Head movement poses a significant challenge in brain positron emission tomography (PET) imaging, resulting in image artifacts and tracer uptake quantification inaccuracies. Effective head motion estimation and correction are crucial for precise quantitative image analysis and accurate diagnosis of neurological disorders. Hardware-based motion tracking (HMT) has limited applicability in real-world clinical practice. To overcome this limitation, we propose a deep-learning head motion correction approach with cross-attention (DL-HMC++) to predict rigid head motion from one-second 3D PET raw data. DL-HMC++ is trained in a supervised manner by leveraging existing dynamic PET scans with gold-standard motion measurements from external HMT. We evaluate DL-HMC++ on two PET scanners (HRRT and mCT) and four radiotracers (18F-FDG,18F-FPEB,11C-UCB-J, and11C-LSN3172176) to demonstrate the effectiveness and generalization of the approach in large cohort PET studies. Quantitative and qualitative results demonstrate that DL-HMC++ consistently outperforms state-of-the-art data-driven motion estimation methods, producing motion-free images with clear delineation of brain structures and reduced motion artifacts that are indistinguishable from gold-standard HMT. Brain region of interest standard uptake value analysis exhibits average difference ratios between DL-HMC++ and gold-standard HMT to be 1.2±0.5% for HRRT and 0.5±0.2% for mCT. DL-HMC++ demonstrates the potential for data-driven PET head motion correction to remove the burden of HMT, making motion correction accessible to clinical populations beyond research settings. The code is available at https://github.com/maxxxxxxcai/DL-HMC-TMI.

Accurate uncertainty estimation is crucial for deploying neural networks in risk-sensitive applications such as medical diagnosis. Monte Carlo Dropout is a widely used technique for approximating predictive uncertainty by performing stochastic forward passes with dropout during inference. However, using static dropout rates across all layers and inputs can lead to suboptimal uncertainty estimates, as it fails to adapt to the varying characteristics of individual inputs and network layers. Existing approaches optimize dropout rates during training using labeled data, resulting in fixed inference-time parameters that cannot adjust to new data distributions, compromising uncertainty estimates in Monte Carlo simulations.In this paper, we propose Rate-In, an algorithm that dynamically adjusts dropout rates during inference by quantifying the information loss induced by dropout in each layer’s feature maps. By treating dropout as controlled noise injection and leveraging information-theoretic principles, Rate-In adapts dropout rates per layer and per input instance without requiring ground truth labels. By quantifying the functional information loss in feature maps, we adaptively tune dropout rates to maintain perceptual quality across diverse medical imaging tasks and architectural configurations. Our extensive empirical study on synthetic data and real-world medical imaging tasks demonstrates that Rate-In improves calibration and sharpens uncertainty estimates compared to fixed or heuristic dropout rates without compromising predictive performance. Rate-In offers a practical, unsupervised, inference-time approach to optimizing dropout for more reliable predictive uncertainty estimation in critical applications.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases , cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions caused by the low metabolite concentrations. Therefore, there is an imperative need for a post-processing approach to generate high-resolution MRSI from low-resolution data that can be acquired fast and with high sensitivity. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but they still have limited capability to generate accurate and high-quality images. Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming. This work introduces a Flow-based Truncated Denoising Diffusion Model (FTDDM) for super-resolution MRSI, which shortens the diffusion process by truncating the diffusion chain, and the truncated steps are estimated using a normalizing flow-based network. The network is conditioned on upscaling factors to enable multi-scale super-resolution. To train and evaluate the deep learning models, we developed a 1 H-MRSI dataset acquired from 25 high-grade glioma patients. We demonstrate that FTDDM outperforms existing generative models while speeding up the sampling process by over 9-fold compared to the baseline diffusion model. Neuroradiologists’ evaluations confirmed the clinical advantages of our method, which also supports uncertainty estimation and sharpness adjustment, extending its potential clinical applications.

In bone cancer imaging, positron emission tomography (PET) is ideal for the diagnosis and staging of bone cancers due to its high sensitivity to malignant tumors. The diagnosis of bone cancer requires tumor analysis and localization, where accurate and automated wholebody bone segmentation (WBBS) is often needed. Current WBBS for PET imaging is based on paired Computed Tomography (CT) images. However, mismatches between CT and PET images often occur due to patient motion, which leads to erroneous bone segmentation and thus, to inaccurate tumor analysis. Furthermore, there are some instances where CT images are unavailable for WBBS. In this work, we propose a novel multimodal fusion network (MMF-Net) for WBBS of PET images, without the need for CT images. Specifically, the tracer activity ($\lambda$-MLAA), attenuation map ($\mu$-MLAA), and synthetic attenuation map ($\mu$-DL) images are introduced into the training data. We first design a multi-encoder structure employed to fully learn modalityspecific encoding representations of the three PET modality images through independent encoding branches. Then, we propose a multimodal fusion module in the decoder to further integrate the complementary information across the three modalities. Additionally, we introduce revised convolution units, SE (Squeeze-and-Excitation) Normalization and deep supervision to improve segmentation performance. Extensive comparisons and ablation experiments, using 130 whole-body PET image datasets, show promising results. We conclude that the proposed method can achieve WBBS with moderate to high accuracy using PET information only, which potentially can be used to overcome the current limitations of CT-based approaches, while minimizing exposure to ionizing radiation.

Accurate cardiac segmentation in cross-modality images plays an important role in the quantitative analysis of the heart to diagnose cardiovascular diseases. However, achieving high performance in cross-modality segmentation is hindered by the time-consuming annotation and modality gap. While some approaches employ Unsupervised Domain Adaptation (UDA) through adversarial learning to address the issue, it still remains challenging due to the instability of the adversarial generative models. In this work, we propose Symmetric Consistency with Cross-Domain Mixup (SCCDM), integrated with the teacher-student model for cross-modality cardiac segmentation. Specifically, we introduce symmetric consistency across the domains for two mixed data to diversify the data distribution from both the source domain and target domain. Extensive experiments on a public cardiac dataset demonstrate that SCCDM achieves superior domain adaptation performance for cardiac segmentation compared to state-of-the-art methods.

Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh labels for precise spatial accuracy and the simultaneous optimization of isotropic and anisotropic deformation energies for volumetric mesh quality. Mesh generation takes only 0.13 seconds/scan during inference, and each mesh can be directly used for finite element analyses without any manual post-processing. Calcification meshes can also be subsequently incorporated for increased simulation accuracy. Numerous stent deployment simulations validate the viability of our approach for large-batch analyses. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.

Unsupervised Domain Adaptation (UDA), which aligns the labeled source distribution to the unlabeled target distribution, has shown remarkable achievement in the medical image segmentation task. Previous UDA methods unilaterally consider the global distribution alignment through explicit category-based loss while good separation and discrimination of class are insufficiently explored, resulting in the sub-aligned distribution across domains. In this paper, we propose cross-prototype contrastive learning method (CPCL) for UDA segmentation through class centroid alignment. Specifically, to reduce the intra-class distance and increase the inter-class distance, we first introduce prototype-feature contrastive learning to align the pixel-level features and the same-class global prototype across domains. Secondly, we further present prototype-prototype contrastive learning to align the same class prototypes between the source domain and target domain for compact category centroid and better global domain distribution alignment. Extensive experiments on two public cardiac datasets demonstrate that the proposed CPCL achieves superior domain adaptation performance as compared with the state-of-the-art.

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Attenuation maps (μ-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve the diagnostic accuracy of cardiac SPECT. However, in clinical practice, SPECT and CT scans are acquired sequentially, potentially inducing misregistration between the two images and further producing AC artifacts. Conventional intensity-based registration methods show poor performance in the cross-modality registration of SPECT and CT-derived μ-maps since the two imaging modalities might present totally different intensity patterns. Deep learning has shown great potential in medical imaging registration. However, existing deep learning strategies for medical image registration encoded the input images by simply concatenating the feature maps of different convolutional layers, which might not fully extract or fuse the input information. In addition, deep-learning-based cross-modality registration of cardiac SPECT and CT-derived μ-maps has not been investigated before. In this paper, we propose a novel Dual-Channel Squeeze-Fusion-Excitation (DuSFE) co-attention module for the cross-modality rigid registration of cardiac SPECT and CT-derived μ-maps. DuSFE is designed based on the co-attention mechanism of two cross-connected input data streams. The channel-wise or spatial features of SPECT and μ-maps are jointly encoded, fused, and recalibrated in the DuSFE module. DuSFE can be flexibly embedded at multiple convolutional layers to enable gradual feature fusion in different spatial dimensions. Our studies using clinical patient MPI studies demonstrated that the DuSFE-embedded neural network generated significantly lower registration errors and more accurate AC SPECT images than existing methods. We also showed that the DuSFE-embedded network did not over-correct or degrade the registration performance of motion-free cases. The source code of this work is available at https://github.com/XiongchaoChen/DuSFE_CrossRegistration.

The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require accurate registration of the post-implantation CT images to the pre-implantation functional and structural magnetic resonance imaging to guide surgical resection of epileptic tissue. One way to perform the registration is via surface matching. The key challenge in this setup is the CT segmentation, where the extraction of the cortical surface is difficult due to the missing parts of the skull and artifacts introduced from the electrodes. In this paper, we present a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. We propose learning a model of locally oriented appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Utilizing a database of clinical epilepsy imaging data to train and test our approach, we demonstrate that our method using locally oriented image appearance both more accurately extracts the brain surface and better localizes electrodes on the post-operative brain surface compared to standard, non-oriented appearance modeling. In addition, we compare our method to a standard atlas-based segmentation approach and to a U-Net-based deep convolutional neural network segmentation method.

Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with18F-FPDTBZ and one with18F-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The18F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.

Kernel methods are powerful tools for nonlinear feature representation. Incorporated with structured LASSO, the kernelized structured LASSO is an effective feature selection approach that can preserve the nonlinear input-output relationships as well as the structured sparseness. But as the data dimension increases, the method can quickly become computationally prohibitive. In this paper we propose a stochastic optimization algorithm that can efficiently address this computational problem on account of the redundant kernel representations of the given data. Experiments on simulation data and PET 3D brain image data show that our method can achieve superior accuracy with less computational cost than existing methods.

Accurate and robust image registration is a fundamental task in medical image analysis applications, and requires non-rigid transformations with a large number of degrees of freedom. Statistical deformation models (SDMs) attempt to learn the distribution of non-rigid deformations, and can be used both to reduce the transformation dimensionality and to constrain the registration process. However, high-dimensional SDMs are difficult to train given orders of magnitude fewer training samples. In this paper, we utilize both a small set of annotated imaging data and a large set of unlabeled data to effectively learn an SDM of non-rigid transformations in a semi-supervised training (SST) framework. We demonstrate results applying this framework towards inter-subject registration of skull-stripped, magnetic resonance (MR) brain images. Our approach makes use of 39 labeled MR datasets to create a set of supervised registrations, which we augment with a set of over 1200 unsupervised registrations using unlabeled MRIs. Through leave-one-out cross validation, we show that SST of a non-rigid SDM results in a robust registration algorithm with significantly improved accuracy compared to standard, intensity-based registration, and does so with a 99% reduction in transformation dimensionality.