194

“…The two side peaks correspond to the centers of the two outer rings of crystal blocks, which lie at a distance of from the axial center. This behavior is typical for multi-ring PET scanners [ 23 , see e.g.,].…”

Section: Discussionmentioning

confidence: 94%

SAFIR-I: first NEMA NU 4-2008-based performance characterization

Bebié,

Lustermann,

Debus

et al. 2023

EJNMMI Phys

0

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Background Small Animal Fast Insert for MRI detector I (SAFIR-I) is a novel Positron Emission Tomography insert for a $$7\,\hbox {T}$$ 7 T Bruker BioSpec 70/30 Ultra Shield Refrigerated Magnetic Resonance Imaging (MRI) system. It facilitates truly simultaneous quantitative imaging in mice and rats at injected activities as high as $$500\,\hbox {MBq}$$ 500 MBq . Exploitation of the resulting high count rates enables quick image formation at few seconds per frame. In this investigation, key performance parameters of SAFIR-I have been determined according to the evaluations outlined in the National Electrical Manufacturers Association (NEMA) Standards Publication NU 4-2008 (NEMA-NU4) protocol. Results Using an energy window of 391 to $$601\,\hbox {keV}$$ 601 keV and a Coincidence Timing Window of $$500\,\hbox {ps}$$ 500 ps , the following performance was observed: The average spatial resolution at $$5\,\hbox {mm}$$ 5 mm radial offset (Full Width at Half Maximum) is $$2.54\,\hbox {mm}$$ 2.54 mm when using Filtered Backprojection, 3D Reprojection reconstruction. For the mouse- and rat-like phantoms, the maximal Noise-Equivalent Count Rates (NECRs) are $$1368\,\hbox {kcps}$$ 1368 kcps at the highest tested average effective concentration of $$14.7\,\hbox {MBq}\,\hbox {cc}^{-1}$$ 14.7 MBq cc - 1 , and $$713\,\hbox {kcps}$$ 713 kcps at the highest tested average effective concentration of $$1.72\,\hbox {MBq}\,\hbox {cc}^{-1}$$ 1.72 MBq cc - 1 , respectively. The NECR peak is not yet reached for either of these cases. The peak sensitivity is $$1.46\,\%$$ 1.46 % . The Image Quality phantom uniformity standard deviation is $$4.8\,\%$$ 4.8 % . The Recovery Coefficient for the $$5\,\hbox {mm}$$ 5 mm rod is $$(1.08 \pm 0.10)$$ ( 1.08 ± 0.10 ) . The Spill-Over Ratios are $$(0.22 \pm 0.03)$$ ( 0.22 ± 0.03 ) and $$(0.22 \pm 0.02)$$ ( 0.22 ± 0.02 ) , for the water- and air-filled cylinder, respectively. An accuracy of $$4.3\,\%$$ 4.3 % was achieved for the quantitative calibration of reconstructed voxel values. Conclusions The measured performance parameters indicate that the various design goals have been achieved. SAFIR-I offers excellent performance, especially at the high activities it was designed for. This facilitates planned experiments with fast tracer kinetics in small animals. Ways to potentially improve performance can still be explored. Simultaneously, further performance gains can be expected for a forthcoming insert featuring 2.7 times longer axial coverage named Small Animal Fast Insert for MRI detector II (SAFIR-II).

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“…According to the above results, it can be inferred that the in-plane (Y and Z directions) resolution of the DH-Mammo PET is about 2.00 mm, which is larger than the in-plane resolution deduced from the point source and Derenzo phantom experiments in this work. We think that the spatial resolution inconsistency, which is also existed in other published papers (Liu et al 2021;Doss et al 2022), may be mainly due to the differences in phantoms, such as the presence of background, etc Therefore, for the special structures where NEMA standards do not apply, resolution evaluation with a variety of phantoms is required to achieve a fair evaluation.…”

Section: Discussionmentioning

confidence: 98%

DH-Mammo PET: a dual-head positron emission mammography system for breast imaging

Shi¹,

Wang²,

Zhou³

et al. 2022

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Objective: To develop a simultaneous PET-Optical (OPET) breast imaging dual-head PET subsystem, called DH-Mammo PET, for accurate, early diagnosis and efficacy assessment of breast cancer with high resolution and sensitivity. Approach: We developed a breast-dedicated PET based on LYSO crystal, silicon photomultiplier array and multi-voltage threshold sampling technique. It consists of two detector heads, each with a detection area of 216 mm × 145.5 mm. The distance between the detector heads is fixed at 120 mm. In order to extract coincidences and correct data, GPU-based software coincidence processing, random, scatter, normalization, gap-filling and attenuation corrections were applied in turn. The images were reconstructed using maximum likelihood expectation maximization with depth of interaction (DOI) modeling. The performance of DH-Mammo PET was evaluated referring to NEMA NU 4-2008, NU 2-2007 and Chinese industry recommended standard YY/T 1835-2022. Besides, several clinical patient images of DH-Mammo PET were compared with those of a whole-body PET/CT. Main results: The energy resolution was 14.5%, and time resolution was < 1.31 ns. Indicated by the 22Na point source imaging, its spatial resolution was 2.60 mm (5.40 mm), 1.00 mm (1.04 mm), and 0.96 mm (0.93 mm) in the X, Y and Z directions, respectively, using the system response matrix with (without) DOI modeling. Indicated by the Derenzo phantom imaging, the spatial resolution was ~3.0 mm, <1.2 mm, and <1.2 mm in the X, Y and Z directions. The system sensitivity was 6.87%, 4.89% and 3.37% with an energy window of 100-800, 250-750 and 350-650 keV, respectively. The scatter fraction was 26.43%, and the peak NECR was 162.6 kcps at 24.1 MBq for the modified rat-like phantom. As for the recovery coefficients, they ranged from 0.15 to 1.04 for rods between 1 mm and 5 mm obtained with a NEMA image quality phantom. The spill-over ratio for the air-filled and water-filled chamber was 0.05 and 0.11, respectively. DH-Mammo PET can provide more image details in clinical experiments and fulfil a fast scan with 60s-120 s acquisition time. Significance: Good spatial resolution and high sensitivity of DH-Mammo PET would enable fast and accurate PET imaging of the breast. Besides, combining the DH-Mammo PET with the diffuse optical tomography would make full use of tumor metabolic imaging and tissue endogenous optical imaging, which would improve the accuracy of early clinical diagnosis of small lesions of breast cancers.

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“…The dataset includes images taken of five micro PET phantoms (Two different sized micro Derenzo phantoms, image quality phantom, three rods phantom, and mouse shaped phantom) as well as two rats. The PET scans were examined using PET Inline scanner (Bruker BioSpin MRI GmbH, Ettlingen, Germany) 43 . Measurements were acquired over approximately 10 min for phantoms and 15 min for animals.…”

Section: Methodsmentioning

confidence: 99%

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low‐dose PET images in the sinogram domain

Manoj Doss,

Chen

2023

Medical Physics

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BackgroundLow‐dose positron emission tomography (LD‐PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD‐PET images often exhibit poor quality and high noise levels due to the low signal‐to‐noise ratio. Deep learning (DL) techniques such as generative adversarial networks (GANs) and convolutional neural network (CNN) have the capability to enhance the quality of images derived from noisy or low‐quality PET data, which encodes critical information about radioactivity distribution in the body.PurposeOur objective was to optimize the image quality and reduce noise in preclinical PET images by utilizing the sinogram domain as input for DL models, resulting in improved image quality as compared to LD‐PET images.MethodsA GAN and CNN model were utilized to predict high‐dose (HD) preclinical PET sinograms from the corresponding LD preclinical PET sinograms. In order to generate the datasets, experiments were conducted on micro‐phantoms, animal subjects (rats), and virtual simulations. The quality of DL‐generated images was weighted by performing the following quantitative measures: structural similarity index measure (SSIM), root mean squared error (RMSE), peak signal‐to‐noise ratio (PSNR), signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR). Additionally, DL input and output were both subjected to a spatial resolution calculation of full width half maximum (FWHM) and full width tenth maximum (FWTM). DL outcomes were then compared with the conventional denoising algorithms such as non‐local means (NLM), block‐matching, and 3D filtering (BM3D).ResultsThe DL models effectively learned image features and produced high‐quality images, as reflected in the quantitative metrics. Notably, the FWHM and FWTM values of DL PET images exhibited significantly improved accuracy compared to LD, NLM, and BM3D PET images, and just as precise as HD PET images. The MSE loss underscored the excellent performance of the models, indicating that the models performed well. To further improve the training, the generator loss (G loss) was increased to a value higher than the discriminator loss (D loss), thereby achieving convergence in the GAN model.ConclusionsThe sinograms generated by the GAN network closely resembled real HD preclinical PET sinograms and were more realistic than LD. There was a noticeable improvement in image quality and noise factor in the predicted HD images. Importantly, DL networks did not fully compromise the spatial resolution of the images.

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Performance Evaluation of a PET of 7T Bruker Micro-PET/MR Based on NEMA NU 4-2008 Standards

Cited by 8 publications

References 37 publications

SAFIR-I: first NEMA NU 4-2008-based performance characterization

SAFIR-I: first NEMA NU 4-2008-based performance characterization

DH-Mammo PET: a dual-head positron emission mammography system for breast imaging

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low‐dose PET images in the sinogram domain

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