Soft Tissue Contrast Research Articles

Magnetic resonance imaging of the dromedary camel carpus

BackgroundThe dromedary camel (Camelus dromedarius) carpal joint presents multiple joints and constitutes several bones and soft tissues. Radiography and/or ultrasonography of the carpus are challenging due to structural superimposition. High-field magnetic resonance imaging (MRI) technique precludes superimposed tissues and offers high soft tissue contrast in multiple sequences and planes. Hence, understanding the normal MRI anatomy is crucial during clinical investigations. Magnetic resonance imaging is highly sensitive for investigation of soft tissues and articular cartilage; therefore, it is extensively used for outlining joint anatomy and evaluation of a wide range of musculoskeletal conditions. MRI images of a specific anatomical region acquired by using multiple sequences in various planes are necessary for a complete MRI examination. Given the dearth of information on the MRI features of the dromedary camel carpus, the current study demonstrates the MRI appearance of the clinically significant structures in the camel carpus in various sequences and planes using a high-field 1.5 Tesla superconducting magnet. For this purpose, twelve cadaveric forelimbs, obtained from 6 clinically sound lameness free adult dromedary camels, were examined.ResultsThe cortex and medulla of the radius, carpal bones and metacarpus were evaluated. Articular cartilage of the carpal joints was depicted and showed intermediate intensity. Carpal tendons expressed lower signal intensity in all pulse sequences. The collateral and inter-carpal ligaments showed mixed signal intensity.ConclusionsThe obtained data outlines the validation of MRI for investigation of the camel carpus and could set as a reference for interpretation in clinical patients.

Free-Running Cardiac and Respiratory Motion-Resolved Imaging: A Paradigm Shift for Managing Motion in Cardiac MRI?

Cardiac magnetic resonance imaging (MRI) is widely used for non-invasive assessment of cardiac morphology, function, and tissue characteristics due to its exquisite soft-tissue contrast. However, it remains time-consuming and requires proficiency, making it costly and limiting its widespread use. Traditional cardiac MRI is inefficient as signal acquisition is often limited to specific cardiac phases and requires complex view planning, parameter adjustments, and management of both respiratory and cardiac motion. Recent efforts have aimed to make cardiac MRI more efficient and accessible. Among these innovations, the free-running framework enables 5D whole-heart imaging without the need for an electrocardiogram signal, respiratory breath-holding, or complex planning. It uses a fully self-gated approach to extract cardiac and respiratory signals directly from the acquired image data, allowing for more efficient coverage in time and space without the need for electrocardiogram gating, triggering, navigators, or breath-holds. This review provides a comprehensive overview of the free-running framework, detailing its history, concepts, recent improvements, and clinical applications.

Electrophysiological Cardiovascular Magnetic Resonance (EP-CMR)-Guided Interventional Procedures: Challenges and Opportunities.

Cardiovascular magnetic resonance (CMR) imaging excels in providing detailed three-dimensional anatomical information together with excellent soft tissue contrast and has already become a valuable tool for diagnostic evaluation, electrophysiological procedure (EP) planning, and therapeutical stratification of atrial or ventricular rhythm disorders. CMR-based identification of ablation targets may significantly impact existing concepts of interventional electrophysiology. In order to exploit the inherent advantages of CMR imaging to the fullest, CMR-guided ablation procedures (EP-CMR) are justly considered the ultimate goal. Electrophysiological cardiovascular magnetic resonance (EP-CMR) interventional procedures have more recently been introduced to the CMR armamentarium: in a single-center series of 30 patients, an EP-CMR guided ablation success of 93% has been reported, which is comparable to conventional ablation outcomes for typical atrial flutter and procedure and ablation time were also reported to be comparable. However, moving on from already established workflows for the ablation of typical atrial flutter in the interventional CMR environment to treatment of more complex ventricular arrhythmias calls for technical advances regarding development of catheters, sheaths and CMR-compatible defibrillator equipment. CMR imaging has already become an important diagnostic tool in the standard clinical assessment of cardiac arrhythmias. Previous studies have demonstrated the feasibility and safety of performing electrophysiological interventional procedures within the CMR environment and fully CMR-guided ablation of typical atrial flutter can be implemented as a routine procedure in experienced centers. Building upon established workflows, the market release of new, CMR-compatible interventional devices may finally enable targeting ventricular arrhythmias.

Predicting liver ablation volumes with real-time MRI thermometry

Background & AimsMRI guidance offers better lesion targeting for microwave ablation of liver lesions with higher soft-tissue contrast, as well as the possibility of real-time thermometry. This study aims to evaluate the correlation of real-time MR thermometry-predicted lesion volume with the ablation zone in postprocedural first-day images. Materials and MethodsThis single center retrospective analysis evaluated prospectively collected patients who underwent MRI-guided microwave ablation with real-time thermometry between December 2020 and July 2023. All procedures were performed in general anesthesia on a 1.5T MRI scanner. Real-time thermometry data were acquired using multi-slice gradient-echo EPI sequences, and thermal dose (CEM43 of 240 minutes as a threshold) maps were created. The volume of tissue exposed to lethal thermal dose in MR thermometry (thermal dose) was compared with the ablation zone volume in portal phase T1w MRI on postprocedural first-day using Pearson correlation test, and visual quantitative assessment by radiologists was performed to evaluate the similarity of shapes and volumes. ResultsOut of 30 patients with 33 lesions with thermometry images, six (18.1%) lesions were excluded due to artifacts limiting interpretation of thermal dose volume. Twenty-four patients with 27 lesions (20 male, age 63.1±9.1) were evaluated for the volume correlation. The volume of thermal dose-predicted lesions and the postprocedural first-day ablation zones showed a strong correlation (R=0.89, p<0.001). Similarly, visual similarity of MR thermometry-predicted shape and the ablation zone shape was graded as perfect in 23 (85.1%) lesions. ConclusionsReal-time thermal dose-predicted volumes show very good correlation with the ablation zone volumes in images obtained one day after the procedure, which could reduce the local recurrence rates with the possibility of reablating lesions within the same procedure.

CT- and MR-based image-based data mining are consistent in the brain

PurposeImage-based data mining (IBDM) is a voxel-based analysis technique to investigate dose–response. Most often, IBDM uses radiotherapy planning CTs because of their broad accessibility, however, it was unknown whether CT provided sufficient soft tissue contrast for brain IBDM. This study evaluates whether MR-based IBDM improves upon CT-based IBDM for studies of children with brain tumours. MethodsWe compared IBDM pipelines using either CT- or MRI-based spatial normalisation in 128 children (ages 3.3–19.7 years) who received photon radiotherapy for primary brain tumours at a single institution. We quantified spatial-normalisation accuracy using contour comparison measures (centre-of-mass separation, average contour distance-to-agreement (DTavg), and Hausdorff distance) at multiple anatomic loci. We performed an end-to-end test of CT- and MRI-IBDM using modified clinical dose distributions and simulated effect labels to detect associations in pre-defined anatomic loci. Accuracy was assessed via sensitivity and specificity. ResultsSpatial normalisation accuracy was comparable for both modalities, with a significant but small improvement for MRI compared to CT in all structures except the brainstem. The median (range) difference between the DTavg for the two pipelines was 0.37 (0.00–2.91) mm. The end-to-end test revealed no significant difference in sensitivity of the IBDM-identified regions for the two pipelines. Specificity slightly improved for MR-IBDM at the 99% significance level. ConclusionCT-based IBDM was comparable to MR-based IBDM, despite a small advantage in spatial normalisation accuracy with MRI. The use of CT-IBDM over MR-IBDM is useful for multi-institutional retrospective IBDM studies, where the availability of standardised MRI data can be limited.

The role of MRI in perianal fistulizing disease: diagnostic imaging and classification systems to monitor disease activity.

Perianal fistulizing disease, commonly associated with Crohn's disease, poses significant diagnostic and therapeutic challenges due to its complex anatomy and high recurrence rates. Radiological imaging plays a pivotal role in the accurate diagnosis, classification, and management of this condition. This article reviews the current radiological modalities employed in the evaluation of perianal fistulizing disease, including magnetic resonance imaging (MRI), endoanal ultrasound, and computed tomography (CT). MRI, recognized as the gold standard, offers superior soft tissue contrast and multiplanar capabilities, facilitating detailed assessment of fistula tracts and associated abscesses. CT, although less detailed than MRI, remains valuable in acute settings for detecting abscesses and guiding drainage procedures. This article discusses the advantages and limitations of each modality, highlights the importance of standardized imaging protocols, and underscores the need for interdisciplinary collaboration in the management of perianal fistulizing disease. Future directions include advancements in imaging techniques and the integration of artificial intelligence to enhance diagnostic accuracy and treatment outcomes.

The dilemma of mediastinal lymphadenopathy between invasive and non-invasive procedures: ROC analysis of T2WI and DWI-MRI advanced parameters correlated with PET-CT and biopsy

BackgroundThe characterization of pathologically enlarged mediastinal lymph nodes is clinically essential for effective disease management and accurate prognosis. Malignancy (metastases and lymphoma) and granulomatous conditions (sarcoidosis and tuberculosis) are the most common causes. Magnetic resonance imaging (MRI) is a good modality to characterize the mediastinal pathologically enlarged lymph nodes based on the excellent soft tissue contrast. It can save high-risk patients from radiation exposure and hazards of intervention such as general anesthesia and biopsy.Aim of the workTo estimate the accuracy of different advanced MRI quantitative parameters in the differentiation between benign and malignant mediastinal lymphadenopathy. This would involve the lesion-to-cord signal intensity ratio (SIR) in the T2-WI and diffusion weighted image (DWI), the lesion-to-chest wall muscle SIR in T2-WI and DWI, and the mean apparent diffusion coefficient (ADC) values. These values would be correlated with the prospective pathological data and the results of the positron emission tomography (PET-CT).ResultsProspectively, the study was conducted during the period between June 2022 and September 2023 on 45 patients with indeterminate or suspicious mediastinal lymphadenopathy identified by CT. MRI examination, PET-CT, and biopsy were applied for all patients. The intra-class correlation coefficient ranged between 0.89 and 0.95. (A) The lesion-to-cord SIR in T2-WI in the malignant group (1.49 ± 0.30) was higher than that in the benign group (0.83 ± 0.24) with P < 0.001. The statistically calculated cutoff value (> 1.2) estimated 90% sensitivity, 100% specificity, with AUC = 0.989. (B) The lesion-to-chest wall muscle SIR in T2-WI in malignant nodes (3.13 ± 0.84) was significantly higher than that in the benign nodes (1.90 ± 0.80) with P < 0.001. The statistically calculated cutoff value (> 2.4) estimated 86.67% sensitivity, 86.67% specificity, with AUC = 0.88. (C) The lesion-to-cord signal SIR in b500-DWI in the malignant node (1.80 ± 0.54) was higher than that in the benign group (0.75 ± 0.29) with P < 0.001. The statistically calculated cutoff value (> 1.2) estimated 100% sensitivity and specificity with AUC = 1.00. (D) The lesion-to-chest wall SIR in b500-DWI in the malignant node (6.43 ± 1.28) was higher than that in the benign node (2.63 ± 0.94) with P < 0.001. The statistically calculated cutoff value (> 4.1) estimated 96.67% sensitivity, 100% specificity, with AUC = 0.997. € The mean ADC values in the malignant nodes (0.83 ± 0.20 × 10–3 mm2/s) were significantly lower than that in the benign nodes (1.85 ± 0.19 × 10−3 mm2/s) with P < 0.001. The statistically calculated cutoff value (≤ 1.2) estimated 100% sensitivity and specificity with AUC = 1.00.ConclusionsBiopsy remains the main diagnostic modality for the characterization of mediastinal lymphadenopathy despite its hazards and limitations. This study proved that MRI could be accepted as an alternative non-invasive imaging tool that can overcome the PET-CT limitations. The lesion signal-to-cord and to-muscle ratios in both T2-WI and DWI-MRI side by side with the mean ADC value showed high statistical accuracy.

On-board synthetic 4D MRI generation from 4D CBCT for radiotherapy of abdominal tumors: A feasibility study.

Magnetic resonance-guided radiotherapy with an MR-guided LINAC represents potential clinical benefits in abdominal treatments due to the superior soft-tissue contrast compared to kV-based images in conventional treatment units. However, due to the high cost associated with this technology, only a few centers have access to it. As an alternative, synthetic 4D MRI generation based on artificial intelligence methods could be implemented. Nevertheless, appropriate MRI texture generation from CT images might be challenging and prone to hallucinations, compromising motion accuracy. To evaluate the feasibility of on-board synthetic motion-resolved 4D MRI generation from prior 4D MRI, on-board 4D cone beam CT (CBCT) images, motion modeling information, and deep learning models using the digital anthropomorphic phantom XCAT. The synthetic 4D MRI corresponds to phases from on-board 4D CBCT. Each synthetic MRI volume in the 4D MRI was generated by warping a reference 3D MRI (MRIref, end of expiration phase from a prior 4D MRI) with a deformation field map (DFM) determined by (I) the eigenvectors from the principal component analysis (PCA) motion-modeling of the prior 4D MRI, and (II) the corresponding eigenvalues predicted by a convolutional neural network (CNN) model using the on-board 4D CBCT images as input. The CNN was trained with 1000 deformations of one reference CT (CTref, same conditions as MRIref) generated by applying 1000 DFMs computed by randomly sampling the original eigenvalues from the prior 4D MRI PCA model. The evaluation metrics for the CNN model were root-mean-square error (RMSE) and mean absolute error (MAE). Finally, different on-board 4D-MRI generation scenarios were assessed by changing the respiratory period, the amplitude of the diaphragm, and the chest wall motion of the 4D CBCT using normalized root-mean-square error (nRMSE) and structural similarity index measure (SSIM) for image-based evaluation, and volume dice coefficient (VDC), volume percent difference (VPD), and center-of-mass shift (COMS) for contour-based evaluation of liver and target volumes. The RMSE and MAE values of the CNN model reported 0.012±0.001 and 0.010±0.001, respectively for the first eigenvalue predictions. SSIM and nRMSE were 0.96±0.06 and 0.22±0.08, respectively. VDC, VPD, and COMS were 0.92±0.06, 3.08±3.73 %, and 2.3±2.1mm, respectively, for the target volume. The more challenging synthetic 4D-MRI generation scenario was for one 4D-CBCT with increased chest wall motion amplitude, reporting SSIM and nRMSE of 0.82 and 0.51, respectively. On-board synthetic 4D-MRI generation based on predicting actual treatment deformation from on-board 4D-CBCT represents a method that can potentially improve the treatment-setup localization in abdominal radiotherapy treatments with a conventional kV-based LINAC.

Phosphotungstic acid-enhanced micro-computed tomography and RNA sequencing provide a new perspective on temporomandibular joint arthritis induced by complete Freund's adjuvant and collagen-induced arthritis in rat models

Background/PurposeTemporomandibular joint (TMJ) arthritis causes inflammation and degradation of the mandibular condylar cartilage and subchondral bone. Complete Freund's adjuvant (CFA) and collagen-induced arthritis (CIA) are models for studying TMJ arthritis. While micro-computed tomography (micro-CT) is crucial for three-dimensional (3D) bone analysis, it has limitations in imaging nonmineralized tissues. Phosphotungstic acid (PTA) enhances soft tissue contrast. However, research on the 3D imaging of mandibular condylar cartilage and the molecular mechanisms of CFA- and CIA-induced arthritis remains unclear. This study aimed to investigate the bone and PTA-stained cartilage in the mandibular condyle using 3D reconstruction and explore the characteristics of enriched gene ontology terms underlying CFA- and CIA-induced TMJ arthritis in rat models. Materials and methodsRat mandibular condyles were collected from control, CFA, and CIA groups. Live micro-CT created 3D bone structures, and PTA-enhanced micro-CT constructed 3D mandibular condylar cartilage. Gene ontology enrichment analysis identified enriched gene ontology terms from differentially expressed genes through RNA sequencing. ResultsMajor deformities in cartilage volume and bone morphology were observed in the arthritis-induced groups. The CIA group exhibited significant correlations between cartilage volume and bone parameters changes. Gene ontology enrichment analysis indicated fewer terms with upregulated differentially expressed genes related to inflammation and immune response in the CIA group than in the CFA group. ConclusionThis study reveals distinct responses between CFA- and CIA-induced TMJ arthritis models. The CIA group exhibited strong correlations between cartilage volume and bone parameter changes and had less pronounced inflammation and immune response than the CFA group.

Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy

Purpose objective(s)Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques.Materials/methodsWe used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT.ResultsThe average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques.ConclusionResults demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning.

Ultrashort Echo Time and Fast Field Echo Imaging for Spine Bone Imaging with Application in Spondylolysis Evaluation

Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures.

Spatial-aware contrastive learning for cross-domain medical image registration.

With the rapid advancement of medical imaging technologies, precise image analysis and diagnosis play a crucial role in enhancing treatment outcomes and patient care. Computed tomography (CT) and magnetic resonance imaging (MRI), as pivotal technologies in medical imaging, exhibit unique advantages in bone imaging and soft tissue contrast, respectively. However, cross-domain medical image registration confronts significant challenges due to the substantial differences in contrast, texture, and noise levels between different imaging modalities. The purpose of this study is to address the major challenges encountered in the field of cross-domain medical image registration by proposing a spatial-aware contrastive learning approach that effectively integrates shared information from CT and MRI images. Our objective is to optimize the feature space representation by employing advanced reconstruction and contrastive loss functions, overcoming the limitations of traditional registration methods when dealing with different imaging modalities. Through this approach, we aim to enhance the model's ability to learn structural similarities across domain images, improve registration accuracy, and provide more precise imaging analysis tools for clinical diagnosis and treatmentplanning. With prior knowledge that different domains of images (CT and MRI) share same content-style information, we extract equivalent feature spaces from both images, enabling accurate cross-domain point matching. We employ a structure resembling that of an autoencoder, augmented with designed reconstruction and contrastive losses to fulfill our objectives. We also propose region mask to solve the conflict between spatial correlation and distinctiveness, to obtain a better representation space. Our research results demonstrate the significant superiority of the proposed spatial-aware contrastive learning approach in the domain of cross-domain medical image registration. Quantitatively, our method achieved an average Dice similarity coefficient (DSC) of 85.68%, target registration error (TRE) of 1.92 mm, and mean Hausdorff distance (MHD) of 1.26 mm, surpassing current state-of-the-art methods. Additionally, the registration processing time was significantly reduced to 2.67 s on a GPU, highlighting the efficiency of our approach. The experimental outcomes not only validate the effectiveness of our method in improving the accuracy of cross-domain image registration but also prove its adaptability across different medical image analysis scenarios, offering robust support for enhancing diagnostic precision and patient treatmentoutcomes. The spatial-aware contrastive learning approach proposed in this paper introduces a new perspective and solution to the domain of cross-domain medical image registration. By effectively optimizing the feature space representation through carefully designed reconstruction and contrastive loss functions, our method significantly improves the accuracy and stability of registration between CT and MRI images. The experimental results demonstrate the clear advantages of our approach in enhancing the accuracy of cross-domain image registration, offering significant application value in promoting precise diagnosis and personalized treatment planning. In the future, we look forward to further exploring the application of this method in a broader range of medical imaging datasets and its potential integration with other advanced technologies, contributing more innovations to the field of medical image analysis andprocessing.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning.Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans.The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT.SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Diagnostics Accuracy of Magnetic Resonance Imaging in Detection of Atherosclerotic Plaque Characteristics in Carotid Arteries Compared to Histology: A Systematic Review.

Carotid plaque composition represents one of the main risk factors of future ischemic stroke. MRI provides excellent soft tissue contrast that can distinguish plaque characteristics. Our objective was to analyze the diagnostic accuracy of MRI imaging in the detection of carotid plaque characteristics compared to histology in patients with symptomatic and asymptomatic carotid atherosclerosis through a systematic review. After prospective registration in PROSPERO (ID CRD42022329690), Medline Ovid, Embase.com, Cochrane Library, and Web of Science Core were searched without any search limitation up to May 27, 2022 to identify eligible articles. Of the 8168 studies, 53 (37 × 1.5 T MRI, 17 × 3 T MRI) evaluated MRI accuracy in the detection of 13 specific carotid plaque characteristics in 169 comparisons. MRI demonstrated high diagnostic accuracy for detection of calcification (3 T MRI: mean sensitivity 92%/mean specificity 90%; 1.5 T MRI: mean sensitivity 81%/mean specificity 91%), fibrous cap (1.5 T: 89%/87%), unstable plaque (1.5 T: 89%/87%), intraplaque hemorrhage (1.5 T: 86%/88%), and lipid-rich necrotic core (1.5 T: 89%/79%). MRI also proved to have a high level of tissue discrimination for the carotid plaque characteristics investigated, allowing potentially for a better risk assessment and follow-up of patients who may benefit from more aggressive treatments. These results emphasize the role of MRI as the first-line imaging modality for comprehensive assessment of carotid plaque morphology, particularly for unstable plaque. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

In vivo bioluminescence tomography-guided system for pancreatic cancer radiotherapy research.

Recent development of radiotherapy (RT) has heightened the use of radiation in managing pancreatic cancer. Thus, there is a need to investigate pancreatic cancer in a pre-clinical setting to advance our understanding of the role of RT. Widely-used cone-beam CT (CBCT) imaging cannot provide sufficient soft tissue contrast to guide irradiation. The pancreas is also prone to motion. Large collimation is unavoidably used for irradiation, costing normal tissue toxicity. We innovated a bioluminescence tomography (BLT)-guided system to address these needs. We established an orthotopic pancreatic ductal adenocarcinoma (PDAC) mouse model to access BLT. Mice underwent multi-projection and multi-spectral bioluminescence imaging (BLI), followed by CBCT imaging in an animal irradiator for BLT reconstruction and radiation planning. With optimized absorption coefficients, BLT localized PDAC at 1.25 ± 0.19 mm accuracy. To account for BLT localization uncertainties, we expanded the BLT-reconstructed volume with margin to form planning target volume(PTVBLT) for radiation planning, covering 98.7 ± 2.2% of PDAC. The BLT-guided conformal plan can cover 100% of tumors with limited normal tissue involvement across both inter-animal and inter-fraction cases, superior to the 2D BLI-guided conventional plan. BLT offers unique opportunities to localize PDAC for conformal irradiation, minimize normal tissue involvement, and support reproducibility in RT studies.

Integrating muscle fiber orientation from visible human data into radiotherapy target volumes.

Objective.A major challenge in treatment of tumors near skeletal muscle is defining the target volume for suspected tumor invasion into the muscle. This study develops a framework that generates radiation target volumes with muscle fiber orientation directly integrated into their definition. The framework is applied to nineteen sacral tumor patients with suspected infiltration into surrounding muscles.Approach.To compensate for the poor soft-tissue contrast of CT images, muscle fiber orientation is derived from cryo-images of two cadavers from the human visible project (VHP). The approach consists of (a) detecting image gradients in the cadaver images representative of muscle fibers, (b) mapping this information onto the patient image, and (c) embedding the muscle fiber orientation into an expansion method to generate patient-specific clinical target volumes (CTV). The validation tested the consistency of image gradient orientation across VHP subjects for the piriformis, gluteus maximus, paraspinal, gluteus medius, and gluteus minimus muscles. The model robustness was analyzed by comparing CTVs generated using different VHP subjects. The difference in shape between the new CTVs and standard CTV was analyzed for clinical impact.Main results.Good agreement was found between the image gradient orientation across VHP subjects, as the voxel-wise median cosine similarity was at least 0.86 (for the gluteus minimus) and up to 0.98 for the piriformis. The volume and surface similarity between the CTVs generating from different VHP subjects was on average at least 0.95 and 5.13 mm for the Dice Similarity Coefficient and the Hausdorff 95% Percentile Index, showing excellent robustness. Finally, compared to the standard CTV with different margins in muscle and non-muscle tissue, the new CTV margins are reduced in muscle tissue depending on the chosen clinical margins.Significance.This study implements a method to integrate muscle fiber orientation into the target volume without the need for additional imaging.

Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm

Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.

Reconstruction of Tomographic Size Using a Filtered Back Projection Algorithm

Due to their widespread use, image processors are among the most essential computer processors. FBP technique is one of the most widely used tomographic reconstruction methods. Images from a computed tomography (CT) scanner with 512x512 pixels of raw data for parallel beam projections are the metadata to which this approach is applied. This work is divided into three parts. Several projections are created from an input test picture termed Shepp-Logan phantom in the first section, utilizing radon transformation. The picture is rebuilt from the projection of a parallel beam in the second section to minimize reconstruction time. Finally, the proposed system filters the projections using the Ram Lak filter and the Hann window in the third section to refine the picture and then recombine the projections to generate the reconstructed image. The reconstruction procedure is performed on CT images using a filtered back-projection algorithm in this article for image optimization and then projection recombination to generate the rebuilt picture. Since CT can accurately depict anatomical elements like bones and organs and may pick slides in any orientation, it is frequently used in biological applications and diagnostics. It also offers a great spatial resolution and soft tissue contrast. We develop, create, and run computer programs when we work with MATLAB. After projecting the 2D image onto a CT scanner, a very good result in reconstructing the image is received.

Decision-Tree Models Indicative of Microvascular Invasion on MRI Predict Survival in Patients with Hepatocellular Carcinoma Following Tumor Ablation.

Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE). Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher's exact and t-test, Kaplan-Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson's correlation. OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed. The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.

Deep learning for MRI lesion segmentation in rectal cancer.

Rectal cancer (RC) is a globally prevalent malignant tumor, presenting significant challenges in its management and treatment. Currently, magnetic resonance imaging (MRI) offers superior soft tissue contrast and radiation-free effects for RC patients, making it the most widely used and effective detection method. In early screening, radiologists rely on patients' medical radiology characteristics and their extensive clinical experience for diagnosis. However, diagnostic accuracy may be hindered by factors such as limited expertise, visual fatigue, and image clarity issues, resulting in misdiagnosis or missed diagnosis. Moreover, the distribution of surrounding organs in RC is extensive with some organs having similar shapes to the tumor but unclear boundaries; these complexities greatly impede doctors' ability to diagnose RC accurately. With recent advancements in artificial intelligence, machine learning techniques like deep learning (DL) have demonstrated immense potential and broad prospects in medical image analysis. The emergence of this approach has significantly enhanced research capabilities in medical image classification, detection, and segmentation fields with particular emphasis on medical image segmentation. This review aims to discuss the developmental process of DL segmentation algorithms along with their application progress in lesion segmentation from MRI images of RC to provide theoretical guidance and support for further advancements in this field.