Performance of 18F-FDG PET/MRI and its parameters in staging and neoadjuvant therapy response evaluation in bladder cancer.

18F-FDG PET/MRI shows potential efficacy in the diagnosis of bladder cancer (BLCA). However, the performance of 18F-FDG PET/MRI in staging and neoadjuvant therapy (NAT) response evaluation for BLCA patients remains elusive. Here, we conduct this study to evaluate the performance of 18F-FDG PET/MRI and its derived parameters for tumor staging and NAT response prediction in BLCA. Forty BLCA patients were retrospectively enrolled to evaluate the performance of 18F-FDG PET/MRI in staging and NAT response prediction in BLCA. The feasibility of using 18F-FDG PET/MRI-related parameters for tumor staging and NAT response evaluation was also analyzed. In conclusion, 18F-FDG PET/MRI is found to show good performance in the BLCA staging and NAT response prediction. Moreover, ΔSUVmean is an efficacious candidate parameter for NAT response prediction. This study highlights that 18F-FDG PET/MRI is a promising imaging approach in the clinical diagnosis and treatment for BLCA.

Comparative study of bilateral putamen for patients with severe Parkinson's disease detected by 1H magnetic resonance spectroscopy.

Background: The diagnosis of Parkinson's disease (PD) is challenging because the clinical symptoms overlap with other neurodegenerative diseases. The discovery of reliable biomarkers is highly expected to facilitate clinical diagnosis. Through the analysis of the 1H magnetic resonance spectroscopy (1H-MRS) in the putamen, the purpose of the study was to discuss the possibility of the difference in metabolite concentrations between the left and right putamen as biomarkers for patients with severe PD. Methods: We collected 1H-MRS of unilateral or bilateral putamen from 41 patients and used the independent sample t-test and paired t-test to analyze 4 metabolite concentrations, including choline (Cho), total N-acetyl aspartate (tNAA), total creatine (tCr), and combined glutamate and glutamine; Bonferroni correction was used to correct P values for multiple comparisons. We designed 4 controlled experiments as follows: (I) PD patients versus healthy controls (HCs) in the left putamen; (II) PD patients versus HCs in the right putamen; (III) the left putamen versus the right putamen for PD patients; and (IV) the left putamen versus the right putamen for HCs. Results: No statistically significant differences (P>0.05) were detected among 4 metabolites in the ipsilateral and bilateral putamen for the PD and HCs groups, except for tCr in the left putamen (PD 6.426±0.557, HCs 6.026±0.460, P=0.046) for ipsilateral comparisons. Conclusions: In the bilateral putamen of severe PD patients, there was no statistically significant difference in the 4 metabolites. The difference (P<0.05) in tCr in the left putamen might be a potential biomarker to distinguish HCs from severe patients in clinic. This might provide a reference for the clinical diagnosis and acquisition strategy of 1H-MRS in severe PD.

One-Dimensional Deep Low-Rank and Sparse Network for Accelerated MRI.

Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2D convolution since many magnetic resonance images or their corresponding k-space are in 2D. In this work, we present a new approach that explores the 1D convolution, making the deep network much easier to be trained and generalized. We further integrate the 1D convolution into the proposed deep network, named as One-dimensional Deep Low-rank and Sparse network (ODLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed ODLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, ODLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1D deep learning scheme is memory-efficient and robust in fast MRI.

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.

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.

Small lesion depiction and quantification accuracy of oncological 18F-FDG PET/CT with small voxel and Bayesian penalized likelihood reconstruction.

BACKGROUND: To investigate the influence of small voxel Bayesian penalized likelihood (SVB) reconstruction on small lesion detection compared to ordered subset expectation maximization (OSEM) reconstruction using a clinical trials network (CTN) chest phantom and the patients with 18F-FDG-avid small lung tumors, and determine the optimal penalty factor for the lesion depiction and quantification. METHODS: The CTN phantom was filled with 18F solution with a sphere-to-background ratio of 3.81:1. Twenty-four patients with 18F-FDG-avid lung lesions (diameter < 2 cm) were enrolled. Six groups of PET images were reconstructed: routine voxel OSEM (RVOSEM), small voxel OSEM (SVOSEM), and SVB reconstructions with four penalty factors: 0.6, 0.8, 0.9, and 1.0 (SVB0.6, SVB0.8, SVB0.9, and SVB1.0). The routine and small voxel sizes are 4 × 4 × 4 and 2 × 2 × 2 mm3. The recovery coefficient (RC) was calculated by dividing the measured activity by the injected activity of the hot spheres in the phantom study. The SUVmax, target-to-liver ratio (TLR), contrast-to-noise ratio (CNR), the volume of the lesions, and the image noise of the liver were measured and calculated in the patient study. Visual image quality of the patient image was scored by two radiologists using a 5-point scale. RESULTS: In the phantom study, SVB0.6, SVB0.8, and SVB0.9 achieved higher RCs than SVOSEM. The RC was higher in SVOSEM than RVOSEM and SVB1.0. In the patient study, the SUVmax, TLR, and visual image quality scores of SVB0.6 to SVB0.9 were higher than those of RVOSEM, while the image noise of SVB0.8 to SVB1.0 was equivalent to or lower than that of RVOSEM. All SVB groups had higher CNRs than RVOSEM, but there was no difference between RVOSEM and SVOSEM. The lesion volumes derived from SVB0.6 to SVB0.9 were accurate, but over-estimated by RVOSEM, SVOSEM, and SVB1.0, using the CT measurement as the standard reference. CONCLUSIONS: The SVB reconstruction improved lesion contrast, TLR, CNR, and volumetric quantification accuracy for small lesions compared to RVOSEM reconstruction without image noise degradation or the need of longer emission time. A penalty factor of 0.8-0.9 was optimal for SVB reconstruction for the small tumor detection with 18F-FDG PET/CT.

Dosimetry of 177Lu-DOTATOC first circle treatment in patients with advanced metastatic neuroendocrine tumors: A pilot study in China.

First cycle dosimetry calculation of 177Lu-DOTATOC (single activity:1.59-3.49 GBq) was carried out in eight patients with advanced neuroendocrine tumors (NETs) who underwent whole-body planar (0.5, 24, 48, 72 h) and SPECT/CT scans (24 h). Focal uptake of 177Lu-DOTATOC was found in primary and metastatic tumors. Organs with the highest absorbed doses per injected activity were tumors (1.293 ± 0.862 mGy/MBq) and spleen (0.461 ± 0.408 mGy/MBq), while low absorbed doses were observed in kidneys (0.384 ± 0.112 mGy/MBq) and bone marrow (0.0297 ± 0.0123 mGy/MBq). 177Lu-DOTATOC is safe, well-tolerated and appropriate in Chinese NETs patients for PRRT.

Hybrid PET-MRI for early detection of dopaminergic dysfunction and microstructural degradation involved in Parkinson's disease.

Dopamine depletion and microstructural degradation underlie the neurodegenerative processes in Parkinson's disease (PD). To explore early alterations and underlying associations of dopamine and microstructure in PD patients utilizing the hybrid positron emission tomography (PET)-magnetic resonance imaging (MRI). Twenty-five PD patients in early stages and twenty-four matched healthy controls underwent hybrid 18F-fluorodopa (DOPA) PET-diffusion tensor imaging (DTI) scanning. The striatal standardized uptake value ratio (SUVR), DTI maps (fractional anisotropy, FA; mean diffusivity, MD) in subcortical grey matter, and deterministic tractography of the nigrostriatal pathway were processed. Values in more affected (MA) side, less affected (LA) side and mean were analysed. Correlations and mediations among PET, DTI and clinical characteristics were further analysed. PD groups exhibited asymmetric pattern of dopaminergic dysfunction in putamen, impaired integrity in the microstructures (nigral FA, putaminal MD, and FA of nigrostriatal projection). On MA side, significant associations between DTI metrics (nigral FA, putaminal MD, and FA of nigrostriatal projection) and motor performance were significantly mediated by putaminal SUVR, respectively. Early asymmetric disruptions in putaminal dopamine concentrations and nigrostriatal pathway microstructure were detected using hybrid PET-MRI. The findings further implied that molecular degeneration mediates the modulation of microstructural disorganization on motor dysfunction in the early stages of PD.