Metal implant segmentation in CT images based on diffusion model.

BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

[Research on Position Verification of Multi-Leaf Collimator (MLC) and Dose Verification Based on Electronic Portal Imaging Device].

Objective: A quality control (QC) system based on the electronic portal imaging device (EPID) system was used to realize the Multi-Leaf Collimator (MLC) position verification and dose verification functions on Primus and VenusX accelerators. Methods: The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation. The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation. The dose data obtained by EPID and two-dimensional matrix (MapCheck/PTW) were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis. Results: The position error of VenusX MLC was less than 1 mm. The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID. For the dose reconstructed by EPID, the average γ passing rates of Primus were 98.86% and 91.39% under the criteria of 3%/3 mm, 10% threshold and 2%/2 mm, 10% threshold, respectively. The average γ passing rates of VenusX were 98.49% and 91.11%, respectively. Conclusion: The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Augmented reality-guided positioning system for radiotherapy patients.

In modern radiotherapy, error reduction in the patients' daily setup error is important for achieving accuracy. In our study, we proposed a new approach for the development of an assist system for the radiotherapy position setup by using augmented reality (AR). We aimed to improve the accuracy of the position setup of patients undergoing radiotherapy and to evaluate the error of the position setup of patients who were diagnosed with head and neck cancer, and that of patients diagnosed with chest and abdomen cancer. We acquired the patient's simulation CT data for the three-dimensional (3D) reconstruction of the external surface and organs. The AR tracking software detected the calibration module and loaded the 3D virtual model. The calibration module was aligned with the Linac isocenter by using room lasers. And then aligned the virtual cube with the calibration module to complete the calibration of the 3D virtual model and Linac isocenter. Then, the patient position setup was carried out, and point cloud registration was performed between the patient and the 3D virtual model, such the patient's posture was consistent with the 3D virtual model. Twenty patients diagnosed with head and neck cancer and 20 patients diagnosed with chest and abdomen cancer in the supine position setup were analyzed for the residual errors of the conventional laser and AR-guided position setup. Results show that for patients diagnosed with head and neck cancer, the difference between the two positioning methods was not statistically significant (P > 0.05). For patients diagnosed with chest and abdomen cancer, the residual errors of the two positioning methods in the superior and inferior direction and anterior and posterior direction were statistically significant (t = -5.80, -4.98, P < 0.05). The residual errors in the three rotation directions were statistically significant (t = -2.29 to -3.22, P < 0.05). The experimental results showed that the AR technology can effectively assist in the position setup of patients undergoing radiotherapy, significantly reduce the position setup errors in patients diagnosed with chest and abdomen cancer, and improve the accuracy of radiotherapy.

Optogenetic Inhibition of Medial Prefrontal Cortex-Pontine Nuclei Projections During the Stimulus-free Trace Interval Impairs Temporal Associative Motor Learning.

The medial prefrontal cortex (mPFC) is closely involved in many higher-order cognitive functions, including learning to associate temporally discontiguous events (called temporal associative learning). However, direct evidence for the role of mPFC and the neural pathway underlying modulation of temporal associative motor learning is sparse. Here, we show that optogenetic inhibition of the mPFC or its axon terminals at the pontine nuclei (PN) during trace intervals or whole trial period significantly impaired the trace eyeblink conditioning (TEC), but had no significant effects on TEC during the conditioned stimulus or intertrial interval period. Our results suggest that activities associated with the mPFC-PN projection during trace intervals is crucial for trace associative motor learning. This finding is of great importance in understanding the mechanisms and the relevant neural pathways underlying mPFC modulation of temporal associative motor learning.

Medial Prefrontal Cortex-Pontine Nuclei Projections Modulate Suboptimal Cue-Induced Associative Motor Learning.

Diverse and powerful mechanisms have evolved to enable organisms to modulate learning and memory under a variety of survival conditions. Cumulative evidence has shown that the prefrontal cortex (PFC) is closely involved in many higher-order cognitive functions. However, when and how the medial PFC (mPFC) modulates associative motor learning remains largely unknown. Here, we show that delay eyeblink conditioning (DEC) with the weak conditioned stimulus (wCS) but not the strong CS (sCS) elicited a significant increase in the levels of c-Fos expression in caudal mPFC. Both optogenetic inhibition and activation of the bilateral caudal mPFC, or its axon terminals at the pontine nucleus (PN) contralateral to the training eye, significantly impaired the acquisition, recent and remote retrieval of DEC with the wCS but not the sCS. However, direct optogenetic activation of the contralateral PN had no significant effect on the acquisition, recent and remote retrieval of DEC. These results are of great importance in understanding the elusive role of the mPFC and its projection to PN in subserving the associative motor learning under suboptimal learning cue.

Prepulse inhibition and acoustic startle response in young healthy Chinese.

Prepulse inhibition (PPI) and habituation of the acoustic startle response (ASR) are considered to be effective neurobiological measures of sensorimotor gating and information processing. The deficit of PPI and habituation of ASR has been proposed to be candidate endophenotypes of schizophrenia spectrum disorders. However, there has been little information on PPI and ASR measures in Chinese. The present study aimed to provide more information about the characteristics of PPI and ASR in young healthy Chinese and investigate their sensitivity to experimental parameters and characteristics of population. In this study, we examined the PPI and habituation of ASR in 41 young healthy adults (21 males and 20 females), using an acoustic startle stimulus of 115 dB and a prepulse of 75 dB at a lead interval (LI) of 60 ms and 120 ms, respectively. The behavioral performance demonstrated that the PPI and habituation of ASR in all the young participants were robust. The significant difference was not observed in PPI and habituation between male and female. The block effect on PPI was significant; PPI reduces with increasing training. Latency facilitation was observed under prepulse conditions, with a significant effect of LI. Compared to previous studies in Caucasians, Chinese in this study shows a higher habituation and PPI. In conclusion, this research provides more data of behavioral characteristics of PPI and ASR in young healthy Chinese. Chinese in this study shows a higher habituation and PPI than Caucasians in previous studies.

[Metal artifact reduction for dental implants in kilovoltage computed tomography using megavoltage cone-beam computer tomography].

This paper proposes a novel metal artifact reduction (MAR) algorithm for dental implants in kilovoltage computed tomography (kVCT) using megavoltage cone-beam computer tomography (MVCBCT). Firstly, two CT images were derived by scanning patient with dental implants using kVCT and MVCBCT. Metal image was derived by thresholding segmentation in kVCT. MVCBCT and kVCT images were fused to generate prior image which was forward projected to get surrogate sinogram of metal trace. The corrected image was generated by filtered backprojection (FBP) reconstruction in corrected sinogram. The results of proposed algorithm were compared with other frequently-used metal artifact reduction algorithm, such as normalized MAR (NMAR), normalized MAR using MVCBCT prior images (NMAR-MV), and linear interpolation MAR (LIMAR). The normalized root mean square deviation (NRMSD) and mean absolute deviation (MAD) were computed. The experiment showed that the proposed method removed serious metal artifacts without introducing new artifacts. The values of NRMSD and MAD for proposed method were the minimum in all methods. The values of NRMSD for NMAR, NMAR-MV, LIMAR and the proposed method were 21.0%, 22.1%, 41.9% and 17.0% respectively. And MAD values of them were 232, 235, 553, 205 HU, respectively. In conclusion, the proposed metal artifact reduction algorithm can successfully suppress metal artifacts for dental implants, and greatly improve the quality of CT image.

Prefrontal control of cerebellum-dependent associative motor learning.

Behavioral studies have demonstrated that both medial prefrontal cortex (mPFC) and cerebellum play critical roles in trace eyeblink conditioning. However, little is known regarding the mechanism by which the two brain regions interact. By use of electrical stimulation of the caudal mPFC as a conditioned stimulus, we show evidence that persistent outputs from the mPFC to cerebellum are necessary and sufficient for the acquisition and expression of a trace conditioned response (CR)-like response. Specifically, the persistent outputs of caudal mPFC are relayed to the cerebellum via the rostral part of lateral pontine nuclei. Moreover, interfering with persistent activity by blockade of the muscarinic Ach receptor in the caudal mPFC impairs the expression of learned trace CRs. These results suggest an important way for the caudal mPFC to interact with the cerebellum during associative motor learning.

DiffRecon: Diffusion-based CT reconstruction with cross-modal deformable fusion for DR-guided non-coplanar radiotherapy.

In non-coplanar radiotherapy, DR is commonly used for image guiding which needs to fuse intraoperative DR with preoperative CT. But this fusion task performs poorly, suffering from unaligned and dimensional differences between DR and CT. CT reconstruction estimated from DR could facilitate this challenge. Thus, We propose a unified generation and registration framework, named DiffRecon, for intraoperative CT reconstruction based on DR using the diffusion model. Specifically, we use the generation model for synthesizing intraoperative CTs to eliminate dimensional differences and the registration model for aligning synthetic CTs to improve reconstruction. To ensure clinical usability, CT is not only estimated from DR but the preoperative CT is also introduced as prior. We design a dual-encoder to learn prior knowledge and spatial deformation among pre- and intra-operative CT pairs and DR parallelly for 2D/3D feature deformable conversion. To calibrate the cross-modal fusion, we insert cross-attention modules to enhance the 2D/3D feature interaction between dual encoders. DiffRecon has been evaluated by both image quality metrics and dosimetric indicators. The high image synthesis metrics are with RMSE of 0.02±0.01, PSNR of 44.92±3.26, and SSIM of 0.994±0.003. The mean gamma passing rates between rCT and sCT for 1%/1 mm, 2%/2 mm and 3%/3 mm acceptance criteria are 95.2%, 99.4% and 99.9% respectively. The proposed DiffRecon can reconstruct CT accurately from a single DR projection with excellent image generation quality and dosimetric accuracy. These demonstrate that the method can be applied in non-coplanar adaptive radiotherapy workflows.

GAN-based metal artifacts region inpainting in brain MRI imaging with reflective registration.

BACKGROUND AND OBJECTIVE: Metallic magnetic resonance imaging (MRI) implants can introduce magnetic field distortions, resulting in image distortion, such as bulk shifts and signal-loss artifacts. Metal Artifacts Region Inpainting Network (MARINet), using the symmetry of brain MRI images, has been developed to generate normal MRI images in the image domain and improve image quality. METHODS: T1-weighted MRI images containing or located near the teeth of 100 patients were collected. A total of 9000 slices were obtained after data augmentation. Then, MARINet based on U-Net with a dual-path encoder was employed to inpaint the artifacts in MRI images. The input of MARINet contains the original image and the flipped registered image, with partial convolution used concurrently. Subsequently, we compared PConv with partial convolution, and GConv with gated convolution, SDEdit using a diffusion model for inpainting the artifact region of MRI images. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. In addition, the artifact masks of clinical MRI images were inpainted by physicians. RESULTS: MARINet could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the test results of PConv, GConv, SDEdit, and MARINet, the masked MAEs were 0.1938, 0.1904, 0.1876, and 0.1834, respectively, and the masked PSNRs were 17.39, 17.40, 17.49, and 17.60 dB, respectively. The visualization results also suggest that the network can recover the tissue texture, alveolar shape, and tooth contour. Additionally, for clinical artifact MRI images, MARINet completed the artifact region inpainting task more effectively when compared with other models. CONCLUSIONS: By leveraging the quasi-symmetry of brain MRI images, MARINet can directly and effectively inpaint the metal artifacts in MRI images in the image domain, restoring the tooth contour and detail, thereby enhancing the image quality.

Prelimbic cortex-nucleus accumbens core projection positively regulates itch and itch-related aversion.

Itch is one of the most common clinical symptoms in patients with diseases of the skin, liver, or kidney, and it strongly triggers aversive emotion and scratching behavior. Previous studies have confirmed the role of the prelimbic cortex (Prl) and the nucleus accumbens core (NAcC), which are reward and motivation regulatory centers, in the regulation of itch. However, it is currently unclear whether the Prl-NAcC projection, an important pathway connecting these two brain regions, is involved in the regulation of itch and its associated negative emotions. In this study, rat models of acute neck and cheek itch were established by subcutaneous injection of 5-HT, compound 48/80, or chloroquine. Immunofluorescence experiments determined that the number of c-Fos-immunopositive neurons in the Prl increased during acute itch. Chemogenetic inhibition of Prl glutamatergic neurons or Prl-NAcC glutamatergic projections can inhibit both histaminergic and nonhistaminergic itch-scratching behaviors and rectify the itch-related conditioned place aversion (CPA) behavior associated with nonhistaminergic itch. The Prl-NAcC projection may play an important role in the positive regulation of itch-scratching behavior by mediating the negative emotions related to itch.

Positive feedback of efferent copy via pontine nucleus facilitates cerebellum-mediated associative learning.

The cerebellum is critical for motor coordination and learning. However, the role of feedback circuitry in this brain region has not been fully explored. Here, we characterize a nucleo-ponto-cortical feedback pathway in classical delayed eyeblink conditioning (dEBC) of rats. We find that the efference copy is conveyed from the interposed cerebellar nucleus (Int) to cerebellar cortex through pontine nucleus (PN). Inhibiting or exciting the projection from the Int to the PN can decelerate or speed up acquisition of dEBC, respectively. Importantly, we identify two subpopulations of PN neurons (PN1 and PN2) that convey and integrate the feedback signals with feedforward sensory signals. We also show that the feedforward and feedback pathways via different types of PN neurons contribute to the plastic changes and cooperate synergistically to the learning of dEBC. Our results suggest that this excitatory nucleo-ponto-cortical feedback plays a significant role in modulating associative motor learning in cerebellum.

A transformer-based dual-domain network for reconstructing FOV extended cone-beam CT images from truncated sinograms in radiation therapy.

BACKGROUND AND OBJECTIVE: Cone-beam computed tomography (CBCT) is widely used in clinical radiotherapy, but its small field of view (sFOV) limits its application potential. In this study, a transformer-based dual-domain network (dual_swin), which combined image domain restoration and sinogram domain restoration, was proposed for the reconstruction of complete CBCT images with extended FOV from truncated sinograms. METHODS: The planning CT images with large FOV (LFOV) of 330 patients who received radiation therapy were collected. The synthetic CBCT (sCBCT) images with LFOV were generated from CT images by the trained cycleGAN network, and CBCT images with sFOV were obtained through forward projection, projection truncation, and filtered back projection (FBP), comprising the training and test data. The proposed dual_swin includes sinogram domain restoration, image domain restoration, and FBP layer, and the swin transformer blocks were used as the basic feature extraction module in the network to improve the global feature extraction ability. The proposed dual_swin was compared with the image domain method, the sinogram domain method, the U-Net based dual domain network (dual_Unet), and the traditional iterative reconstruction method based on prior image and conjugate gradient least-squares (CGLS) in the test of sCBCT images and clinical CBCT images. The HU accuracy and body contour accuracy of the predicted images by each method were evaluated. RESULTS: The images generated using the CGLS method were fuzzy and obtained the lowest structural similarity (SSIM) among all methods in the test of sCBCT and clinical CBCT images. The predicted images by the image domain methods are quite different from the ground truth and have low accuracy on HU value and body contour. In comparison with image domain methods, sinogram domain methods improved the accuracy of HU value and body contour but introduced secondary artifacts and distorted bone tissue. The proposed dual_swin achieved the highest HU and contour accuracy with mean absolute error (MAE) of 23.0 HU, SSIM of 95.7%, dice similarity coefficient (DSC) of 99.6%, and Hausdorff distance (HD) of 4.1 mm in the test of sCBCT images. In the test of clinical patients, images that were predicted by dual_swin yielded MAE, SSIM, DSC, and HD of 38.2 HU, 91.7%, 99.0%, and 5.4 mm, respectively. The predicted images by the proposed dual_swin has significantly higher accuracy than the other methods (P < 0.05). CONCLUSIONS: The proposed dual_swin can accurately reconstruct FOV extended CBCT images from the truncated sinogram to improve the application potential of CBCT images in radiotherapy.

Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy.

BACKGROUND: Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT-based ART. PURPOSE: To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high-quality sCT images from CBCT and achieve an accurate dose calculation. METHODS: The proposed SARN was trained in a self-supervised manner. Artifact-CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact-CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U-Net, cycleGAN, attention-gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. RESULTS: The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIUm ) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U-Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively. CONCLUSIONS: The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high-quality sCT images with fewer artifacts, high-accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.

Classification for thyroid nodule using ViT with contrastive learning in ultrasound images.

The lack of representative features between benign nodules, especially level 3 of Thyroid Imaging Reporting and Data System (TI-RADS), and malignant nodules limits diagnostic accuracy, leading to inconsistent interpretation, overdiagnosis, and unnecessary biopsies. We propose a Vision-Transformer-based (ViT) thyroid nodule classification model using contrast learning, called TC-ViT, to improve accuracy of diagnosis and specificity of biopsy recommendations. ViT can explore the global features of thyroid nodules well. Nodule images are used as ROI to enhance the local features of the ViT. Contrast learning can minimize the representation distance between nodules of the same category, enhance the representation consistency of global and local features, and achieve accurate diagnosis of TI-RADS 3 or malignant nodules. The test results achieve an accuracy of 86.9%. The evaluation metrics show that the network outperforms other classical deep learning-based networks in terms of classification performance. TC-ViT can achieve automatic classification of TI-RADS 3 and malignant nodules on ultrasound images. It can also be used as a key step in computer-aided diagnosis for comprehensive analysis and accurate diagnosis. The code will be available at https://github.com/Jiawei217/TC-ViT.

New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer.

OBJECTIVE: A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer. METHODS: The low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing. All patients were fixed in the same posture with a vacuum pad. The CBCT images were scanned under the Fast Chest M20 protocol with a 50% reduction in projection frames compared with the standard Chest M20 protocol. Rigid registration was performed between pCT and CBCT, and deformation registration was performed between rCT and CBCT. In the training stage of the TCBCTNet, truncated CBCT images obtained from complete CBCT images by simulation were used. The input of the CBCT→CT generator was truncated CBCT and pCT, and TCBCTNet was applied to patients with breast cancer after training. The accuracy of the sCT was evaluated by anatomy and dosimetry and compared with the generative adversarial network with UNet and ResNet as the generators (named as UnetGAN, ResGAN). RESULTS: The three models could improve the image quality of CBCT and reduce the scattering artifacts while preserving the anatomical geometry of CBCT. For the chest test set, TCBCTNet achieved the best mean absolute error (MAE, 21.18±3.76 HU), better than 23.06±3.90 HU in UnetGAN and 22.47±3.57 HU in ResGAN. When applied to patients with breast cancer, TCBCTNet performance decreased, and MAE was 25.34±6.09 HU. Compared with rCT, sCT by TCBCTNet showed consistent dose distribution and subtle absolute dose differences between the target and the organ at risk. The 3D gamma pass rates were 98.98%±0.64% and 99.69%±0.22% at 2 mm/2% and 3 mm/3%, respectively. Ablation experiments confirmed that pCT and content loss played important roles in TCBCTNet. CONCLUSIONS: High-quality sCT images could be synthesized from truncated low-dose CBCT and pCT by using the proposed TCBCTNet model. In addition, sCT could be used to accurately calculate the dose distribution for patients with breast cancer.

Inpainting truncated areas of CT images based on generative adversarial networks with gated convolution for radiotherapy.

This study aimed to inpaint the truncated areas of CT images by using generative adversarial networks with gated convolution (GatedConv) and apply these images to dose calculations in radiotherapy. CT images were collected from 100 patients with esophageal cancer under thermoplastic membrane placement, and 85 cases were used for training based on randomly generated circle masks. In the prediction stage, 15 cases of data were used to evaluate the accuracy of the inpainted CT in anatomy and dosimetry based on the mask with a truncated volume covering 40% of the arm volume, and they were compared with the inpainted CT synthesized by U-Net, pix2pix, and PConv with partial convolution. The results showed that GatedConv could directly and effectively inpaint incomplete CT images in the image domain. For the results of U-Net, pix2pix, PConv, and GatedConv, the mean absolute errors for the truncated tissue were 195.54, 196.20, 190.40, and 158.45 HU, respectively. The mean dose of the planning target volume, heart, and lung in the truncated CT was statistically different (p < 0.05) from those of the ground truth CT ([Formula: see text]). The differences in dose distribution between the inpainted CT obtained by the four models and [Formula: see text] were minimal. The inpainting effect of clinical truncated CT images based on GatedConv showed better stability compared with the other models. GatedConv can effectively inpaint the truncated areas with high image quality, and it is closer to [Formula: see text] in terms of image visualization and dosimetry than other inpainting models.

The Anterior Insula and its Projection to the Prelimbic Cortex are Involved in the Regulation of 5-HT-Induced Itch.

Itch is an unpleasant sensation that urges people and animals to scratch. Neuroimaging studies on itch have yielded extensive correlations with diverse cortical and subcortical regions, including the insular lobe. However, the role and functional specificity of the insular cortex (IC) and its subdivisions in itch mediation remains unclear. Here, we demonstrated by immunohistochemistry and fiber photometry tests, that neurons in both the anterior insular cortex (AIC) and the posterior insular cortex (PIC) are activated during acute itch processes. Pharmacogenetic experiments revealed that nonselective inhibition of global AIC neurons, or selective inhibition of the activity of glutaminergic neurons in the AIC, reduced the scratching behaviors induced by intradermal injection of 5-hydroxytryptamine (5-HT), but not those induced by compound 48/80. However, both nonselective inhibition of global PIC neurons and selective inhibition of glutaminergic neurons in the PIC failed to affect the itching-scratching behaviors induced by either 5-HT or compound 48/80. In addition, pharmacogenetic inhibition of AIC glutaminergic neurons effectively blocked itch-associated conditioned place aversion behavior, and inhibition of AIC glutaminergic neurons projecting to the prelimbic cortex significantly suppressed 5-HT-evoked scratching. These findings provide preliminary evidence that the AIC is involved, at least partially via aversive emotion mediation, in the regulation of 5-HT-, but not compound 48/80-induced itch.

Dysgranular retrosplenial cortex modulates histaminergic and nonhistaminergic itch processing.

Itch is an unpleasant sensation followed by an intense desire to scratch. Previous researches have advanced our understanding about the role of anterior cingulate cortex and prelimbic cortex in itch modulation, whereas little is known about the effects of retrosplenial cortex (RSC) during this process. Here we firstly confirmed that the neuronal activity of dysgranular RSC (RSCd) is significantly elevated during itch-scratching processing through c-Fos immunohistochemistry and fiber photometry recording. Then with designer receptors exclusively activated by designer drugs approaches, we found that pharmacogenetic inhibition of global RSCd neurons attenuated the number of scratching bouts as well as the cumulative duration of scratching bouts elicited by both 5-HT or compound 48/80 injection into rats' nape or cheek; selective inhibition of the pyramidal neurons in RSCd, or of the excitatory projections from caudal anterior cingulate cortex (cACC) to RSCd, demonstrated the similar effects of decreasing itch-related scratching induced by both 5-HT or compound 48/80. Pharmacogenetic intervention of the neuronal or circuitry activities did not affect rats' motor ability. This study presents direct evidence that pyramidal neurons in RSCd, and the excitatory projection from cACC to RSCd are critically involved in central regulation of both histaminergic and nonhistaminergic itch.

The prelimbic cortex regulates itch processing by controlling attentional bias.

Itch is a complex and unpleasant sensory experience. Recent studies have begun to investigate the neural mechanisms underlying the modulation of sensory and emotional components of itch in the brain. However, the key brain regions and neural mechanism involved in modulating the attentional processing of itch remain elusive. Here, we showed that the prelimbic cortex (PrL) is associated with itch processing and that the manipulation of itch-responsive neurons in the PrL significantly disrupted itch-induced scratching. Interestingly, we found that increasing attentional bias toward a distracting stimulus could disturb itch processing. We also demonstrated the existence of a population of attention-related neurons in the PrL that drive attentional bias to regulate itch processing. Importantly, itch-responsive neurons and attention-related neurons significantly overlapped in the PrL and were mutually interchangeable in the regulation of itch processing at the cellular activity level. Our results revealed that the PrL regulates itch processing by controlling attentional bias.