Removal efficiency and abundance of nitrogen cycling microorganism in three bio-matrix materials to treat swine wastewater.

A bio-matrix material (BMM) system is used to pretreat swine wastewater and reduce the nitrogen (N) concentration to the tolerance range of plants in constructed wetlands. In this study, rice straw (RS), wheat straw (WS), and corn stalk (CS) were applied to treat pollutants from swine wastewater, respectively. This one year-long field experiment make up for the lack of long-term experiments and mechanistic investigations of BMM. The pollutant removal efficiency, degradation process of crop straw, and the abundance of nitrogen cycling genes were determined in different BMM systems. The results showed that the removal efficiency of COD, TN, NH4+, and NO3- was the best in the initial 6 months. Furthermore, RS and WS exhibited favorable annual removal efficiency of TN and NH4+, which were 32.81% and 32.99%, 35.3% and 34.97%, respectively. Moreover, the removal efficiency of COD was 30.81% in three BMM systems. Meanwhile, it was found that the dry matter (DM) degradation of crop straws was fast in the first 4-5 months. The degradation rates of cellulose, hemicellulose, and lignin were 94.19%, 94.36%, and 87.32%, respectively, in 1 year. The abundance of nitrogen cycling genes significantly increased by adding BMM, compared with CK (P < 0.05). This showed the abundance of the hzsB gene in RS was the highest, while nirK, nirS, AOA, and AOB were the highest in WS. The addition of RS and WS was better than that of CS in promoting the abundance of nitrogen cycling microorganisms. The results indicated that adding BMM could enhance the anaerobic ammonia oxidation, nitrification, and denitrification. This study not only extends our comprehension of BMM mechanisms in swine wastewater treatment but also serves as a guiding light for numerous farms in similar climate regions.

USFM: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis.

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundation models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.

Response of bacterial and micro-eukaryotic communities to spatio-temporal fluctuations of wastewater in full scale constructed wetlands.

How microbial communities respond to wastewater fluctuations is poorly understood. Full-scale surface flow constructed wetlands (SFCWs) were constructed for investigating microbial communities. Results showed that influent wastewater changed sediment bacterial community composition seasonally, indicating that a single bacterial taxonomic group had low resistance (especially, Actinobacteriota and Gammaproteobacteria). However, copy numbers of 16S rRNA, ammonia oxidizing archaea, ammonia oxidizing bacteria, nirS and nirK in the first stage SFCWs were 2.49 × 1010, 3.48 × 109, 5.76 × 106, 8.77 × 108 and 9.06 × 108 g-1 dry sediment, respectively, which remained stable between seasons. Moreover, decreases in the nitrogen concentration in wastewater, changed microbial system state from heterotrophic to autotrophic. Micro-eukaryotic communities were more sensitive to wastewater fluctuations than bacterial communities. Overall, results revealed that microbial communities responded to spatio-temporal fluctuations in wastewater through state changes and species asynchrony. This highlighted complex processes of wastewater treatment by microbial components in SFCWs.

HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation.

High performance of deep learning models on medical image segmentation greatly relies on large amount of pixel-wise annotated data, yet annotations are costly to collect. How to obtain high accuracy segmentation labels of medical images with limited cost (e.g. time) becomes an urgent problem. Active learning can reduce the annotation cost of image segmentation, but it faces three challenges: the cold start problem, an effective sample selection strategy for segmentation task and the burden of manual annotation. In this work, we propose a Hybrid Active Learning framework using Interactive Annotation (HAL-IA) for medical image segmentation, which reduces the annotation cost both in decreasing the amount of the annotated images and simplifying the annotation process. Specifically, we propose a novel hybrid sample selection strategy to select the most valuable samples for segmentation model performance improvement. This strategy combines pixel entropy, regional consistency and image diversity to ensure that the selected samples have high uncertainty and diversity. In addition, we propose a warm-start initialization strategy to build the initial annotated dataset to avoid the cold-start problem. To simplify the manual annotation process, we propose an interactive annotation module with suggested superpixels to obtain pixel-wise label with several clicks. We validate our proposed framework with extensive segmentation experiments on four medical image datasets. Experimental results showed that the proposed framework achieves high accuracy pixel-wise annotations and models with less labeled data and fewer interactions, outperforming other state-of-the-art methods. Our method can help physicians efficiently obtain accurate medical image segmentation results for clinical analysis and diagnosis.

GMRLNet: A Graph-Based Manifold Regularization Learning Framework for Placental Insufficiency Diagnosis on Incomplete Multimodal Ultrasound Data.

Multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) could greatly aid in the early diagnosis and interventional treatment of placental insufficiency (PI), ensuring a normal pregnancy. Existing multimodal analysis methods have weaknesses in multimodal feature representation and modal knowledge definitions and fail on incomplete datasets with unpaired multimodal samples. To address these challenges and efficiently leverage the incomplete multimodal dataset for accurate PI diagnosis, we propose a novel graph-based manifold regularization learning (MRL) framework named GMRLNet. It takes US and MFI images as input and exploits their modality-shared and modality-specific information for optimal multimodal feature representation. Specifically, a graph convolutional-based shared and specific transfer network (GSSTN) is designed to explore intra-modal feature associations, thus decoupling each modal input into interpretable shared and specific spaces. For unimodal knowledge definitions, graph-based manifold knowledge is introduced to describe the sample-level feature representation, local inter-sample relations, and global data distribution of each modality. Then, an MRL paradigm is designed for inter-modal manifold knowledge transfer to obtain effective cross-modal feature representations. Furthermore, MRL transfers the knowledge between both paired and unpaired data for robust learning on incomplete datasets. Experiments were conducted on two clinical datasets to validate the PI classification performance and generalization of GMRLNet. State-of-the-art comparisons show the higher accuracy of GMRLNet on incomplete datasets. Our method achieves 0.913 AUC and 0.904 balanced accuracy (bACC) for paired US and MFI images, as well as 0.906 AUC and 0.888 bACC for unimodal US images, illustrating its application potential in PI CAD systems.

3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation.

Objective. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences.Approach. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction.Main results. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%.Significance. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.

Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation.

The segmentation of the left ventricle endocardium (LVendo) and the left ventricle epicardium (LVepi) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiography segmentation. However, the performance of a well-trained segmentation network may degrade in unseen domain datasets due to the distribution shift of the data. Adaptation algorithms can improve the generalization of deep neural networks to different domains. In this paper, we present a multi-space adaptation-segmentation-joint framework, named MACS, for cross-domain echocardiography segmentation. It adopts a generative adversarial architecture; the generator fulfills the segmentation task and the multi-space discriminators align the two domains on both the feature space and output space. We evaluated the MACS method on two echocardiography datasets from different medical centers and vendors, the publicly available CAMUS dataset and our self-acquired dataset. The experimental results indicated that the MACS could handle unseen domain datasets well, without requirements for manual annotations, and improve the generalization performance by 2.2% in the Dice metric.

[An optimized segmentation of main vessel in coronary angiography images via removing the overlapping pacemaker].

Coronary angiography (CAG) as a typical imaging modality for the diagnosis of coronary diseases hasbeen widely employed in clinical practices. For CAG-based computer-aided diagnosis systems, accurate vessel segmentation plays a fundamental role. However, patients with bradycardia usually have a pacemaker which frequently interferes the vessel segmentation. In this case, the segmentation of vessels will be hard. To mitigate interferences of pacemakers and then extract main vessels more effectively in CAG images, we propose an approach. At first, a pseudo CAG (pCAG) image is generated through a part of a CAG sequence, in which the pacemaker exists. Then, a local feature descriptor is employed to register the relative location of pacemaker between the pCAG image and the target CAG image. Finally, combining the registration result and segmentation results of main vessels and pacemaker, interferences of pacemaker are removed and the segmentation of main vessels is improved. The proposed method is evaluated based on 11 CAG images with pacemakers acquired in clinical practices. An optimization ratio of the Dice coefficient is 12.04%, which demonstrates that our method can remove overlapping pacemakers and achieve the improvement of main vessel segmentation in CAG images.Our method can further become a helpful component in a CAG-based computer-aided diagnosis system, improving its diagnosis accuracy and efficiency.

[Annual Nitrogen Removal Efficiency and Change in Abundance of Nitrogen Cycling Microorganisms in Swine Wastewater Treated by Crop Straw Materials].

Based on previous research, using straw material to treat swine wastewater can effectively reduce the concentration of nitrogen (N); however, the annual N-removal efficiency and change in the abundance of N-cycling functional genes remain unclear. In this study, four treatments (wheat straw, rice straw, corn stalk, and CK) were set up, with the aim of studying the annual N-removal efficiency and change in the abundance of functional genes. Our results showed that:① the total nitrogen (TN) removal and NH4+-N removal efficiency were the best in the first six months and were significantly reduced in the following six months. In addition, the TN removal and NH4+-N efficiency in straw and wheat straw were better than those in corn straw. The TN-removal efficiency in straw and wheat straw were 32.81%±11.34% and 32.99%±9.60%, respectively. The NH4+-N removal efficiency in straw and wheat straw were 35.3%±13.23% and 34.97%±12.00%, respectively. ② The abundance of N-cycling functional genes significantly increased by the addition of straw materials, compared with that of the CK (P<0.05). The average abundances of nirK, nirS, and hzsB were 6.45×109 copies·L-1, 6.18×109 copies·L-1, and 2.31×109 copies·L-1, respectively. The average abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) were 6.12×1010 copies·L-1 and 4.93×109 copies·L-1, respectively. The average hzsB gene abundance was 2.31×109 copies·L-1. The average abundance of 16S rRNA in the treatment was 8.90×1010 copies·L-1. The abundances of hzsB and nirS genes in the straw and wheat straw were higher than those in the other treatment, indicating that the activities of anaerobic ammonia oxidation and denitrifying microorganisms were significantly increased by the addition of straw and wheat straw (P<0.05). In addition, the abundance of AOA and AOB genes were increased in wheat straw, suggesting that wheat straw could promote nitrification. The results provided data supporting the molecular mechanism of nitrogen removal in swine wastewater treatment with straw materials.

Multilevel structure-preserved GAN for domain adaptation in intravascular ultrasound analysis.

The poor generalizability of intravascular ultrasound (IVUS) analysis methods caused by the great diversity of IVUS datasets is hopefully addressed by the domain adaptation strategy. However, existing domain adaptation models underperform in intravascular structural preservation, because of the complex pathology and low contrast in IVUS images. Losing structural information during the domain adaptation would lead to inaccurate analyses of vascular states. In this paper, we propose a Multilevel Structure-Preserved Generative Adversarial Network (MSP-GAN) for transferring IVUS domains while maintaining intravascular structures. On the generator-discriminator baseline, the MSP-GAN integrates the transformer, contrastive restraint, and self-ensembling strategy, for effectively preserving structures in multi-levels, including global, local, and fine levels. For the global-level pathology maintenance, the generator explores long-range dependencies in IVUS images via an incorporated vision transformer. For the local-level anatomy consistency, a region-to-region correspondence is forced between the translated and source images via a superpixel-wise multiscale contrastive (SMC) constraint. For reducing distortions of fine-level structures, a self-ensembling mean teacher generates the pixel-wise pseudo-label and restricts the translated image via an uncertainty-aware teacher-student consistency (TSC) constraint. Experiments were conducted on 20 MHz and 40 MHz IVUS datasets from different medical centers. Ablation studies illustrate that each innovation contributes to intravascular structural preservation. Comparisons with representative domain adaptation models illustrate the superiority of the MSP-GAN in the structural preservation. Further comparisons with the state-of-the-art IVUS analysis accuracy demonstrate that the MSP-GAN is effective in enlarging the generalizability of diverse IVUS analysis methods and promoting accurate vessel and lumen segmentation and stenosis-related parameter quantification.

AwCPM-Net: A Collaborative Constraint GAN for 3D Coronary Artery Reconstruction in Intravascular Ultrasound Sequences.

3D coronary artery reconstruction (3D-CAR) in intravascular ultrasound (IVUS) sequences allows quantitative analyses of vessel properties. Existing methods treat two main tasks of the 3D-CAR separately, including the cardiac phase retrieval (CPR) and the membrane border extraction (MBE). They ignore the CPR-MBE connection that could achieve mutual promotions to both tasks. In this paper, we pioneer to achieve one-step 3D-CAR via a collaborative constraint generative adversarial network (GAN) named the AwCPM-Net. The AwCPM-Net consists of a dual-task collaborative generator and a dual-task constraint discriminator. The generator combines a self-supervised CPR branch with a semi-supervised MBE branch via a warming-up connection. The discriminator promotes dual-branch predictions simultaneously. The CPR branch requires no annotations and outputs inter-frame deformation fields used for identifying cardiac phases. Deformation fields are additionally constrained by the MBE branch and the discriminator. The MBE branch predicts membrane boundaries for each frame. Two aspects assist the semi-supervised segmentation: annotation augmentation by deformation fields of the CPR branch; information exploitation on unlabeled images enabled by GAN design. Trained and tested on an IVUS dataset acquired from atherosclerosis patients, the AwCPM-Net is effective in both CPR and MBE tasks, superior to state-of-the-art IVUS CPR or MBE methods. Hence, the AwCPM-Net reconstructs reliable 3D artery anatomy in the IVUS modality.

Extraction of media adventitia and luminal intima borders by reconstructing intravascular ultrasound image sequences with vascular structural continuity.

PURPOSE: Most published methods directly achieve vessel membrane border detection on cross-sectional intravascular ultrasound (IVUS) images. The vascular structural continuity that exists in entire IVUS image sequences has been overlooked. However, this continuity can have a helpful role in the delineation of vessel membrane contours. To achieve the vessel membrane segmentation more effectively through employing this continuity, a strategy, referred to as multiangle reconstruction, segmentation, and recovery (RSR), is proposed in this paper. METHODS: Four main steps are contained in the multiangle-RSR: first, a combination of sampling and interpolation is employed to reconstruct long-axis-model IVUS frames, in which continuity information becomes available. Second, a clustering algorithm is conducted on long-axis-model IVUS frames to roughly extract the media-adventitia (MA) and lumen-intima (LI) boundaries. Third, the segmentation results of cross-sectional IVUS frames are recovered based on the rough results of long-axis-model IVUS frames, and an optimization process that combines downsampling, fitting and smoothing is designed to reduce the interference of bifurcation and side vessels. RESULTS: Multiangle-RSR is tested on a public dataset, and the Hausdorff distance (HD), Jaccard measure (JM), and percentage of area difference (PAD) are utilized as quantitative evaluation metrics. Mean HDs of 0.34 and 0.29 mm are obtained for MA border detection and LI border detection, respectively, which decrease by 43.3% and 9.4%, respectively, compared with their counterparts in previously published approaches. Furthermore, the mean JM is 0.87 for both MA border detection and LI border detection. The mean PADs of the MA contour extraction and the LI contour extraction are 0.10 and 0.11, respectively. CONCLUSION: The results indicate that the proposed strategy effectively introduces vascular structural continuity by reconstructing long-axis-model IVUS frames and achieves more precise extraction of MA and LI borders.

Extracting Membrane Borders in IVUS Images Using a Multi-Scale Feature Aggregated U-Net.

Automatic extraction of the lumen-intima border (LIB) and the media-adventitia border (MAB) in intravascular ultrasound (IVUS) images is of high clinical interest. Despite the superior performance achieved by deep neural networks (DNNs) on various medical image segmentation tasks, there are few applications to IVUS images. The complicated pathological presentation and the lack of enough annotation in IVUS datasets make the learning process challenging. Several existing networks designed for IVUS segmentation train two groups of weights to detect the MAB and LIB separately. In this paper, we propose a multi-scale feature aggregated U-Net (MFAU-Net) to extract two membrane borders simultaneously. The MFAU-Net integrates multi-scale inputs, the deep supervision, and a bi-directional convolutional long short-term memory (BConvLSTM) unit. It is designed to sufficiently learn features from complicated IVUS images through a small number of training samples. Trained and tested on the publicly available IVUS datasets, the MFAU-Net achieves both 0.90 Jaccard measure (JM) for the MAB and LIB detection on 20 MHz dataset. The corresponding metrics on 40 MHz dataset are 0.85 and 0.84 JM respectively. Comparative evaluations with state-of-the-art published results demonstrate the competitiveness of the proposed MFAU-Net.

Vessel membrane segmentation and calcification location in intravascular ultrasound images using a region detector and an effective selection strategy.

BACKGROUND AND OBJECTIVE: Segmenting vessel membranes and locating the calcific region in intravascular ultrasound (IVUS) images aid physicians in the diagnosis of atherosclerosis. However, the manual extraction of the media adventitia (MA)/lumen border and calcification location are cumbersome due to the excessive number of IVUS frames. Moreover, most existing (semi-)automatic detection methods cannot achieve both vessel membrane extraction and calcification location simultaneously, and they are unable to detect vessel membranes in IVUS frames from different acquisition systems. METHOD: A fully automatic approach is proposed based on extremal regions and a flexible selection strategy to extract vessel membranes in different IVUS frames and locate the calcific region in high-frequency ones. Three main steps are included in the algorithm. First, a region detector is employed to extract extremal regions from an IVUS image. Then, according to the selection strategy, a part of the extracted regions is selected. At the same time, the calcification is located according to its special acoustic properties. Next, approximate MA and lumen border segmentation is achieved based on the selected extremal regions and the located calcification in polar coordinates. Finally, the final segmentation results are obtained by smoothing the approximate values. RESULT: To demonstrate the feasibility of the method, it was evaluated based on a standard public dataset. Furthermore, to quantitatively evaluate the segmentation performance, the Hausdorff distance (HD), Jaccard measure (JM) and percentage of area difference (PAD) were used. The results show that a mean HD of 1.13/1.21 mm, a mean JM of 0.83/0.77 and a mean PAD of 0.11/0.23 are achieved for MA/lumen border detection in 77 40-MHz IVUS images. For MA/lumen border extraction in 435 20-MHz IVUS frames, the average HD, JM and PAD values are 0.47/0.28 mm, 0.84/0.89 and 0.13/0.10, respectively. In addition, the approach successfully achieves calcification location in 40-MHz IVUS frames. In comparison with other published methods, the method proposed in this study is competitive. CONCLUSION: According to these results, our strategy can extract MA/lumen borders in different IVUS frames and effectively locate calcification in high-frequency IVUS frames.

[Treatment of Highly Concentrated Swine Wastewater and Its Degradation Processes Using Three Matrix Materials].

The direct discharge of wastewater from pig farms can restrict wetland plant growth meaning that constructed wetlands (CWs) have generally low treatment efficiency. The treatment of farming wastewater using pre-ecological treatment technologies can be used to ensure that effluent concentrations reach the tolerable limits of wetland plants. This study focused on the efficient use of crop straw for reducing the pollution load of swine wastewater. Using field-scale pilot tests, wheat straw, straw, and corn stalk were used as test biological matrix pool fillers to treat the farming wastewater. Continuous intake of wastewater and a hydraulic retention time of 7 days was adopted. When the average concentrations of COD, TN, NH4+-N, NO3--N, and TP in the influent were 1652.83 mg·L-1, 371.31 mg·L-1, 303.51 mg·L-1, 0.67 mg·L-1, and 65.22 mg·L-1, respectively, wheat straw had the greatest removal effect on COD, TN, and TP, achieving a removal rate of 32.1%, 40.9%, and 33.3%, respectively. The removal efficiency of straw on NH4+-N was highest, reaching 43.4%. After 180 days, the lignin, cellulose, and hemicellulose of the three matrix materials were not completely decomposed. The degradation rate of lignin was lower than for cellulose and hemicellulose; the degradation of lignin and cellulose in the straw was fastest; and the degradation hemicellulose in wheat straw was fastest. The results show that wheat straw and straw offer a higher efficiency treatment for swine wastewater than corn stalk, and the suggested replacement cycle period is five months. These results provide initial data to support the application of biological matrix materials in the treatment of swine wastewater.

IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution.

The detection of the lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images is crucial for quantifying plaque burdens. The challenge of the segmentation work mainly roots in various artifacts in the image. Most of the published methods involve the establishment of complex models but do not behave well on images with artifacts. In this study, aiming at automatically delineating borders in IVUS frames acquired by 20 MHz ultrasound probes, we present a fuzzy clustering-initialized hierarchical level set evolution (FC-HLSE) method. A cluster selection strategy based on the spatial fuzzy c-means (FCM) is proposed to generate the initial value and regularization term of the level set evolution (LSE). The contour convergence splits into two LSE steps between which an ingenious contour extraction (consisting of the morphological processing, the seek and linear interpolation, the gradient-based and circular fitting-based refinement) is carried out. We evaluate the proposed methodology on the publicly available 435 images by comparing auto-segmented results with the ground truth. The performance of the method is quantified using the Jaccard measure (JM), the Hausdorff distance (HD), the percentage of area difference (PAD), the linear regression and Bland-Altman analysis. Results reveal that our method can handle images with or without artifacts. The algorithm is able to extract the lumen/MA border with the JM of 0.90/0.89, the HD of 0.31/0.40 mm, the PAD of 0.07/0.08 in average, which is better in some cases compared with several state-of-the-art methods.

Increased 131I Accumulation in the Gallbladder and High-grade Serous Ovarian Adenocarcinoma.

It is unusual to observe I accumulation in the gallbladder and high-grade serous ovarian adenocarcinoma during posttherapeutic I scan. We report the case of a 55-year-old woman with papillary thyroid cancer, who received total thyroidectomy and then 3 courses of I therapy. The posttherapeutic whole-body scan after the third dose of I therapy revealed abnormal I uptake in the right upper abdomen, overlapping the liver, and the pelvis. Further SPECT/CT imaging found that they were from an enlarged gallbladder and a large pelvic complex solid and cystic mass, which was pathologically confirmed as bilateral high-grade serous ovarian adenocarcinoma.