Transformers have revolutionized medical image restoration, but the quadratic complexity still poses limitations for their application to high-resolution medical images. The recent advent of the Receptance Weighted Key Value (RWKV) model in the natural language processing field has attracted much attention due to its ability to process long sequences efficiently. To leverage its advanced design, we propose Restore-RWKV, the first RWKV-based model for medical image restoration. Since the original RWKV model is designed for 1D sequences, we make two necessary modifications for modeling spatial relations in 2D medical images. First, we present a recurrent WKV (Re-WKV) attention mechanism that captures global dependencies with linear computational complexity. Re-WKV incorporates bidirectional attention as basic for a global 16 receptive field and recurrent attention to effectively model 2D dependencies from various scan directions. Second, we develop an omnidirectional token shift (Omni-Shift) layer that enhances local dependencies by shifting tokens from all directions and across a wide context range. These adaptations make the proposed Restore-RWKV an efficient and effective model for medical image restoration. Even a lightweight variant of Restore-RWKV, with only 1.16 million parameters, achieves comparable or even superior results compared to existing state-of-the-art (SOTA) methods. Extensive experiments demonstrate that the resulting Restore-RWKV achieves SOTA performance across a range of medical image restoration tasks, including PET image synthesis, CT image denoising, MRI image superresolution, and all-in-one medical image restoration. Code is available at: https://github.com/Yaziwel/Restore-RWKV.

Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for reconstruction. We notice that the importance of each feature is different and they should be weighted dissimilarly so that tiny information can be captured by the neural network. Furthermore, the synthesis on some regions of interest is important in some applications. Here we propose a novel segmentation guided style-based generative adversarial network (SGSGAN) for PET synthesis. (1) We put forward a style-based generator employing style modulation, which specifically controls the hierarchical features in the translation process, to generate images with more realistic textures. (2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance. Extensive experiments show the superiority of our overall framework in PET synthesis, especially on those regions of interest.

Purpose PETbox is a low cost bench top preclinical PET scanner dedicated to pharmacokinetic and pharmacodynamic mouse studies. A prototype system was developed at our institute, and this manuscript characterizes the performance of the prototype system. Procedures The PETbox detector consists of a 20 × 44 bismuth germanate crystal array with a thickness of 5 mm and cross-section size of 2.05 × 2.05 mm. Two such detectors are placed facing each other at a spacing of 5 cm, forming a dual-head geometry optimized for imaging mice. The detectors are kept stationary during the scan, making PETbox a limited angle tomography system. 3D images are reconstructed using a maximum likelihood and expectation maximization (ML–EM) method. The performance of the prototype system was characterized based on a modified set of the NEMA NU 4-2008 standards. Results In-plane image spatial resolution was measured to be an average of 1.53 mm full width at half maximum for coronal images and 2.65 mm for the anterior–posterior direction. The volumetric reconstructed resolution was below 8 mm³ at most locations in the field of view (FOV). The sensitivity, scatter fraction, and noise equivalent count rate (NECR) were measured for different energy windows. With an energy window of 150 - 650 keV and a timing window of 20 ns optimized for mouse imaging, the peak absolute sensitivity was 3.99% at the center of FOV and a peak NECR of 20 kcps was achieved for a total activity of 3.2 MBq (86.8 μCi). Phantom and in vivo imaging studies were performed and demonstrated the utility of the system at low activity levels. The quantitation capabilities of the system were also characterized showing that despite the limited angle tomography, reasonably good quantification accuracy was achieved over a large dynamic range of activity levels. Conclusions The presented results demonstrate the potential of this new tomograph for small animal imaging.

PoleStar m660 is a newly developed clinical PET/CT system with time-of-flight (TOF) capability. The aim of this study is to characterize the performance of the new system. Spatial resolution, sensitivity, scatter fraction, and noise equivalent count rate (NECR) were measured on the scanner according to the NEMA NU 2-2012 protocol. The timing resolution was measured using a rotating line source that orbited around the center of field of view (CFOV) at a radius of 20 cm. The image quality phantom was also imaged to quantify the percent contrast, percent background variability, and residual error. The impacts of data acquisition time and bed overlap on the PET image quality were also evaluated using phantom and patient studies. The transverse (axial) spatial resolutions were 3.59 mm (3.67), 4.08 mm (4.65), and 5.32 mm (6.48) full width at half maximum (FWHM) at 1 cm, 10 cm, and 20 cm, respectively, off the CFOV. The measured sensitivity was 10.7 cps/kBq at the CFOV and 10.4 cps/kBq at 10 cm off the CFOV. The peak NECR was 216.7 kcps at an activity concentration of 29.1 kBq/ml, and the scatter fraction was 38.2%. An average of 435 ps FWHM timing resolution was measured. For the image quality phantom, the contrast recovery ratios ranged from 33.9 to 76.4%, while the background variability ranged from 4.7 to 2.0%. In the preliminary clinical study, no noticeable difference in the image quality was observed when the scan time for the whole body and brain was reduced to 1 min/bed and 3 min, respectively. The tested 21% bed overlap showed no significant difference in the image quality compared with the default 38% bed overlap setting. The physical performances of the PoleStar m660 PET/CT system showed good sensitivity, count rate performance, and timing resolution. The improved performance could help to reduce the acquisition time and bed overlap in the clinical application without detectable compromise in the image quality.

Accurate differentiation of intermediate/high mitosis-karyorrhexis index (MKI) from low MKI is vital for the further management of neuroblastoma. The purpose of this research was to investigate the efficacy of ¹⁸F-FDG PET/CT–based radiomics features for the prediction of MKI status of pediatric neuroblastoma via machine learning. A total of 102 pediatric neuroblastoma patients were retrospectively enrolled and divided into training (68 patients) and validation sets (34 patients) in a 2:1 ratio. Clinical characteristics and radiomics features were extracted by XGBoost algorithm and were used to establish radiomics and clinical models for MKI status prediction. A combined model was developed, encompassing clinical characteristics and radiomics features and presented as a radiomics nomogram. The predictive performance of the models was evaluated by AUC and decision curve analysis. The radiomics model yielded AUC of 0.982 (95% CI: 0.916, 0.999) and 0.955 (95% CI: 0.823, 0.997) in the training and validation sets, respectively. The clinical model yielded AUC of 0.746 and 0.670 in the training and validation sets, respectively. The combined model demonstrated AUC of 0.988 (95% CI: 0.924, 1.000) and 0.951 (95% CI: 0.818, 0.996) in the training and validation sets, respectively. The radiomics features could non-invasively predict MKI status of pediatric neuroblastoma with high accuracy.