The automatic segmentation of anterior mediastinal lesions in enhanced CT imaging is of significant importance in clinical diagnostics. Anterior mediastinal lesions are characterized by various types and blurred boundary, which increases the difficulty of anterior mediastinal lesions segmentation. This study leverages the robust zero-shot classification capability and semantic expression of CLIP to formulate CLIP-prompt that express the semantic correlation between images and text, so that CLIP-prompt guided cross-attention has been proposed. By integrating the CLIP-prompt into the image features through cross-attention, the network can focus more intently on the lesion areas. Additionally, to better capture the unknown categorical features of the images, this paper introduces a learnable image prompt that works in conjunction with an attention module integrated with textual information, thereby enhancing the constraints on the segmentation targets. Finally, to address the blurred boundary of the anterior mediastinal lesion, this study proposes a boundary-enhanced loss. By augmenting the weights of difficult-to-segment edge points, the network is enabled to focus on these challenging boundary areas, consequently improving the segmentation accuracy of these points. Compared to existing state-of-the-art methods, our approach has achieved an overall Dice coefficient of 89.43% and has achieved good performance in terms of ASSD metric for segmentation edges.

3GPP has completed the standardization of 5G NR positioning in Release 16 (Rel-16) and Rel-17, and the Rel-18 study on expanded and improved positioning was finished in November, 2022. The standard solutions enable several positioning techniques in efforts to improve accuracy and power efficiency, and to decrease the latency. RAT-independent positioning techniques have also been standardized to support user equipment positioning via satellites and sensors. This article provides the latest progress on 3GPP 5G positioning including requirements, use cases, and the newly supported positioning techniques in Release-17 and Release-18. Finally, future challenges are discussed.

3GPP is paving the way to enhance the 5G NR positioning for general use cases as well as vertical industry scenarios, which attracts wide research from industry and academy. Among the varied requirements the most important one is to reduce the power consumption of the positioning device while keeping high accuracy in order to dominate the increasing market of positioning. In this paper, we propose a LPHAP solution under the 3GPP NR standard framework. For reduced power consumption, a new category or capability for positioning device is defined where RRC_INACTIVE state positioning with user-centric positioning area. For accuracy enhancement, we propose to improve TOA measurement by stitching the SRSs in multiple bands and analyze the Cramer-Rao Bound of TOA estimation. Uplink time difference of arrival (UL-TDOA) with expected angle-of-arrival is investigated to enhance TDOA measurements. Performance evaluations show that the device can reach the accuracy of 0.3m @90 % and the device battery life can be extended 20 times compared to Rel-17 UE.

This paper presents a new medical image segmentation method by using wavelet frame energy distribution, which is the sum of squares of the wavelet frame coefficients at each pixel. This work shows that the wavelet frame energy distribution contains the fine texture information extracted from images with low intensity contrast and complex structures using wavelet frame transform. Thus it is employed to enhance the segmentation quality under some challenge conditions such as low intensity contrast, weak/ambiguous boundaries, intensity inhomogeneity and heavy noise. Furthermore, this paper adopts convex relaxation approach to solve the corresponding optimization problem instead of classical level-set method, so the leading numerical computation is efficient and robust to initialization values. Experimental results also illustrate the efficiency of the proposed segmentation method for biomedical images under these extreme imaging conditions.

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L0regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.

Spatial modulation (SM) is a recently proposed multiple-input multiple-output (MIMO) architecture that exploits the space domain for information transmission. In this paper, SM systems with continuous amplitude-phase modulation (APM) and the corresponding maximum likelihood (ML) active transmit antenna detector are considered. We introduced a method to compute the theoretical transition probability matrix and the mutual information of the ML antenna detector. For SM multiple-input single-output (MISO) system, we derive the error probability and the mutual information of the antenna detector in closed-form. For multiple receive antenna SM system, the union bound of error probability and the lower bound of antenna detection mutual information are proposed. The analytical results are verified by numerical simulation over Rayleigh fading channels. We investigate systems with different number of antennas and APM symbol input distributions to evaluate the performance of SM. The joint APM and antenna detection mutual information of SM systems is compared to the capacity of vertical Bell Labs layered space-time (V-BLAST) spatial multiplexing MIMO scheme. Our research gives achievable transmission rates of SM-MISO/MIMO systems and can be a guideline for suitable application scenarios.

An error transfer chart is introduced to trace the iterative process of the decision feedback equalizer for the single-carrier modulation. In the investigated iterative MF-DFE structure, the forward filter is set to the matched filter to maximize the signal power, while the feedback filter is used to cancel the inter-symbol interference. MMSE detection is applied to minimize the decision error power. The trajectory of iterations is shown in the error transfer chart, in which MF-DFE maps a decision error power to an SINR and MMSE detection further converts the SINR to a new decision error power. The final state of iterations is shown to be the intersection of the MMSE curve and error power transfer curve corresponding to the MF-DFE algorithm. It is shown by simulation that the error transfer chart is a useful tool to analyze the convergence of non-linear iterative equalizers.

Frequency domain equalization (FDE), as a linear equalizing method, is widely adopted in many applications with either OFDM or single carrier (SC) modulation. This paper analyzes the the noise transfer effect, a case for noise colorization, after FDE. This process is investigated for CP-OFDM, ZP-OFDM and SC systems where SC is treated as a multi-carrier modulation with the number of subcarriers equal to 1. Based on this analysis and by assuming the de-mapping independence among subcarriers, the average channel capacity for the systems is derived. We have proved that the capacity of the CP-OFDM system is at least equal to that of ZP-OFDM in both low and high SNR scenarios and that the capacity of the SC system is no greater than that of either ZP-OFDM or CP-OFDM systems. In a multi-path channel, if the IDFT size of the OFDM symbol is not equal to the DFT size of the equalizer, the leakage of the enhanced noise in a deeply faded sub-channel will cause the loss of capacity in adjacent sub-channels and the overall average capacity is thus reduced.

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.

This paper presents a method to improve the semi-automatic method for intensity inhomogeneity correction by Dawant et al. through introducing a fully automatic approach to reference points generation, which is based on order statistics and integrates information from the fine to coarse scale representations of the input image. The method has been validated and compared with two popular methods, N3 and BFC. Advantages of the proposed method are demonstrated.

Rendering deformable volume data currently needs separate processes for deformation and rendering, and is expensive in terms of both computational and memory costs. Recognizing the importance of unifying these processes, we present a new approach to the direct rendering of deformable volumes without explicitly constructing the intermediate deformed volumes. The volume deformation is done by a radial basis function that is piecewise linearly approximated by an adaptive subdivision of the octree encoded target volume. The octree blocks in the target volume are then projected, reverse morphed and texture mapped, using the SGI 3D texture mapping hardware, in a back-to-front order. A template-based Z-plane/block intersection method is used to expedite the block projection computation.