Computed Tomography Magnetic Resonance Imaging Research Articles

Childhood Solid Pseudopapillary Neoplasm of the Pancreas: Acute Presentation of an Uncommon Tumor

AbstractSolid pseudopapillary tumor is a mixed solid and cystic neoplasm of the pancreas which occurs most commonly in females in second or third decade of life with few reported pediatric cases. It has a low malignancy potential with good prognosis after surgical resection.We present a case of an 11-year-old male child with solid pseudopapillary tumor of pancreas who presented with clinical features of acute pancreatitis. He was initially evaluated with ultrasound, followed by computed tomography (CT) and magnetic resonance imaging (MRI).Solid pseudopapillary tumor of pancreas presenting as acute pancreatitis is rarely reported in literature. This case is a unique presentation of a pancreatic tumor and highlights the value of multiphase CT imaging and MRI in the diagnosis.

Image Compression Using Improved Method

Effective use of digital images requires certain techniques to lower the number of bits needed to represent them. The goal of compression is to reduce the size of an image while maintaining its information and originality. This research aims to achieve this target by developing a fuzzy logic and histogram-based image compression type detection approach. The main goal is to provide an approach that employs fuzzy logic and graphs for detecting the type of image compression. In order to decide the compression technique depending on the number of color levels in the image which was recovered with the use of histogram, the suggested technique combines a new combination of fuzzy logic as well as image histogram analysis. The experimental findings demonstrated the suggested technique's robustness and efficiency in detecting the best approach for each type of image compression. After 75 images with varying color densities were evaluated, the system's compression rate was approximately 95%. In addition to being a significant contribution to image processing, the study finds practical applications in the following areas: remote sensing through satellite; facsimile transmission of medical images in computer tomography magnetic resonance imaging; teleconferencing systems; military communication systems via radars; geological surveys; and communications systems built by computers.

Inter-reader Agreement for CT/MRI LI-RADS Category M Imaging Features: A Systematic Review and Meta-analysis.

To systematically evaluate inter-reader agreement in the assessment of individual Liver Imaging Reporting and Data System (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LI-RADS v2018, and to explore the causes of poor agreement in LR-M assignment. Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using Cochran's Q test and I2 statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity. In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval, 0.65-0.78) for rim arterial phase hyperenhancement, 0.52 (0.39-0.65) for peripheral washout, 0.60 (0.50-0.70) for delayed central enhancement, 0.68 (0.57-0.78) for targetoid restriction, 0.74 (0.65-0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (0.49-0.78) for infiltrative appearance, 0.49 (0.30-0.68) for marked diffusion restriction, and 0.61 (0.48-0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test: p < 0.01; I2 ≥ 89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features. The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.

Neurogenic heterotopic ossification of the hip: Magnetic resonance imaging versus computed tomography for pre-surgical assessment

PurposeNeurogenic heterotopic ossification (NHO) of the hip is a frequent complication of spinal cord injuries, often requiring surgical management. Pre-surgical imaging assessment is essential, usually with computed tomography (CT)-scan. We aimed to compare magnetic resonance imaging (MRI) and CT for pre-surgical imaging assessment of the NHO, particularly for their relationships with vessels and nerves. MethodThis prospective study included consecutive patients who underwent surgery for NHO from July 2019 to April 2022. All patients had CT angiography and MRI including Zero Echo Time and TRICKS sequences. Radiologists used standardized reports for CT and MRI to evaluate NHO and their features, bone mineralization, and relation to the arteries, veins and nerves. Agreement between pre-surgical CT and MRI was evaluated. ResultsTwenty-four patients (mean age: 53.5 ± 12.2 years) were included, among which 7 had bilateral NHO (31 hips). NHO were anterior in 15/31 hips (48 %), multifragmented in 25/31 hips (81 %). Mild and significant demineralization was most frequent. Gutter and tunnel were reported in 11.1 % of the arteries. Nerves were more often identified in MRI than in CT-scan. Agreement coefficients between CT and MRI were excellent for NHO location (0.95) and implantation (0.92), good for fragmentation (0.70), contact with joint capsule (0.66), bone mineralization (0.74), and relation to arteries (0.85), veins (0.76), sciatic nerve (0.7) and moderate for femoral nerve (0.47). ConclusionMRI exhibited a good agreement with CT for pre-surgical assessment of NHO of the hip, especially to evaluate their relationships with the arteries, veins and sciatic nerve. Femoral nerves were more often identified in MRI than in CT-scan.

From technical to understandable: Artificial Intelligence Large Language Models improve the readability of knee radiology reports.

The purpose of this study was to evaluate the effectiveness of an Artificial Intelligence-Large Language Model (AI-LLM) at improving the readability of knee radiology reports. Reports of 100 knee X-rays, 100 knee computed tomography (CT) scans and 100 knee magnetic resonance imaging (MRI) scans were retrieved. The following prompt command was inserted into the AI-LLM: 'Explain this radiology report to a patient in layman's terms in the second person:[Report Text]'. The Flesch-Kincaid reading level (FKRL) score, Flesch reading ease (FRE) score and report length were calculated for the original radiology report and the AI-LLM generated report. Any 'hallucination' or inaccurate text produced by the AI-LLM-generated report was documented. Statistically significant improvements in mean FKRL scores in the AI-LLM generated X-ray report (12.7 ± 1.0-7.2 ± 0.6), CT report (13.4 ± 1.0-7.5 ± 0.5) and MRI report (13.5 ± 0.9-7.5 ± 0.6) were observed. Statistically significant improvements in mean FRE scores in the AI-LLM generated X-ray report (39.5 ± 7.5-76.8 ± 5.1), CT report (27.3 ± 5.9-73.1 ± 5.6) and MRI report (26.8 ± 6.4-73.4 ± 5.0) were observed. Superior FKRL scores and FRE scores were observed in the AI-LLM-generated X-ray report compared to the AI-LLM-generated CT report and MRI report, p < 0.001. The hallucination rates in the AI-LLM generated X-ray report, CT report and MRI report were 2%, 5% and 5%, respectively. This study highlights the promising use of AI-LLMs as an innovative, patient-centred strategy to improve the readability of knee radiology reports. The clinical relevance of this study is that an AI-LLM-generated knee radiology report may enhance patients' understanding of their imaging reports, potentially reducing the responder burden placed on the ordering physicians. However, due to the 'hallucinations' produced by the AI-LLM-generated report, the ordering physician must always engage in a collaborative discussion with the patient regarding both reports and the corresponding images. Level IV.

Which Tests Should Be Routinely Ordered Prior to Brachial Plexus Reconstruction? Results of a Delphi Panel

Background: There are notable differences in how adult patients with traumatic brachial plexus injuries (BPI) are evaluated and treated. Purpose: To better understand existing philosophies, we used the Delphi method to measure and foster consensus on routine use of electrodiagnostic testing, ultrasound, magnetic resonance imaging (MRI), and computed tomography (CT) myelograms prior to surgery. Panelists were 10 peripheral nerve surgeons board certified in their respective specialties at 5 academic medical centers in the United States. Methods: We presented 2 cases (1 complete/pan-BPI and 1 upper trunk BPI) to panelists and asked how often they would order the following preoperative diagnostic tests: electrodiagnostic studies; ultrasound of the brachial plexus; MRI of the brachial plexus; CT myelogram. Our Delphi process included an initial survey with videoconference discussion after the first round. A second survey/videoconference round was conducted to further probe the items that did not reach consensus during the first round. Results: Among the 10 surgeons, there was consensus that prior to brachial plexus surgery electrodiagnostic studies and MRI should be routinely ordered and ultrasound of the brachial plexus should not be routinely ordered. The group did not reach consensus on whether CT myelogram should or should not be routinely ordered. Conclusions: Based on our panel’s discussion, future work should focus on comparing the accuracy of CT myelogram and MRI for evaluation of cervical nerve root presence and viability.

Investigation of Medical Image Technology Based on Big Data Neuroscience in Exercise Rehabilitation.

The purpose of this article is to combine the functional information of CT images with the anatomical and soft tissue information of MRI through image fusion technology, providing more detailed information for rehabilitation treatment and thus providing a scientific basis for clinical applications and better training plans. In this paper, functional brain imaging technology combining CT (computed tomography) and MRI (magnetic resonance imaging) was used for image fusion, and SURF (accelerated robust feature) feature points of images were extracted. In this study, 40 patients with mild and moderate closed traumatic brain injury admitted to the rehabilitation department of a rehabilitation center from 2018 to 2022 were selected as the research objects. Compared with using only CT images and MRI images for brain injury diagnosis, the fusion image had a higher detection rate of abnormal brain injury diagnosis, with a detection rate of 97.5%. When using fused images for the diagnosis of abnormal brain injury, the patient's exercise rehabilitation effect was better. CT and MRI image fusion technology had a high diagnostic accuracy for brain injury, which could timely guide doctors in determining exercise rehabilitation plans and help improve the effectiveness of patient exercise rehabilitation.

Fast field echo resembling CT using restricted echo-spacing (FRACTURE) MR sequence can provide craniocervical region images comparable to a CT in dogs.

Magnetic resonance imaging (MRI) is essential for evaluating cerebellar compression in patients with craniocervical junction abnormalities (CJA). However, it is limited in depicting cortical bone because of its short T2 relaxation times, low proton density, and organized structure. Fast field echo resembling a computed tomography (CT) scan using restricted echo-spacing (FRACTURE) MRI, is a new technique that offers CT-like bone contrast without radiation. This study aimed to assess the feasibility of using FRACTURE MRI for craniocervical junction (CCJ) assessment compared with CT and conventional MRI, potentially reducing the need for multiple scans and radiation exposure, and simplifying procedures in veterinary medicine. CT and MRI of the CCJ were obtained from five healthy beagles. MRI was performed using three-dimensional (3D) T1-weighted, T2-weighted, proton density-weighted (PDW), single echo-FRACTURE (sFRACTURE), and multiple echo-FRACTURE (mFRACTURE) sequences. For qualitative assessment, cortical delineation, trabecular bone visibility, joint space visibility, vertebral canal definition, overall quality, and artifacts were evaluated for each sequence. The geometrical accuracy, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Both sFRACTURE and CT images provided significantly higher scores for cortical delineation and trabecular bone visibility than conventional MRI. Joint space visibility and vertebral canal definition were similar to those observed on CT images, regardless of the MR sequence. In the quantitative assessment, the distances measured on T2-weighted images differed significantly from those measured on CT. There were no significant differences between the distances taken using T1-weighted, PD-weighted, sFRACTURE, mFRACTURE and those taken using CT. T1-weighted and sFRACTURE had a higher SNR for trabecular bone than CT. The CNR between the cortical bone and muscle was high on CT and FRACTURE images. However, the CNR between the cortical and trabecular bones was low in mFRACTURE. Similar to CT, FRACTURE sequences showed higher cortical delineation and trabecular bone visibility than T2-weighted, T1-weighted, and PDW CCJ sequences. In particular, sFRACTURE provided a high signal-to-noise ratio (SNR) of the trabecular bone and a high CNR between the cortical bone and muscle and between the cortical and trabecular bones. FRACTURE sequences can complement conventional MR sequences for bone assessment of the CCJ in dogs.

Assessing the Usefulness of Ultrasonography for the Diagnosis and Evaluation of Intra-Orbital Lesions in Pediatric Patients: A Retrospective Analysis.

To assess the usefulness of ultrasonography in the diagnosis and evaluation of extraocular intra-orbital lesions in pediatric patients. Twenty-three pediatric patients with intra-orbital lesions who underwent both ultrasound and computed tomography/magnetic resonance imaging (CT/MRI) were included. The following parameters were evaluated using ultrasound: 1) lesion detection rate (presence or absence of lesions), 2) lesion characteristics, 3) lesion location (extraconal or intraconal), and 4) the lesion longest linear dimensions, and these were compared using Fisher's exact test and Mann-Whitney U test. Two lesions could not be detected using ultrasound; in the other 21 cases, the lesion characteristics diagnosed by ultrasound were correct. Diagnostic accuracy of detection and characteristics assessment using ultrasound were 91.3% and 91.3%, respectively. The lesion location was not significantly different between the two groups (intraconal/extraconal in those detected using ultrasound versus those in the absence on ultrasound = 7/14 versus 0/2, P > .999); however, in two cases that were not detected on ultrasound, the lesions were located at extraconal. Lesions that were small in longest linear dimensions on CT/MRI were not detected using ultrasound (the longest linear dimensions in lesions detected using ultrasound versus that in the absence of ultrasound: 29.5 ± 8.2 [range, 13-46] versus 10 and 11 mm, P = .043). Ultrasonography proved to be useful for visualizing and evaluating intra-orbital lesions except for lesions that were relatively small in size. Therefore, although ultrasound could not detect lesions located behind bone and bone invasion, it could be used for diagnosing and selecting treatment strategies for intra-orbital lesions.

Compromised Accuracy of Stereotactic Target Delineation Associated with Computed Tomography-Based Frame Registration: A Comparative Analysis of Magnetic Resonance Imaging-Computed Tomography Fusion

Introduction: Recent advancements in stereotactic neurosurgical techniques have become increasingly reliant on image-based target planning. We devised a case-phantom comparative analysis to evaluate the target registration errors arising during the magnetic resonance imaging (MRI)-computed tomography (CT) image fusion process. Methods: For subjects whose preoperative MRI and CT images both contained fiducial frame localizers, we investigated discrepancies in target coordinates derived from frame registration based on either MRI or CT. We generated a phantom target through an image fusion process, merging the framed CT images with their corresponding reference MRIs after masking their fiducial indicators. This phantom target was then compared with the original during each instance of target planning. Results: In our investigative study with 26 frame registrations, a systematic error in the y-axis was observed as −0.89 ± 0.42 mm across cases using either conventional CT and/or cone-beam CT (O-arm). For the z-axis, errors varied on a case-by-case basis, recording at +0.64 ± 1.09 mm with a predominant occurrence in those merged with cone-beam CT. Collectively, these errors resulted in an average Euclidean error of 1.33 ± 0.93 mm. Conclusion: Our findings suggest that the accuracy of frame-based stereotactic planning is potentially compromised during MRI-CT fusion process. Practitioners should recognize this issue, underscoring a pressing need for strategies and advancements to optimize the process.

MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network

PurposeThis study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time.MethodsWe used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Content, style, and L1 losses were used for training the generator to preserve the texture and structure information of the MRI and CT images.ResultsThe MedFusionGAN successfully generates fused images with MRI soft-tissue and CT bone contrast. The results of the MedFusionGAN were quantitatively and qualitatively compared with seven traditional and eight deep learning (DL) state-of-the-art methods. Qualitatively, our method fused the source images with the highest spatial resolution without adding the image artifacts. We reported nine quantitative metrics to quantify the preservation of structural similarity, contrast, distortion level, and image edges in fused images. Our method outperformed both traditional and DL methods on six out of nine metrics. And it got the second performance rank for three and two quantitative metrics when compared with traditional and DL methods, respectively. To compare soft-tissue contrast, intensity profile along tumor and tumor contours of the fusion methods were evaluated. MedFusionGAN provides a more consistent, better intensity profile, and a better segmentation performance.ConclusionsThe proposed end-to-end unsupervised method successfully fused MRI and CT images. The fused image could improve targets and OARs delineation, which is an important aspect of radiotherapy treatment planning.

Complementary Role of CEUS and CT/MR LI-RADS for Diagnosis of Recurrent HCC.

We retrospectively compared the diagnostic performance of contrast-enhanced ultrasonography (CEUS) and contrast-enhanced computer tomography-magnetic resonance imaging (CT/MRI) for recurrent hepatocellular carcinoma (HCC) after curative treatment. After curative treatment with 421 ultrasound (US) detected lesions, 303 HCC patients underwent both CEUS and CT/MRI. Each lesion was assigned a Liver Imaging Reporting and Data System (LI-RADS) category according to CEUS and CT/MRI LI-RADS. Receiver-operating characteristic (ROC) curves were computed to determine the optimal diagnosis algorithms for CEUS, CT and MRI. The diagnostic accuracy, sensitivity, specificity, and area under the curve (AUC) were compared between CEUS and CT/MRI. Among the 421 lesions, 218 were diagnosed as recurrent HCC, whereas 203 lesions were diagnosed as benign. In recurrent HCC, CEUS detected more arterial hyperenhancement (APHE) and washout than CT and more APHE than MRI. CEUS yielded better diagnostic performance than CT (AUC: 0.981 vs. 0.958) (p = 0.024) comparable diagnostic performance to MRI (AUC: 0.952 vs. 0.933) (p > 0.05) when using their optimal diagnostic criteria. CEUS missed 12 recurrent HCCs, CT missed one, and MRI missed none. The detection rate of recurrent HCC on CEUS (94.8%, 218/230) was lower than that on CT/MRI (99.6%, 259/260) (p = 0.001). Lesions located on the US blind spots and visualization score C would hinder the ability of CEUS to detect recurrent HCC. CEUS demonstrated excellent diagnostic performance but an inferior detection rate for recurrent HCC. CEUS and CT/MRI played a complementary role in the detection and characterization of recurrent HCC.

Two-and-a-half order score-based model for solving 3D ill-posed inverse problems

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models demonstrated effectiveness in addressing different inverse problems encountered in the field of CT and MRI, such as sparse-view CT and fast MRI reconstruction. However, these models face challenges in achieving accurate three dimensional (3D) volumetric reconstruction. The existing score-based models predominantly concentrate on reconstructing two-dimensional (2D) data distributions, resulting in inconsistencies between adjacent slices in the reconstructed 3D volumetric images. To overcome this limitation, we propose a novel two-and-a-half order score-based model (TOSM). During the training phase, our TOSM learns data distributions in 2D space, simplifying the training process compared to working directly on 3D volumes. However, during the reconstruction phase, the TOSM utilizes complementary scores along three directions (sagittal, coronal, and transaxial) to achieve a more precise reconstruction. The development of TOSM is built on robust theoretical principles, ensuring its reliability and efficacy. Through extensive experimentation on large-scale sparse-view CT and fast MRI datasets, our method achieved state-of-the-art (SOTA) results in solving 3D ill-posed inverse problems, averaging a 1.56 dB peak signal-to-noise ratio (PSNR) improvement over existing sparse-view CT reconstruction methods across 29 views and 0.87 dB PSNR improvement over existing fast MRI reconstruction methods with × 2 acceleration. In summary, TOSM significantly addresses the issue of inconsistency in 3D ill-posed problems by modeling the distribution of 3D data rather than 2D distribution which has achieved remarkable results in both CT and MRI reconstruction tasks.

Diagnostic approach to splenic lesions.

Splenic lesions are rare and mostly incidental findings on cross-sectional imaging. Most lesions are of benign nature and can be correctly identified based on imaging characteristics. Further, invasive evaluation is only necessary in cases of splenic lesions with uncertain or potentially malignant etiology. While in most cases a correct diagnosis can be made from computed tomography (CT), (additional) magnetic resonance imaging (MRI) can aid in the identification of lesions. As these lesions are rare, only a few of the differential diagnoses are regularly diagnosed in the clinical routine. This review presents the differential diagnoses of splenic lesions, including imaging characteristics and a flowchart to determine the right diagnosis. In conjunction with laboratory results and clinical symptoms, histological workup is necessary only in a few cases, especially in incidental findings. In these cases, image-guided biopsies should be preferred over splenectomy, if possible. · Splenic lesions are rare and are usually incidental findings on abdominal imaging. · CT imaging and MRI imaging are the diagnostic tools of choice for the further workup of splenic lesions. · Based on their image morphological characteristics, a large number of splenic lesions can be assigned to one entity and do not need histological analysis. · Krähling H, Seidensticker M, Heindel WL et al. Diagnostic approach to splenic lesions. Fortschr Röntgenstr 2024; 196: 573 - 581.

Unnecessary Brain CT Scan and MRI Prescriptions for Evaluation of Children Suffering from Headache Referring to Namazi Hospital of Shiraz

Background and Objectives Despite the significant role of brain magnetic resonance imaging (MRI) and computed tomography (CT) scans in diagnosing diseases, unnecessary prescriptions of these imaging techniques may lead to adverse consequences, imposing a heavy financial burden on patients and health systems. The present study aimed to assess the unnecessary use of imaging services for the evaluation of children suffering from headaches in Namazi Hospital in Shiraz in 2020.. Subjects and Methods The research population of this retrospective study included all children who were referred to Namazi Hospital with the chief complaint of headache in 2019. Since the research sample was equal to the research population, no sampling was performed, and all individuals were surveyed through the census method. The data were analyzed in SPSS software (version 19) using statistical methods. Results Of the 54 brain MRIs performed, 7.4% were necessary, and 92.5% were unnecessary. Moreover, regarding 81 brain CT scans performed, 8.6% were necessary and 91.3% were not. The indication and insurance status had a significant positive relationship with the necessity of brain MRI and CT scan prescriptions (P&lt;0.05). The total financial burden imposed by unnecessary brain MRIs and brain CT scans of the studied patients in Shiraz Namazi Hospital was 158,298,588 Iranian Rials in 2020. Conclusion The results pointed out that over 90% of the brain MRIs and CT scans prescribed for children with headaches were unnecessary, which could impose unnecessary patient costs. Therefore, it seems essential to use effective clinical guidelines for the proper administration of brain MRI and CT scans.

Maxillary Sinus Epidermoid Cyst: A Case Report.

The preoperative diagnosis of a paranasal epidermoid cysts (EC) is challenging and is frequently mistaken. We present the case of a patient who developed a swelling on the left side of the face. The clinical examination showed a fixed and poorly defined swelling in the left maxillary region, with a slightly painful bulge in the upper vestibular peri-gingival area. A computed tomography (CT) scan showed a well-encapsulated expansive hypodensity that filled the left maxillary sinus. Magnetic resonance imaging (MRI) revealed a mass hypo-intense on T1-weighted images, high intense on T2-weighted images, hyperintense on diffusion-weighted images, and had a low apparent diffusion coefficient with no contrast uptake observed after the injection of Gadolinium. Surgical excision under general anesthesia was considered. A combined approach using an endoscopic sinus and Caldwell-Luc approach was performed and the histopathological examination confirmed the diagnosis of an EC. In conclusion, the preoperative imaging, including CT scans and MRI, aids in diagnosing ECs and differentiating them from other sinus tumors. Postoperative follow-up involving endoscopic surveillance and CT scans is crucial to monitor for potential recurrence.

Response Evaluation Using Contrast-Enhanced Ultrasound for Unresectable Advanced Hepatocellular Carcinoma Treated With Tyrosine Kinase Inhibitors Plus Anti-PD-1 Antibody Therapy

The aim of the work described here was to evaluate the role of contrast-enhanced ultrasound (CEUS) in response evaluation for unresectable advanced hepatocellular carcinoma (HCC) treated with tyrosine kinase inhibitors (TKIs) plus anti-programmed cell death protein-1 (PD-1) antibody therapy. A prospective cohort of consecutive patients with HCC who received combined TKI/anti-PD-1 antibody treatment for unresectable HCC between January 2022 and October 2022 was included in this study. The patients underwent unenhanced ultrasound (US) and CEUS examinations before treatment and at follow-up. Changes in the largest diameters of the target tumor on unenhanced US and the largest diameters of the enhancing target tumors on CEUS were evaluated. Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with unenhanced US and magnetic resonance imaging/computed tomography (MRI/CT) and modified RECIST (mRECIST) with CEUS and CEMRI/CT were used to assess treatment response. A total of 24 HCC patients (23 men and 1 woman; mean age: 56.5 ± 8.5 y; Barcelona Clinic Liver Cancer stage C, 62.5%; 29 intrahepatic target tumors) were studied. Calculations of degree of necrosis in the target tumors revealed no significant differences between CEUS and CEMRI/CT (44.5 ± 36.2% vs. 45.3 ± 36.8%, p=0.862). As for the differentiation of responders from non-responders, the agreement between RECIST version 1.1 of unenhanced US and mRECIST-CEUS was poor (κ coefficient=0.233). Meanwhile, there was a high degree of concordance between mRECIST-CEUS and mRECIST-CEMRI/CT (κ coefficient=0.812). CEUS proved to be superior to baseline US and is comparable to CEMRI/CT in defining treatment outcome for combined TKI/anti-PD-1 antibody therapy.

Contrast-enhanced ultrasonography findings of LAMNs with peritoneal and splenic metastases: a case report and literature review.

Low-grade appendiceal mucinous neoplasms (LAMNs) are rare appendiceal tumors that are primarily diagnosed using computed tomography(CT) enhancement and magnetic resonance imaging (MRI). Herein, we report the sonographic features, especially for contrast-enhanced ultrasound (CEUS), of a 70-year-old female with an unusual LAMN metastasizing to the peritoneum and spleen. The patient had a right pelvic mass 2 days prior to presentation. Two-dimensional (2D) ultrasound revealed a mixed cystic-solid mass in the right lower abdomen and spleen parenchyma; CEUS showed heterogeneous enhancement in both areas, suspected to be a mucinous mass. CT enhancement and MRI findings revealed concurrent findings. Histopathologically, LAMN lesions were confirmed in the appendix, spleen, and peritoneum of the specimens obtained during exploratory laparoscopy. No recurrences were reported at three years postoperatively. LAMN lesions may metastasize to abdominal organs, and imaging examinations are essential for diagnosis. This study presents major ultrasonography and CEUS findings for the diagnosis of LAMNs.

Sinogram upsampling using Primal–Dual UNet for undersampled CT and radial MRI reconstruction

Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works. This paper proposes a unified solution for both sparse CT and undersampled radial MRI reconstruction, achieved by applying Fourier transform-based pre-processing on the radial MRI and then finally reconstructing both modalities using sinogram upsampling combined with filtered back-projection. The Primal–Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal–Dual UNet, which improves the Primal–Dual network in terms of accuracy and reconstruction speed. The proposed method resulted in an average SSIM of 0.932±0.021 while performing sparse CT reconstruction for fan-beam geometry with a sparsity level of 16, achieving a statistically significant improvement over the previous model, which resulted in 0.919±0.016. Furthermore, the proposed model resulted in 0.903±0.019 and 0.957±0.023 average SSIM while reconstructing undersampled brain and abdominal MRI data with an acceleration factor of 16, respectively - statistically significant improvements over the original model, which resulted in 0.867±0.025 and 0.949±0.025. Finally, this paper shows that the proposed network not only improves the overall image quality, but also improves the image quality for the regions-of-interest: liver, kidneys, and spleen; as well as generalises better than the baselines in presence the of a needle.

Inter-translation Between CT and MRI Brain Scans Based on Cycle Consistent Adversarial Networks

Since Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are often used together in diagnosing brain diseases, it is rather time-consuming and expensive for patients to obtain two scans simultaneously. In this paper, the author proposed a new idea for CT-MRI inter-translation, using dataset of human brain scans to achieve unpaired image-to-image translation between CT scans and MRI scans. More specifically, the author proposed using Cycle Consistent Adversarial Networks (CycleGAN) to realize the idea of style transfer between CT and MRI. Briefly, this paper had trained two sets of generator and discriminator to form a cycle. This model can retain the main characteristics of a scan while transferring the scans style. To keep this translation process cycle-consistent, the Loss_cyc is used to keep the main content. Furthermore, the Loss_GAN ensures that the generated images exhibit a close stylistic resemblance to the target domain and the Loss_identity guarantees that the hue of the generated images is retained during the translation process. In addition, the author has added weights to the Loss_cyc and the Loss_identity to help the model perform better. Finally, this paper visualize the training process by presenting the original images and generated images together. Experimental results indicates that CycleGAN achieves a competitive performance can hopefully be a good auxiliary in brain diseases diagnosis.