90Y-microsphere radioembolization is an established treatment for primary and metastatic liver cancer. Post-treatment PET/CT enables voxel-level absorbed dose estimation, but the optimal calculation method remains under evaluation. In this study, we compared three dosimetry approaches: Monte Carlo (MC), voxel S-value (VSV) method, and local energy deposition method (LDM). Post-treatment PET/CT data sets from 68 patients were analyzed (6 from the Deep Blue Data Repository and 62 from Beijing Tsinghua Changgung Hospital). MC dosimetry was implemented in GATE v9.0 and served as the reference standard. VSV was derived by convolving PET images with a precomputed 90Y voxel kernel, while LDM assumed complete local absorption of β-energy. Agreement with MC was assessed qualitatively using relative-difference maps, dose profiles, and isodose overlays, and quantitatively via voxelwise root-mean-square error (RMSE), Pearson correlation, Bland-Altman analysis of mean absorbed dose and equivalent uniform biological effective dose (EUBED), and absolute maximum deviations between cumulative and differential dose-volume histograms (DVHs) (1 Gy bins). MC simulations required ∼41 h per case, whereas the VSV kernel calculation required only one simulation with ∼6 h. Then VSV and LDM costed only a few seconds to obtain the dose images. VSV preserved MC isodose geometry and dose-profile shapes, while LDM overestimated doses in high-dose regions. At profile maxima, VSV's peak relative deviation was ∼1-3% versus ∼12-19% for LDM. Across patients, VSV achieved a voxelwise RMSE of 2.21% and <3% differences in mean dose and EUBED, with tighter Bland-Altman limits than LDM. LDM showed larger bias, wider limits, and greater DVH deviations at higher doses, confirming that VSV attains near-MC accuracy with orders-of-magnitude faster computation. Overall, all three methods enable voxel-level dosimetry of 90Y-microsphere therapy, but VSV combines MC-like accuracy with dramatically reduced computation time. VSV thus offers a clinically practical solution for rapid and reliable post-treatment dose verification and may support more personalized treatment evaluation in 90Y radioembolization.

Background: In the clinical application of brachytherapy, the relevant quantities of brachytherapy seed strength must be converted into absorbed dose at a reference depth of 1 cm in water. The current method of obtaining the absorbed dose in water is based on the air kerma strength and dose rate constant, which has an uncertainty of more than 10% (k=2), potentially affecting cancer treatment outcomes. Purpose: To ensure accurate dosimetry for 125I brachytherapy seeds, an extrapolation chamber embedded in the water-equivalent material was designed and manufactured to measure the absorbed dose in water directly. Methods: The mathematical model for determining the absorbed dose in water is based on radiation transport theory, where the key term conversion factor C(xi+1,xi) is determined using the Monte Carlo (MC) methods. In this paper, the basic structure, the measurement method, and the MC simulation of the extrapolation chamber are described. The dose rate constant of the model 6711 125I brachytherapy seed was obtained using three methods (experimental measurement, MC simulation, and AAPM recommended values), and the results was compared and analyzed. Results: The absorbed dose in water of the model 6711 125I brachytherapy seed was determined, and after repeated measurements and uncertainty evaluation, the result was 12.39 mGy/h, with an uncertainty of 3.5% (k=2). In addition, the brachytherapy seed was calibrated using an absolute measurement device for the air kerma strength, and its dose rate constant was calculated, which was in good agreement with both the AAPM-recommended values and MC simulated values. Conclusions: We successfully developed an absolute measurement device for the absorbed dose in water, which reduced the measurement uncertainty for 125I brachytherapy seeds and achieved dose accuracy for external radiotherapy. This study contributes to the establishment of primary standards for the absorbed dose in water of 125I brachytherapy seeds.

Yttrium-90 resin microspheres selective internal radiation therapy ( 90 $$ {}^{90} $$ Y-SIRT) has been increasingly adopted worldwide as a locoregional treatment option for appropriately selected patients with liver malignancies. The key to ensuring that the tumor receives an adequate radiation dose while minimizing the dose to normal tissues is to optimize the trade-off between tumor control and the probability of normal tissue complications. To accurately determine a patient's internal radiation dose, pre-treatment dose planning and post-treatment dose verification using dosimetric methods are recommended and increasingly considered best practice in 90 $$ {}^{90} $$ Y-SIRT, where feasible. Due to the increased demand for personalized treatment and dose accuracy in clinical practice, 90 $$ {}^{90} $$ Y-SIRT dosimetry has transitioned from organ-level to voxel-level dosimetry. This paper introduces the relevant principles and development history of 90 $$ {}^{90} $$ Y-SIRT dosimetry for resin microspheres. It also discusses the clinical performance, influencing factors and practical applications of relevant dosimetry methods. These include body surface area (BSA) method, MIRD multi-compartment model method, and partition model method; voxel-S-value (VSV); local deposition method (LDM); and Monte Carlo (MC) method. Finally, it covers the subsequent development of resin microsphere 90 $$ {}^{90} $$ Y-SIRT dosimetry.

Background:Myocardial involvement frequently occurs in patients with systemic sclerosis (SSc), leading to myocardial tissue changes and subclinical ventricular dysfunction. This form of primary cardiac involvement is one of the major determinants of mortality in SSc and may eventually progress to overt heart failure. To date, the pathophysiology and subsequent subclinical myocardial dysfunction in asymptomatic patients with SSc have not been extensively described. This study aimed to assess subclinical myocardial deformation and tissue abnormalities in asymptomatic patients with SSc using non-contrast cardiac magnetic resonance (CMR) feature tracking and native T1 mapping, and to explore the relationship between native T1 values and myocardial strain. Methods:The patients with SSc and age- and sex-matched healthy controls (HCs) underwent non-contrast CMR. Myocardial strain parameters and myocardial native T1 values were measured and compared between the HCs and patient subgroups based on skin involvement and disease duration. The association between the myocardial strain parameters and myocardial native T1 values was analyzed using Pearson's or Spearman's correlation and multivariate regression models. Results:A total of 44 patients with SSc and 22 HCs were enrolled in the study. The patients with SSc had significantly lower absolute longitudinal peak strain (-17.5%±3.0% vs. -18.9%±2.3%, P=0.030) and peak diastolic strain rate (0.9±0.2 vs. 1.1±0.3 1/s, P=0.025) than the HCs, after adjustment for age, sex, and body mass index. The patients with SSc had significantly higher myocardial native T1 values compared with the HCs (1,278.4±59.7 vs. 1,233.1±27.9 ms, P<0.001). The myocardial native T1 values demonstrated moderate correlations with the longitudinal strain parameters (|r|=0.40-0.56) in the patients with SSc, but this association did not persist after adjustment for confounders in multivariate regression models. No significant differences were found in the myocardial strain parameters and myocardial native T1 values between the patient subgroups (all P>0.05). Conclusions:Reduced absolute myocardial longitudinal strain and elevated myocardial native T1 values in patients with SSc indicate myocardial dysfunction and tissue impairment. These findings suggest that CMR may serve as a valuable tool for characterizing early alterations of the heart and optimizing the clinical management of patients with SSc.

We present a novel high-resolution, high-sensitivity time-space coincidence imaging system for cascade gamma photons. The system employs a LaBr 3 ring detector with hybrid pinhole-slit collimators, combining the high spatial resolution of pinhole collimation with the high detection efficiency of slit collimation. Simulations were conducted with a point source and a Derenzo phantom using 177 Lu, a theranostic radionuclide that emits cascade gamma-ray pairs at 113 keV and 208 keV. Images were reconstructed using direct back-projection (DBP) and maximum likelihood expectation maximization (MLEM), as well as two newly proposed algorithms: refined DBP (R-DBP) and multi-information joint reconstruction (MIJR).The system achieved a central coincidence efficiency of 2.98×10⁻⁵. Point source imaging with MLEM reconstruction yielded a spatial resolution of 1.7 mm full width at half maximum (FWHM) in the transaxial plane. Derenzo phantom imaging demonstrated clear resolution of hot rods as small as 1.2 mm in diameter, with a contrast-to-noise ratio (CNR) of 9.29 for the largest rods.These results demonstrate that the proposed ring detector enables high-quality cascade gamma photon coincidence imaging, with spatial resolution and sensitivity that significantly exceed those of previously reported systems. The combination of high resolution, reasonable sensitivity, and theranostic capability positions this technology as a promising platform for integrated diagnosis and therapy.

Stemmed from our novel single-photon imaging concept of detector self-collimation-which leverages detectors themselves as collimators to overcome the inherent resolution-sensitivity trade-off in conventional SPECT-this study presents the design and evaluation of the first full-ring self-collimation SPECT (SC-SPECT) scanner for small animal imaging. The system features four concentric detector rings and two interchangeable high-aperture-ratio tungsten collimator rings optimized for high-resolution (HR) and general-purpose (GP) imaging applications. Detector rings contain 480, 720, 960, and 1,200 evenly distributed GAGG(Ce) scintillators, each measuring 0.84 mm (tangential) $\times 6$ mm (radial) $\times 20$ mm (axial) and separated by 0.84-mm gaps to enable effective photon collimation. Inner detector rings and the collimator ring collectively provide collimation for photons reaching subsequent outer rings. Dual-end SiPM readouts facilitate axial depth-of-interaction measurements. Phantom and mouse studies are performed to assess the system's resolution, sensitivity, and field-of-view volume, and SC-SPECT demonstrates generally superior performance compared with state-of-the-art small-animal SPECT systems. Mouse bone images using 99mTc-MDP show CT-like resolution, clearly delineating detailed tracer uptake distributions within small structures such as mouse paws and skulls, indicating a significant technological advancement in small-animal SPECT imaging.

BACKGROUND:This study aims to assess the prognostic significance of myocardial blood flow (MBF) quantification parameters obtained through cadmium-zinc-telluride (CZT)-single-photon emission computed tomography (SPECT) in post-percutaneous coronary intervention (post-PCI) acute myocardial infarction (AMI) patient population. METHODS:A prospective cohort comprising 144 AMI patients who underwent primary PCI was enrolled. All participants received PCI within 12 hours of AMI diagnosis and subsequently underwent cardiac-dedicated CZT-SPECT dynamic imaging within two weeks post procedure. Quantitative MBF parameters, semiquantitative perfusion scores, and left ventricular functional parameters were collected. Major adverse cardiovascular events (MACEs) were defined as cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, heart failure, late coronary revascularization, or hospitalization due to unstable angina. RESULTS:Over a median follow-up duration of 27 months (interquartile range: 15-37), a total of 40 MACEs were observed. Stress myocardial blood flow (sMBF) (P < 0.001) and myocardial flow reserve (MFR) (P = 0.002) were significantly lower in patients with MACEs. Receiver operating characteristic analysis determined optimal prognostic thresholds: sMBF <1.39 (area under the curve [AUC]: 0.67, 95% confidence interval [CI]: 0.58-0.76, sensitivity: 46.2%, specificity: 90.0%, P < 0.001) and MFR <1.84 (AUC: 0.66, 95% CI: 0.57-0.75, sensitivity: 44.2%, specificity: 90.0%, P < 0.01). The Kaplan-Meier survival analysis demonstrated significantly diminished event-free survival in patients exhibiting impaired sMBF (log-rank = 14.67; P < 0.0001) or MFR (log-rank = 12.98; P = 0.0003). CONCLUSIONS:CZT-SPECT MBF quantification provides substantial prognostic value for post-PCI AMI patients. Importantly, stress-induced sMBF and MFR were identified as independent predictors of MACEs, demonstrating enhanced diagnostic efficacy over traditional semiquantitative perfusion parameters.

The methods for assessing colonic transit in patients with functional constipation include the radiological method and Tc-99m scintigraphic method. This study aims to validate the practicality and accuracy of the Tc-99m scintigraphic method in evaluating colon transit, while also exploring the significance of the geometric center (GC). Our study is a single-center, retrospective analysis. We examined the medical records of 47 patients, who underwent colonic transit by the Tc-99m scintigraphic method and the radiological method for investigation of chronic constipation. Geometric center (GC) and transmit index (TI) were calculated respectively in these two methods. The patients were divided into four types of constipation: normal transit constipation (NTC), slow transit constipation (STC), defecatory disorders (DD) and STC combined with DD. The results of the Tc-99m scintigraphic method and radiological method were consistent in the diagnosis of slow colon transmission (Kappa value = 0.718, P < 0.001). TI and 48-h GC were positively correlated (r = 0.657, p = 0.001). Patients with DD exhibited higher 24-h GC and 48-h GC values compared to those with STC. Tc-99m scintigraphic method can be used to evaluate colonic transit in patients with constipation, and GC values may be used to distinguish the types of constipation.

Prostate-specific membrane antigen-targeted radioligand therapy (PSMA-RLT) is a promising approach to treating metastatic castration-resistant prostate cancer (mCRPC). With the emergence of oxalyldiaminopropionic acid urea (ODAP-Urea) based radioligands targeting PSMA, novel paradigms focused on PSMA-RLT are garnering attention. This study aims to assess potentially novel ODAP-Urea-based radioligands prepared for PSMA-RLT. Albumin binding moieties (ABMs) were selected for optimization. Candidates were evaluated in vitro and subsequently investigated through biodistribution and imaging studies in 22Rv1 tumor-bearing mice. We synthesized five novel ODAP-Urea-based derivatives (CXY-18, CXY-19, CXY-20, CXY-21, CXY-23) with specific ABM. All compounds demonstrated high affinities for PSMA (Ki values ranging from 0.21 nM to 3.6 nM) and strong human albumin protein binding abilities (83.4 ± 1.6

Background:Due to the inherent limitations of imaging sensors, acquiring medical images that simultaneously provide functional metabolic information and detailed structural organization remains a significant challenge. Multi-modal image fusion has emerged as a critical technology for clinical diagnosis and surgical navigation, as it enables the integration of complementary information from different imaging modalities. However, existing deep learning (DL)-based fusion methods often face difficulties in effectively combining high-frequency detail information with low-frequency contextual information, which frequently leads to the degradation of high-frequency details. Therefore, there is a pressing need for a method that addresses these challenges, preserving both high- and low-frequency information while maintaining clear structural contours. In response to this issue, a novel convolutional neural network (CNN), named the multi-scale pyramid residual weight network (LYWNet), is proposed. The objective of this approach is to improve the fusion process by effectively integrating high- and low-frequency information, thereby enhancing the quality and accuracy of multimodal image fusion. This method aims to overcome the limitations of current fusion techniques and ensure the preservation of both functional and structural details, ultimately contributing to more precise clinical diagnoses and better surgical navigation outcomes. Methods:We propose a novel CNN, LYWNet, designed to address these challenges. LYWNet is composed of three modules: (I) data preprocessing module: utilizes three convolutional layers to extract both deep and shallow features from the input images. (II) Feature extraction module: incorporates three identical multi-scale pyramid residual weight (LYW) blocks in series, each featuring three interactive branches to preserve high-frequency detail information effectively. (III) Image reconstruction module: utilizes a fusion algorithm based on feature distillation to ensure the effective integration of functional and anatomical information. The proposed image fusion algorithm enhances the interaction of contextual cues and retains the metabolic details from functional images while preserving texture details from anatomical images. Results:The proposed LYWNet demonstrated its ability to retain high-frequency details during feature extraction, effectively combining them with low-frequency contextual information. The fusion results exhibited reduced differences between the fused image and the original images. The structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) were 0.5592±0.0536 and 17.3594±1.0211, respectively, for single-photon emission computed tomography-magnetic resonance imaging (SPECT-MRI), 0.5195±0.0730 and 14.5324±1.7365 for PET-MRI; 0.5376±0.0442 and 13.9202±0.7265 for magnetic resonance imaging-computed tomography. Conclusions:LYWNet excels at integrating high-frequency detail information and low-frequency contextual information, addressing the deficiencies of existing DL-based image fusion methods. This approach provides superior fused images that retain the functional metabolic information and anatomical texture, making it a valuable tool for clinical diagnosis and surgical navigation.

Cholestasis can lead to unreliable results of routine liver function assessment tests in clinical practice and the functional cutoff value of hepatectomy is still unclear. The aim of this study was to determine which 99mTc-GSA scintigraphy functional indicators can predict post-hepatectomy liver failure (PHLF) in patients before major liver resection due to malignant perihilar biliary disease. In addition, it aimed to assess the efficiency of functional future liver remnant (FLR) assessment of 99mTc-GSA scintigraphy indicators. A 99mTc-GSA scintigraphy was performed prior to planned surgery in 187 patients, including 81 patients with major liver resection. The 99mTc-GSA scintigraphy parameters including functional liver volume (FLV), ratio of the FLR functional volume to body weight (FLVFLR–BWR), and predictive residual index (PRI) were calculated from radioactive count in regions of FLR and total liver (TOTAL). Morphological liver volume (MLV) was calculated from computed tomography and standardized by standard liver volume (SLV). The efficacy of these parameters in predicting PHLF was compared using generalized linear mixed models and receiver operating characteristic (ROC) curve analysis. PHLF occurred in 22 patients, who showed lower MLVFLR/SLV, FLVFLR, FLVFLR/FLVTOTAL, FLVFLR–BWR, and PRI and higher resection rate (P < 0.05 for all) than patients without PHLF. After adjusting for clinical parameters, a decreased FLVFLR–BWR (odds ratio, OR 0.17; 95

BACKGROUND:The feasibility of renal multi-delay arterial spin labeling (ASL) imaging at 5 T remains unclear. PURPOSE:To evaluate the feasibility of the saturated multi-delay renal ASL (SAMURAI) sequence at 5 T by comparing image quality and perfusion quantification with 3 T. STUDY TYPE:Prospective, cross-sectional. POPULATION:Twenty healthy volunteers (28.6 ± 7.8 years, 9 males) for primary comparison; 6 volunteers (24.2 ± 1.5 years, 5 males) for reproducibility study at 5 T. FIELD STRENGTH/SEQUENCE:SAMURAI sequence at 3 T and 5 T. ASSESSMENT:The SAMURAI sequence was optimized at 5 T with renal-specific B1 shimming and an optimized saturation scheme by numerical simulation. Each participant underwent 3 T and 5 T scans in randomized order. Cortical T1 value, renal blood flow (RBF), arterial and tissue bolus arrival times were measured. The signal-to-noise ratio (SNR) and cortico-medullary contrast-to-noise ratio (CNR) were calculated from perfusion-weighted images. Short-term repeatability (n = 20) and reproducibility (n = 6) tests of quantitative parameters were performed at 5 T. STATISTICAL TESTS:Differences and agreement between 3 T and 5 T were analyzed using the Wilcoxon signed-rank test, intraclass correlation coefficients (ICC) and linear correlation analysis (R 2). The repeatability at 5 T was assessed by ICC. A p < 0.05 was considered statistically significant. RESULTS:Renal cortical T1 values were significantly higher at 5 T than 3 T (1417.9 ± 75.7 ms vs. 1184.2 ± 84.4 ms), with R 2 = 0.509. Cortical RBF showed an insignificant difference between 5 T and 3 T: 324.4 (interquartile range [IQR]: 310.1-366.4) vs. 329.7 (IQR: 309.5-368.1) [mL/100 g/min] (p = 0.333), with R 2 = 0.914. 5 T showed significantly higher mean SNR (4.6 vs. 3.9) and CNR (3.2 vs. 2.0) than 3 T across all inversion times, with excellent repeatability and reproducibility of quantitative parameters (ICC = 0.855-0.973). DATA CONCLUSIONS:Renal quantitative imaging with SAMURAI sequence at 5 T is feasible and repeatable, with significantly higher SNR and CNR than 3 T and strong interfield agreement of cortical RBF measurements. LEVEL OF EVIDENCE: 2: TECHNICAL EFFICACY STAGE:1.

Radionuclide imaging combines nuclear technique and medicine through the administration of radioactive drugs into living organisms, followed by imaging with specialized instruments. This technique is essential in modern medicine, facilitating diagnosis, treatment, medical research, exploration of drug mechanisms, and evaluation of drug efficacy. This review critically synthesizes the pivotal advancements in the field over the past decade, moving beyond a descriptive overview to analyze the clinical impact and translational barriers of emerging technologies. We evaluate key innovations in traditional modalities, such as the role of CZT detectors in transforming cardiac SPECT and the impact of TOF and DOI on quantitative accuracy in PET. Furthermore, we provide a comparative analysis of multimodal systems (e.g., PET/CT vs. PET/MRI), focusing on their clinical decision-making context. Emerging paradigms like self-collimation and cascade gamma photon imaging are examined as potential solutions to the inherent limitations of current systems, with a critical assessment of their technology readiness levels. A significant focus is placed on the rapidly evolving landscape of theranostics, highlighting the synergy between imaging and targeted radionuclide therapy. By identifying key trends, persistent challenges, and future directions, this review provides a comprehensive and critical perspective on the ongoing evolution of radionuclide imaging from a technological and clinical standpoint.

e16179 Background: To evaluate the predictive potential of preprocedural clinical factors and CT radiomic features for treatment response in hepatocellular carcinoma (HCC) patients after selective internal radiation therapy (SIRT) with yttrium-90 resin microspheres. Methods: A retrospective analysis was conducted on 112 HCC patients treated with SIRT at Tsinghua Chang Gung Hospital between September 2022 and June 2024, with at least three months of follow-up. Patients were divided into a training set (78) and a validation set (34) based on treatment time. Treatment response was assessed using the modified Response Evaluation in Solid Tumors (mRECIST) criteria, classifying complete or partial remission as objective response (OR) and stable or progressive disease as no response (NR). Clinical, laboratory, and radiomic data were collected. Radiomic features were extracted from arterial and portal-phase contrast-enhanced CT scans within two months pre-SIRT, normalized using Z-scores, and redundant features were removed via intraclass correlation coefficients and correlation analysis. Key features were selected through univariate logistic regression, least absolute shrinkage and selection operator (LASSO), variance inflation factor analysis, and stepwise regression. Using these features, a nomogram model was constructed with traditional logistic regression, alongside machine learning models, including logistic regression (LR), naive bayes (NB), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Model performance was evaluated by the area under the curve (AUC), and ROC curves were compared using the DeLong test. Results: Seven clinical features were selected. AUCs for the Nomogram, LR, NB, SVM, RF, XGBoost, and DNN models were 0.919, 0.915, 0.900, 0.953, 0.985, 0.979, and 0.901 in the training set, and 0.760, 0.760, 0.792, 0.757, 0.740, 0.802, and 0.774 in the validation set. Six radiomics features were selected, yielding training set AUCs of 0.927, 0.929, 0.888, 0.946, 1.000, 1.000, and 0.986, and validation set AUCs of 0.681, 0.670, 0.568, 0.611, 0.576, 0.608, and 0.663. In the combined analysis (5 radiomic and 2 clinical features), the AUCs were 0.943, 0.943, 0.914, 0.931, 1.000, 1.000, and 0.959 in the training set, and 0.736, 0.726, 0.623, 0.660, 0.679, 0.670, and 0.646 in the validation set. Machine learning models, particularly RF and XGBoost, outperformed traditional statistical models in the training set (p < 0.05), though no significant differences were observed in the validation set, where traditional models remained robust. Conclusions: Models integrating clinical and radiomic features, developed using statistical and machine learning algorithms, show promise for predicting response to SIRT in HCC patients.

OBJECTIVES:Systemic Sclerosis (SSc) is a systemic autoimmunity White matter hyperintensities (WMH) are typical indicators of cerebral small vessel disease, classified into periventricular hyperintensity (PVH) and deep white matter hyperintensity (DWMH). Our study aims to investigate the quantitative characteristics and distribution patterns of WMH between SSc patients, healthy population and between different SSc subtypes using magnetic resonance (MR) imaging. Whether cognitive dysfunction and anxiety/depression are associated with white matter alterations in the SSc were also explored. METHODS:SSc patients and healthy controls (HCs) were recruited and brain MR was performed. Clinical information, laboratory tests and scales were collected. Whole white matter and WMH volume were segmented and quantified. Independent t test, Mann-Whitney U test and the χ2 test were applied to determine the differences between groups. Clinical information and its association with WMH were investigated by logistic regression. RESULTS:Eighty-four SSc patients and 30 HCs were included. WMH was more prevalent and a significantly greater proportion of DWMH [2.28 × 10-4 (IQR 0.35 × 10-4, 9.68 × 10-4) vs 0.91 × 10-4 (IQR 0,3.31 × 10-4), P= 0.0339] was notified in SSc patients. Distinct WMH distribution patterns were noted between SSc subtypes, that PVH/WMH proportion corrected residual value was larger in dcSSc patients [0.06 (IQR -0.15 - 0.17) vs -0.16 (IQR -0.30-0.13), P= 0.0346]. No correlations were found between scales results and WMH volume ratios. CONCLUSION:These findings suggested that SSc patients are more likely to acquire WMH, particularly in the deep white matter regions. Comparisons also lend support to the hypothesis that the distribution patterns of WMH between dcSSc and lcSSc patients might be distinct.

Purpose:We have developed a bone-dedicated collimator with higher sensitivity but slightly degraded resolution on single-photon emission computed tomography (SPECT) for planar bone scintigraphy, compared with conventional low-energy high-resolution collimator. In this work, we investigated the feasibility of using the blind deconvolution algorithm to improve the resolution of planar images on bone scintigraphy. Materials and Methods:Monte Carlo simulation was performed with the NCAT phantom for modeling bone scintigraphy on the clinical dual-head SPECT scanner (Imagine NET 632, Beijing Novel Medical Equipment Ltd.) equipped with the bone-dedicated collimator. Maximum likelihood estimation method was used for the blind deconvolution algorithm. The initial estimation of point spread function (PSF) and iteration number for the method were determined by comparing the deblurred images obtained from different input parameters. We simulated different tumors in five different locations and with five different diameters to evaluate the robustness of the initial inputs. Furthermore, we performed chest phantom studies on the clinical SPECT scanner. The quantified increased contrast ratio (CR) between the tumor and the background was evaluated. Results:The 2 mm PSF kernel and 10 iterations provided a practical and robust deblurred image on our system. Those two inputs can generate robust deblurred images in terms of the tumor location and size with an average increased CR of 21.6%. The phantom studies also demonstrated the ability of blind deconvolution, using those two inputs, with increased CRs of 17%, 17%, 22%, 20%, and 13% for lesions with diameters of 1 cm, 2 cm, 3 cm, 4 cm, and 5 cm, respectively. Conclusions:It is feasible to use the blind deconvolution algorithm to deblur the planar images for SPECT bone scintigraphy. The appropriate values of the PSF kernel and the iteration number for the blind deconvolution can be determined using simulation studies.

OBJECTIVES:The characteristics of brain impairment in different subtypes of systemic sclerosis (SSc) (dcSSc, diffuse cutaneous SSc; lcSSc, limited cutaneous SSc) remain unclear. This study aimed to characterize cerebral structure and perfusion changes in different subtypes of SSc patients using magnetic resonance (MR) imaging. METHODS:Seventy SSc patients (46.0 ± 11.7 years, 62 females) and 30 healthy volunteers (44.8 ± 13.7 years, 24 females) were recruited and underwent brain MR imaging and Montreal Cognitive Assessment (MoCA) test. Gray matter (GM) volumes were measured using voxel-based morphometry analysis on T1-weighted images. Voxel-based and regional cerebral blood flow (CBF) was calculated on arterial spin labelling images. The cerebral structural and perfusion measurements by MR imaging were compared among dcSSc, lcSSc and healthy subjects using one-way ANOVA. The correlations between clinical characteristics and MR imaging measurements were also analysed. RESULTS:The dcSSc patients exhibited a significant reduction in GM volume in the para-hippocampal region (cluster P < 0.01, FWE corrected) compared with healthy volunteers. Whereas SSc patients, particularly lcSSc patients, showed elevated CBF in cerebellum, insula, cerebral cortex and subcortical structures (regional analyses: all P < 0.05; voxel-based analyses: cluster P < 0.01, FWE corrected). Furthermore, clinical characteristics of modified Rodnan skin score (mRSS) (r value ranged from -0.29 to -0.45), MoCA scores (r = 0.40) and anti-nuclear antibody (ANA) positivity (r = -0.33) were significantly associated with CBF in some regions (all P < 0.05). CONCLUSION:The manifestations of brain involvement vary among different subtypes of SSc. In addition, severe skin sclerosis may indicate higher risk of brain involvement in SSc patients.