A fully automatic parenchyma extraction method for MRI T2* relaxometry of iron loaded liver in transfusion-dependent patients.

PURPOSE: To develop a fully automatic parenchyma extraction method for the T2* relaxometry of iron overload liver. METHODS: A retrospective multicenter collection of liver MR examinations from 177 transfusion-dependent patients was conducted. The proposed method extended a semiautomatic parenchyma extraction algorithm to a fully automatic approach by introducing a modified TransUNet on the R2* (1/T2*) map for liver segmentation. Axial liver slices from 129 patients at 1.5 T were allocated to training (85%) and internal test (15%) sets. Two external test sets separately included 1.5 T data from 20 patients and 3.0 T data from 28 patients. The final T2* measurement was obtained by fitting the average signal of the extracted liver parenchyma. The agreement between T2* measurements using fully and semiautomatic parenchyma extraction methods was assessed using coefficient of variation (CoV) and Bland-Altman plots. RESULTS: Dice of the deep network-based liver segmentation was 0.970 ± 0.019 on the internal dataset, 0.960 ± 0.035 on the external 1.5 T dataset, and 0.958 ± 0.014 on the external 3.0 T dataset. The mean difference bias between T2* measurements of the fully and semiautomatic methods were separately 0.12 (95% CI: -0.37, 0.61) ms, 0.04 (95% CI: -1.0, 1.1) ms, and 0.01 (95% CI: -0.25, 0.23) ms on the three test datasets. The CoVs between the two methods were 4.2%, 4.8% and 2.0% on the internal test set and two external test sets. CONCLUSIONS: The developed fully automatic parenchyma extraction approach provides an efficient and operator-independent T2* measurement for assessing hepatic iron content in clinical practice.

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability. PURPOSE: To develop a deep learning-based method for automatic septum segmentation from black-blood MR images for the myocardial T2* measurement of thalassemia patients. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: One hundred forty-six transfusion-dependent thalassemia patients with cardiac MR examinations from two centers. Data from Center 1 (1.5 T) were assigned to the training (100 examinations) and internal testing (20 examinations) sets; data from Center 2 were assigned to the external testing set (26 examinations; 10 at 1.5 T and 16 at 3.0 T). FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, multiecho gradient-echo sequence. ASSESSMENT: A modified attention U-Net for septum segmentation was constructed and trained, and its performance evaluated on unseen internal and external datasets. T2* was measured by fitting the average septum signal, separately segmented by automatic and manual methods. STATISTICAL TESTS: Agreement between manual and automatic septum segmentations was assessed with the Dice coefficient, and T2* agreement was assessed using the Bland-Altman plot and the coefficient of variation (CoV). RESULTS: The median Dice coefficient of deep network-based septum segmentation was 0.90 [0.05] on the internal dataset, 0.82 [0.10] on the external 1.5 T dataset, and 0.86 [0.14] on the external 3.0 T dataset. T2* measurements using automatic segmentation corresponded with those from manual segmentation, with a mean difference of 0.02 (95% LoA: -0.74 to 0.79) msec, 0.43 (95% LoA: -2.1 to 3.0) msec, and 0.36 (95% LoA: -0.72 to 1.4) msec on the three datasets. The CoVs between the two methods were 3.1%, 7.0%, and 6.1% on the internal and two external datasets, respectively. DATA CONCLUSIONS: The proposed septum segmentation yielded myocardial T2* measurements which were highly consistent with those obtained by manual segmentation. This automatic approach may facilitate data processing and avoid operator-dependent variability in practice. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging. Recently, deep-learning-based image deconvolution approaches have shown promise for image recovery. However, PAT imaging presents unique challenges, including spatially varying resolution and the absence of ground truth data. Consequently, there is a pressing need for a novel learning strategy specifically tailored for PAT imaging. Herein, we propose a configurable network model named Deep hybrid Image-PSF Prior (DIPP) that builds upon the physical image degradation model of PAT. DIPP is an unsupervised and deeply learned network model that aims to extract the ideal PAT image from complex system degradation. Our DIPP framework captures the degraded information solely from the acquired PAT image, without relying on ground truth or labeled data for network training. Additionally, we can incorporate the experimentally measured Point Spread Functions (PSFs) of the specific PAT system as a reference to further enhance performance. To evaluate the algorithm's effectiveness in addressing multiple degradations in PAT, we conduct extensive experiments using simulation images, publicly available datasets, phantom images, and in vivo small animal imaging data. Comparative analyses with classical analytical methods and state-of-the-art deep learning models demonstrate that our DIPP approach achieves significantly improved restoration results in terms of image details and contrast.

[Cone-beam pseudo lambda tomography under FDK framework].

The medical CT scanner is rapidly evolving from the fan-beam mode to the cone-beam geometry mode. In this paper, a new cone-beam pseudo Lambda tomography was proposed based on the Noo's fan beam super-short scan formula and FDK framework. The proposed pseudo-LT algorithm, which avoids the computation of any PI line and any differential operation, has a significant practical implementation, thus leading to the images with quality improvement and reduced artifacts. The results in the simulation studies confirm the observation that the new algorithm can improve the image resolution over the traditional algorithms with noise projection data.

[Elimination of direct derivative of the projection data for image reconstruction based on hybrid filter in helical cone-beam CT].

OBJECTIVE: We present an alternative approach for precise reconstruction of the images from helical cone-beam projections combining Hilbert filter and Ramp filter. METHODS: Based on the Katsevich algorithm framework, the proposed algorithm combined the FDK-type algorithms and Katsevich algorithm for their respective advantages, to completely avoid the direct derivatives with respect to the coordinates on the detector plane. RESULTS: The experimental results validated the accuracy of the new algorithm, and this approach significantly improved the resolution of the reconstructed images with much reduced artifacts. CONCLUSION: The proposed reconstruction formula based on hybrid Hilbert-Ramp filter is an important development of Katsevich reconstruction formula, and the different forms of the Ramp filters can be designed to realize frequency modulation according to the actual clinical application.

A noval fragile watermarking method for medical images using quadtree structure.

This paper presents a new fragile watermarking scheme based on integral wavelet transform. The proposed method uses the quadtrees structures received by the wavelet decomposition, statistical information on the nodes and secret key to choose the locations where to embed the watermark. Only one bit watermark information will be hidden in the found position. Any changes on the fragile watermarked image will break the way to find out the correct position where the watermark is embedded. The preliminary experimental results indicate that the proposed method conforms to the human perception characteristics and provides a perceptually invisible fragile watermark with fewer image data modified, compared with some other conventional fragile watermarking methods.