Dynamic Imaging Research Articles

An international survey of diffusion and perfusion magnetic resonance imaging implementation in the head and neck.

The goal of this international survey was to understand how diffusion (DWI) and perfusion imaging (PWI) are being applied to clinical head and neck imaging. An online questionnaire focusing on acquisition, clinical indications, analysis, and reporting of qualitative DWI (QlDWI), quantitative DWI (QnDWI) and dynamic contrast-enhanced PWI (DCE-PWI) in the head and neck was circulated to members of the American Society of Head and Neck Radiology (ASHNR) and European Society of Head and Neck Radiology (ESHNR) over a 3-month period. Descriptive statistics and group comparisons were calculated with SPSS® v27. There were 294 unique respondents (17.6% response rate) from 256 institutions (182 ESHNR, 74 ASHNR). DWI was routinely acquired for some head and neck indications at 95.7% of the respondents' institutions, with 92.5% of radiologists interpreting QlDWI but only 36.7% analysing QnDWI. QlDWI was most frequently applied to primary mucosal masses or the middle ear, whilst QnDWI was routinely used to distinguish tumour histologies, and primary or recurrent carcinoma. DCE-PWI was routinely acquired at 53.6% of institutions and used by 40.8% of respondents, however, there was no clinical scenario in which it was routinely applied by most users. DCE-PWI analysis methods varied, with time-intensity curve classifications being the most frequently reported. Lack of standardisation was identified as a key reason for not implementing QnDWI, whilst numerous factors prevented the adoption of DCE-PWI. There is widespread routine interpretation of QlDWI by head and neck radiologists, but there is considerable variation in the application and analysis of head and neck QnDWI and DCE-PWI. Question How are diffusion (DWI) and dynamic contrast-enhanced perfusion imaging (DCE-PWI) being utilised by head and neck radiologists across a wide range of practices? Findings An international survey demonstrated widespread routine interpretation of qualitativeDWI but variable application and analysis of quantitative DWI and DCE-PWI with numerous barriers to implementation. Clinical relevance The survey results will aid discussion on how to standardise and optimally disseminate these MRI techniques in day-to-day practice. More focused education and resource allocation may be required to accelerate the adoption of quantitative DWI and DCE-PWI.

Advanced Multiparametric MRI Strategies for Tumor Restaging After Neoadjuvant Therapy in Locally Advanced Gastric Cancer.

This study tested the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in restaging locally advanced gastric cancer after neoadjuvant therapy (NAT) using pathologic T stage (ypT) and pathologic N stage (ypN) as the reference standard. Between August 2022 and September 2023, the study enrolled a prospective cohort of 70 gastric cancer patients who underwent NAT and subsequent surgical resection. MRI procedures, including DLSB T2-weighted imaging (T2WI), ZOOMit diffusion-weighted imaging (DWI), and XD-VIBE dynamic contrast-enhanced imaging (DCE), were performed after NAT and before surgery. Four abdominal radiologists independently assigned radiologic T stage (yrT) and radiologic N stage (yrN) based on individual and combined sequences. Inter-reader agreement was quantified using Kendall's coefficient. Diagnostic accuracy was determined by comparing MRI assessments and pathologic outcomes, with pairwise comparisons analyzed via the McNemar test. Subgroup analysis evaluated the performance in identifying good responders to NAT. Inter-reader agreement was almost perfect for T restaging and substantial for N restaging. Diagnostic accuracy for T restaging was 0.432 using DLSB-T2WI, 0.586 using ZOOMit DWI, 0.557 using XD-VIBE DCE, and 0.586 using mpMRI. The accuracy demonstrated by DWI, DCE and mpMRI was superior to that of T2WI (all P < 0.05). For N restaging, the accuracy of the mpMRI protocol was 0.443. Notably, mpMRI achieved an AUC of 0.879 (95% confidence interval 0.835-0.915) for differentiating ypT0-1 tumors. Advanced mpMRI strategies can serve as a valuable tool for restaging gastric cancer after NAT. Accurately differentiating good responders to neoadjuvant therapy through mpMRI holds significant clinical implications for personalized treatment strategies and improved patient outcomes.

Fidgetin binds spastin to attenuate the microtubule-severing activity.

Microtubule-severing enzymes such as spastin, katanin, and fidgetin, characterized by their AAA ATPase domains, are pivotal in modulating microtubule dynamics and behavior across various cellular processes. While spastin and katanin are recognized for their predominant and robust severing of stable microtubules, thereby enhancing microtubule turnover, fidgetin exhibits comparatively weaker severing activity and selectively targets labile microtubules. The interplay among these enzymes and their mutual regulatory mechanisms remains inadequately understood. In this study, we elucidate the functional interaction between spastin and fidgetin, focusing on their roles in microtubule severing and neurite outgrowth. Our findings demonstrate that fidgetin serves as a negative regulator of spastin's severing activity. Co-expression assays revealed that fidgetin significantly attenuates spastin's severing efficiency, as confirmed by fluorescence-based microtubule polymerization assays and quantitative imaging of microtubule dynamics. Co-immunoprecipitation and Förster Resonance Energy Transfer (FRET) analyses further established a direct interaction between fidgetin and spastin, suggesting that fidgetin modulates spastin's activity through direct binding, possibly contributing to forming the hetero-hexmeric ring for their severing activities. Functionally, spastin overexpression in neuronal cells enhances neurite outgrowth, an effect that is suppressed upon co-expression with fidgetin, indicating that fidgetin counterbalances spastin's activity to regulate neurite extension. Therefore, this study uncovers a previously unrecognized mechanism by which fidgetin modulates spastin's function, providing critical insights into the intricate regulation of microtubule severing. These findings have significant implications for therapeutic strategies targeting microtubule-severing activities, particularly in neurodevelopmental and neurodegenerative disorders where microtubule dysregulation is a hallmark.

Evaluation of anatomical and functional success in canalicular laceration repair using anterior segment optical coherence tomography and dacryoscintigraphy.

To assess the anatomical and functional outcomes in patients who underwent surgery for canalicular laceration and did not experience significant epiphora, and to compare these outcomes with contralateral uninjured eye. Dacryoscintigraphy, anterior segment optical coherence tomography (OCT), MUNK scores, and a satisfaction questionnaire were administered to 24 patients who had canalicular laceration repair without significant epiphora and had a minimum of 6 months of follow-up. Tear meniscus height (TMH), depth (TMD), and area (TMA) were measured using anterior segment OCT and compared with the values in the uninjured eye. Sixteen patients (67 %) had lower, 7 (29 %) had upper canaliculus, and one (4 %) had lacerations in both canaliculi. Five patients (20.83 %) had concomitant orbital fractures, and three patients (12.5 %) underwent additional repair for corneoscleral laceration. Bicanalicular silicone tube intubation was performed in one patient, while Mini-Monoka stent intubation was used for all other patients. The mean follow-up period was 41 ± 22.3 months, and the mean interval between trauma and surgery was 30.2 ± 29 h. The mean duration until tube removal was 3 ± 2.54 months. The MUNK score was 1 in eight patients (33 %) and 0 in sixteen patients (67 %). Canalicular lacerations occurred due to home accidents in 8 patients, work accidents in 4, assaults in 5, and traffic accidents in 7. Dacryoscintigraphy showed drainage of tracer into the nasal cavity in dynamic imaging for 12 patients. Five patients had retention in the sac, four had prolonged and reduced drainage, one had reduced drainage, and two showed no drainage in the first and second hour images. In the operated eye, the mean tear meniscus height (TMH) was 279.6 µm, depth (TMD) was 215 µm, and area (TMA) was 28.9 µm². In the healthy eye, the mean TMH was 221.5 µm, TMD was 152.5 µm, and TMA was 15.3 µm². The anterior segment OCT values for the affected eye were statistically significantly higher than those for the healthy eye (p = 0.044 for TMH, p = 0.003 for TMD, p = 0.006 for TMA). TMD and TMA were statistically significantly higher in patients with a MUNK score of 1 (p = 0.019 for TMD, p = 0.05 for TMA). Abnormal dacryoscintigraphy results were more common in patients with globe injuries requiring additional surgery. Although patients did not report epiphora after canalicular laceration repair, our observations indicated potential functional and anatomical differences in dacryoscintigraphy and anterior segment OCT compared to their healthy eyes. We believe that close follow-up is crucial to identify and address any issues that may arise in the future following surgery.

Dynamic imaging of macrophages in MASLD: a major interest in insulin resistance and outside the liver

Dynamic imaging of macrophages in MASLD: a major interest in insulin resistance and outside the liver

Self-supervised parametric map estimation for multiplexed PET with a deep image prior

Multiplexed positron emission tomography (mPET) imaging allows simultaneous observation of physiological and pathological information from multiple tracers in a single PET scan. Although supervised deep learning has demonstrated superior performance in mPET image separation compared to purely model-based methods, acquiring large amounts of paired single-tracer data and multi-tracer data for training poses a practical challenge and needs extended scan durations for patients. In addition, the generalisation ability of the supervised learning framework is a concern, as the patient being scanned and their tracer kinetics may potentially fall outside the training distribution. In this work, we propose a self-supervised learning framework based on the deep image prior (DIP) for mPET image separation using just one dataset. In particular, we integrate the multi-tracer compartmental model into the DIP framework to estimate the parametric maps of each tracer from the measured dynamic dual-tracer activity images. Consequently, the separated dynamic single-tracer activity images can be recovered from the estimated tracer-specific parametric maps. In the proposed method, dynamic dual-tracer activity images are used as the training label, and the static dual-tracer image (reconstructed from the same patient data from the start to the end of acquisition) is used as the network input. The performance of the proposed method was evaluated on a simulated brain phantom for dynamic dual-tracer [18F]FDG+[11C]MET activity image separation and parametric map estimation. The results demonstrate that the proposed method outperforms the conventional voxel-wise multi-tracer compartmental modeling method (vMTCM) and the two-step method DIP-Dn+vMTCM (where dynamic dual-tracer activity images are first denoised using a U-net within the DIP framework, followed by vMTCM separation) in terms of lower bias and standard deviation in the separated single-tracer images and also for the estimated parametric maps for each tracer, at both voxel and ROI levels.

Lensless efficient snapshot hyperspectral imaging using dynamic phase modulation

Snapshot hyperspectral imaging based on a diffractive optical element (DOE) is increasingly featured in recent progress in deep optics. Despite remarkable advances in spatial and spectral resolutions, the limitations of current photolithography technology have prevented the fabricated DOE from being designed at ideal heights and with high diffraction efficiency, diminishing the effectiveness of coded imaging and reconstruction accuracy in some bands. Here, we propose, to our knowledge, a new lensless efficient snapshot hyperspectral imaging (LESHI) system that utilizes a liquid-crystal-on-silicon spatial light modulator (LCoS-SLM) to replace the traditionally fabricated DOE, resulting in high modulation levels and reconstruction accuracy. Beyond the single-lens imaging model, the system can leverage the switch ability of LCoS-SLM to implement distributed diffractive optics (DDO) imaging and enhance diffraction efficiency across the full visible spectrum. Using the proposed method, we develop a proof-of-concept prototype with an image resolution of 1920×1080 pixels, an effective spatial resolution of 41.74 μm, and a spectral resolution of 10 nm, while improving the average diffraction efficiency from 0.75 to 0.91 over the visible wavelength range (400–700 nm). Additionally, LESHI allows the focal length to be adjusted from 50 mm to 100 mm without the need for additional optical components, providing a cost-effective and time-saving solution for real-time on-site debugging. LESHI is the first imaging modality, to the best of our knowledge, to use dynamic diffractive optics and snapshot hyperspectral imaging, offering a completely new approach to computational spectral imaging and deep optics.

Development of the office lighting control algorithm based on spatial luminance distribution analysis

ABSTRACT The lighting conditions in office buildings have a significant impact on the productivity and well-being of individuals. It is therefore essential to ensure that the artificial lighting system provides an optimal light environment. Currently, though, most automated office lighting control systems consider only workplace illuminance, without accounting for the luminance distribution in the room which largely determines the level of visual comfort. In this study, a basic algorithm for controlling artificial lighting is presented and evaluated. The algorithm is designed after examining the spatial distribution of luminance in a standard office space with daylight access. The algorithm utilizes the established methods for capturing High Dynamic Range images and linear programming. The suggested algorithm was compared to a proportional closed-loop control algorithm based on light sensors, commonly adopted for controlling lighting in rooms with daylight availability. Data for the comparison was derived from the experiment involving 13 participants, based on the subjective assessment of lighting quality and objective measures of visual performance. The study indicates that the lighting control algorithm proposed received significantly higher subjective ratings from the experiment participants. However, further research is required to evaluate visual performance.

Multifunctional Fluorescent Probe for Simultaneous Detection of ATP, Cys, Hcy, and GSH: Advancing Insights into Epilepsy and Liver Injury.

Adenosine triphosphate (ATP) is a critical intracellular energy currency that plays a key role in various cellular processes and is closely associated with numerous diseases. Similarly, biothiols such as glutathione (GSH), cysteine (Cys), and homocysteine (Hcy) are integral to many physiological and pathological processes due to their strong redox properties. Simultaneous discrimination and detection of ATP and biothiols offer valuable insights into the pathogenesis of conditions such as epilepsy and liver injury. This study introduces the first fluorescent probe, BCR, designed for multifunctional detection of ATP, GSH, Hcy, and Cys. With outstanding optical properties, excellent biocompatibility, high selectivity, and superior sensitivity, probe BCR enables effective imaging of ATP and biothiol dynamics in vivo. Moreover, probe BCR successfully visualizes changes in ATP, GSH, Hcy, and Cys levels in a PTZ-induced epileptic zebrafish model and an APAP-induced mouse liver injury tissue section model. These findings underscore the significant potential of probe BCR for early disease diagnosis and therapeutic applications.

Comparison of global and regional myocardial blood flow quantification using dynamic solid-state detector SPECT and Tc-99m-sestamibi or Tc-99m-tetrofosmin in a routine clinical setting.

Solid-state detector single photon emission computed tomography (SPECT) enables the acquisition of dynamic data for calculation of myocardial blood flow (MBF) and myocardial flow reserve (MFR). Here, we report about our experiences on routine clinical use and robustness using Tc-99m-sestamibi and Tc-99m-tetrofosmin. 307 patients underwent dynamic list-mode myocardial perfusion imaging (MPI) and standard static MPI for clinical workup of coronary artery disease on a dedicated cardiac SPECT camera. After exclusion of 33 scans, 274 scans were eligible for MBF and MFR calculation using a 1-tissue-compartment model. Attenuation correction was performed for all patients using an external computed tomogram. Patients underwent stress-only scans, both stress and rest scans or rest-only scans using Tc-99m-tetrofosmin or Tc-99m-sestamibi. 30 patients without known cardiovascular comorbidities and without perfusion defect on static scans were compared in a sub analysis. Global stress myocardial blood flow (MBF) was significantly higher than rest MBF (2.3 vs. 1.1ml/min/g; p < 0.001), and showed a high variability among individuals. Global myocardial flow reserve (MFR) was 2.1 (range 0.5-7.8). An analysis of 30 patients without known cardiovascular comorbidities yielded similar stress MBF measures for Tc-99m-sestamibi and Tc-99m-tetrofosmin (3.1 ± 1.2 vs. 2.8 ± 0.9ml/min/g; p = 0.429). The use of attenuation correction lead to systematically lower MBF measures. Patients who underwent a one-day protocol had notably higher rest MBF (1.2 ± 0.5 vs. 1.0 ± 0.46ml/min/g; p = 0.009) and consequently a lower MFR. Summed defect scores from standard static scans and presence of cardiovascular comorbidities negatively impacted MBF and MFR. Quantitative SPECT MBF and MFR in a clinical routine setting yields flow measures in range of expectation at an albeit wide range and is comprehensibly linked with results from standard static scan and patients history of cardiovascular diseases. Use of one-day protocols and attenuation correction systematically alters quantitative results. However, SPECT-derived MBF and MFR lack clinical reliability due to less validated reference ranges and high inter-individual variability.

Large blood vessel segmentation in quantitative DCE-MRI of brain tumors: A Swin UNETR approach.

Brain tumor growth is associated with angiogenesis, wherein the density of newly developed blood vessels indicates tumor progression and correlates with the tumor grade. Quantitative dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) has shown potential in brain tumor grading and treatment response assessment. Segmentation of large-blood-vessels is crucial for automatic and accurate tumor grading using quantitative DCE-MRI. Traditional manual and semi-manual rule-based large-blood-vessel segmentation methods are time-intensive and prone to errors. This study proposes a novel deep learning-based technique for automatic large-blood-vessel segmentation using Swin UNETR architectures and comparing it with U-Net and Attention U-Net architectures. The study employed MRI data from 187 brain tumor patients, with training, validation, and testing datasets sourced from two centers, two vendors, and two field-strength magnetic resonance scanners. To test the generalizability of the developed model, testing was also carried out on different brain tumor types, including lymphoma and metastasis. Performance evaluation demonstrated that Swin UNETR outperformed other models in segmenting large-blood-vessel regions (achieving Dice scores of 0.979, and 0.973 on training and validation sets, respectively, with test set performance ranging from 0.835 to 0.982). Moreover, most quantitative parameters showed significant differences (p < 0.05) between with and without large-blood-vessel. After large-blood-vessel removal, using both ground truth and predicted masks, the values of parameters in non-vascular tumoral regions were statistically similar (p > 0.05). The proposed approach has potential applications in improving the accuracy of automatic grading of tumors as well as in treatment planning.

Real-Time High Dynamic Equalization Industrial Imaging Enhancement Based on Fully Convolutional Network

Severe reflections on the surfaces of smooth objects can result in low dynamic range and uneven illumination in images, which negatively impacts downstream tasks such as defect detection and QR code recognition on images of smooth workpieces. Consequently, this paper proposes a novel approach to real-time high dynamic equalization imaging based on a fully convolutional network, termed Multi-exposure Image Fusion with Multi-dimensional Attention Mechanism and Training Storage Units (MEF-AT). Specifically, this paper innovatively proposes using training storage units, which utilize intermediate results during network training as auxiliary images, to remove uneven illumination and enhance image dynamic range effectively. Furthermore, by integrating a multi-dimensional attention mechanism into the backbone network, the model can more efficiently extract and utilize critical image information. Additionally, this paper introduces a Deep Guided Filter (DGF) with learnable parameters, which upsample the weight maps generated by the network, thus better adapting to complex industrial scenarios and producing higher quality fused images. An image evaluation metric assessing the lighting uniformity is introduced to thoroughly evaluate the proposed method’s performance. Given the lack of an MEF dataset for smooth workpieces, this paper collects a new dataset for multi-exposure fusion tasks on metallic workpieces. Our method takes less than 4 ms to run four 2K images on a GPU 3090. Both qualitative and quantitative experimental results demonstrate our method’s superior comprehensive performance in proprietary industrial and public datasets.

Molecular Imaging for Biomimetic Nanomedicine in Cancer Therapy: Current Insights and Challenges.

Coating biological membranes onto biomimetic nanocarriers improves biocompatibility, prolongs circulation, and enhances targeted delivery for cancer precision medicine. To better understand the biodistribution profiles of these biomimetic nanosystems, molecular imaging techniques, including optical imaging, radionuclide imaging, magnetic resonance imaging, and ultrasound imaging, have been widely employed for in vivo tracking and dynamic imaging. Here in this review, we delve into the profound role of these imaging modalities in visualizing changes in the tumor microenvironment, particularly in monitoring oxygen consumption and immune response dynamics, highlighting their potential to improve cancer therapies. We also briefly discuss current applications of molecular imaging in synergistic cancer therapies and future perspectives. Finally, we offer insights into the potential of integrating biomimetic nanomedicine with molecular imaging for clinical translation.

JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI.

Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction. However, existing works mainly utilized matrix low-rank priors, neglecting the tensor characteristics of dynamic MRI images, and only a global threshold is applied for the sparse constraint to the multi-channel data, limiting the flexibility of the network. Additionally, most of them have inherently complex network structure, with intricate interactions among variables. In this paper, we propose a novel deep unrolling network, JotlasNet, for dynamic MRI reconstruction by jointly utilizing tensor low-rank and attention-based sparse priors. Specifically, we utilize tensor low-rank prior to exploit the structural correlations in high-dimensional data. Convolutional neural networks are used to adaptively learn the low-rank and sparse transform domains. A novel attention-based soft thresholding operator is proposed to assign a unique learnable threshold to each channel of the data in the CNN-learned sparse domain. The network is unrolled from the elaborately designed composite splitting algorithm and thus features a simple yet efficient parallel structure. Extensive experiments on two datasets (OCMR, CMRxRecon) demonstrate the superior performance of JotlasNet in dynamic MRI reconstruction.

Event-driven neuromorphic holography for dynamic particle imaging

Event-driven neuromorphic holography for dynamic particle imaging

A Pilot Trial of Tongue Reconstruction Using the Chimeric Innervated Vastus Lateralis Muscle and Antero-Lateral Thigh Free Flap.

Subtotal and total glossectomies for advanced tongue cancer result in significant speech- and swallow-related morbidity, impairing quality of life. This prospective pilot study compares the safety and functional outcomes associated with using a chimeric innervated muscle and fasciocutaneous flap for soft tissue reconstruction. A prospective, non-randomized controlled pilot study evaluated a standardized technique for tongue reconstruction using a chimeric innervated vastus lateralis muscle and anterolateral thigh fasciocutaneous flap. Inclusion criteria were ≥ 50% resection of the oral tongue. Participants were followed longitudinally, with measures recorded at baseline, 6-8 weeks, 6 months, and 12 months after surgery. The primary endpoints were post-operative complications, time to radiotherapy, operative time, and locoregional failure, videofluoroscopy swallow studies (dynamic imaging grade of swallowing toxicity (DIGEST), penetration and aspiration score, performance status scale for head and neck). Secondary endpoints were patient-rated outcomes. Eighteen participants were recruited (10 intervention: 8 controls). Fourteen (78%) experienced complications, only one of which was related to the innervated flap. DIGEST scores deteriorated post-operatively across all participants but did not differ significantly between the intervention and control groups (p = 0.4) at any point post-surgery, despite more extensive resections in the intervention group. Those in the intervention group had better patient-rated intelligibility (p = 0.04). Multimodality treatment was associated with worse speech (p = 0.03) and normalcy of diet (p = 0.02). Less extensive resections were associated with better scores in eating in public (p = 0.005), tongue range of movement (p = 0.0008), intelligibility (p = 0.006), and diet (p = 0.001). The innervated vastus lateralis and antero-lateral thigh free-flap technique is safe and improves speech intelligibility for patients with subtotal and total glossectomy defects. However, the technique requires refinement to optimize functional and quality-of-life outcomes.

Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.

The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advances in CT-less PET imaging further highlight the necessity for an effective PET organ segmentation pipeline that does not rely on CT images. Therefore, the goal of this study was to develop a CT-less multitracer PET segmentation framework. We collected 2062 PET/CT images from multiple scanners. The patients were injected with either 18F-FDG (1487) or 68Ga-PSMA (575). PET/CT images with any kind of mismatch between PET and CT images were detected through visual assessment and excluded from our study. Multiple organs were delineated on CT components using previously trained in-house developed nnU-Net models. The segmentation masks were resampled to coregistered PET images and used to train 4 different deep learning models using different images as input, including noncorrected PET (PET-NC) and attenuation and scatter-corrected PET (PET-ASC) for 18F-FDG (tasks 1 and 2, respectively using 22 organs) and PET-NC and PET-ASC for 68Ga tracers (tasks 3 and 4, respectively, using 15 organs). The models' performance was evaluated in terms of Dice coefficient, Jaccard index, and segment volume difference. The average Dice coefficient over all organs was 0.81 ± 0.15, 0.82 ± 0.14, 0.77 ± 0.17, and 0.79 ± 0.16 for tasks 1, 2, 3, and 4, respectively. PET-ASC models outperformed PET-NC models (P < 0.05) for most of organs. The highest Dice values were achieved for the brain (0.93 to 0.96 in all 4 tasks), whereas the lowest values were achieved for small organs, such as the adrenal glands. The trained models showed robust performance on dynamic noisy images as well. Deep learning models allow high-performance multiorgan segmentation for 2 popular PET tracers without the use of CT information. These models may tackle the limitations of using CT segmentation in PET/CT image quantification, kinetic modeling, radiomics analysis, dosimetry, or any other tasks that require organ segmentation masks.

Using space-filling curves and fractals to reveal spatial and temporal patterns in neuroimaging data

Objective. Magnetic resonance imaging (MRI), functional MRI (fMRI) and other neuroimaging techniques are routinely used in medical diagnosis, cognitive neuroscience or recently in brain decoding. They produce three- or four-dimensional scans reflecting the geometry of brain tissue or activity, which is highly correlated temporally and spatially. While there exist numerous theoretically guided methods for analyzing correlations in one-dimensional data, they often cannot be readily generalized to the multidimensional geometrically embedded setting.Approach. We present a novel method, Fractal Space-Curve Analysis (FSCA), which combines Space-Filling Curve (SFC) mapping for dimensionality reduction with fractal Detrended Fluctuation Analysis. We conduct extensive feasibility studies on diverse, artificially generated data with known fractal characteristics: the fractional Brownian motion, Cantor sets, and Gaussian processes. We compare the suitability of dimensionality reduction via Hilbert SFC and a data-driven alternative. FSCA is then successfully applied to real-world MRI and fMRI scans.Main results. The method utilizing Hilbert curves is optimized for computational efficiency, proven robust against boundary effects typical in experimental data analysis, and resistant to data sub-sampling. It is able to correctly quantify and discern correlations in both stationary and dynamic two-dimensional images. In MRI Alzheimer's dataset, patients reveal a progression of the disease associated with a systematic decrease of the Hurst exponent. In fMRI recording of breath-holding task, the change in the exponent allows distinguishing different experimental phases.Significance. This study introduces a robust method for fractal characterization of spatial and temporal correlations in many types of multidimensional neuroimaging data. Very few assumptions allow it to be generalized to more dimensions than typical for neuroimaging and utilized in other scientific fields. The method can be particularly useful in analyzing fMRI experiments to compute markers of pathological conditions resulting from neurodegeneration. We also showcase its potential for providing insights into brain dynamics in task-related experiments.

Dynamic simulation imaging for complex atmospheric turbulence scenes based on atmospheric coherence length and associated with phase structure function

Dynamic simulation imaging for complex atmospheric turbulence scenes based on atmospheric coherence length and associated with phase structure function

Model-based perfusion reconstruction with time separation technique in cone-beam CT dynamic liver perfusion imaging.

The success of embolization, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan to inspect the contrast agentflow. The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT. Therefore, a model-based perfusion reconstruction based on the time separation technique (TST) was applied. TST uses basis functions to model time attenuation curves. These functions are either analytical or based on prior knowledge (PK), extracted using singular value decomposition of the classical CT perfusion data of animal subjects. To explore how well the PK can model perfusion dynamics and what the potential limitations are, the dynamic cone-beam CT (CBCT) perfusion scan was simulated from a dynamic CT perfusion scan under different noise levels. The TST method was compared to staticreconstruction. It was demonstrated on this simulated dynamic CBCT perfusion scan that a set consisting of only four basis functions results in perfusion maps that preserve relevant information, denoise the data, and outperform static reconstruction under higher noise levels. TST with PK would not only outperform static reconstruction but also the TST with analytical basis functions. Furthermore, it has been shown that only eight CBCT rotations, unlike previously assumed ten, are sufficient to obtain the perfusion maps comparable to the reference CT perfusion maps. This contributes to saving dose and reconstruction time. The real dynamic CBCT perfusion scan, reconstructed under the same conditions as the simulated scan, shows potential for maintaining the accuracy of the perfusion maps. By visual inspection, the embolized region was matching to that in corresponding CT perfusionmaps. CBCT reconstruction of perfusion scan data using the TST method has shown promising potential, outperforming static reconstructions and potentially saving dose by reducing the necessary number of acquisition sweeps. Further analysis of a larger cohort of patient data is needed to draw final conclusions regarding the expected advantages of theTST.