Current Medical Imaging Reviews最新文献

Background: In the last few years, coronavirus disease 2019 (COVID-19) has changed human lifestyle, behavior, and perception of life. This disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). In the literature, there are limited studies about the late renal effects of COVID-19 that reflect the systemic involvement of this disease.

Aim: In the present study, we aimed to compare sonoelastographic changes in both kidneys between patients who had totally recovered from COVID-19 and healthy individuals using strain wave elastography (SWE).

Methods: This study was conducted between June 2021 and May 2022 in Kahramanmaraş City Hospital Department of Radiology. File and archive records were retrospectively evaluated. Basic demographic, laboratory, and renal ultrasonography (USG) and sonoelastographic findings were screened and noted. Two groups were defined to compare sonoelastographic findings. Post-long COVID-19 group had 92 post-long COVID-19 patients, and the comparator group had 9 healthy individuals". Both groups' demographic, laboratory, and ultrasound-elastographic findings were assessed.

Results: The post-long COVID-19 group had a higher renal elastographic value than the comparator group (1.52 [0.77-2.3] vs. 0.96 [0.54-1.54], p<0.001). There were no statistically significant differences between the two groups in terms of age (p=0.063), gender (p=0.654), or body mass index (BMI) (p=0.725), however, there was a significant difference observed between the two groups in the renal strain ratio (RSR). According to an ROC analysis, an RSR cutoff of >1.66 predicted post-long COVID-19 with 44.9% sensitivity and 81.9% specificity. (AUC=0.655, p<0.001). A separate ROC analysis was performed to predict post-long COVID-19 with a BMI cutoff of <33.52, kg/m2 sensitivity of 92.4% and specificity of 17% (AUC=0.655, p<0.001).

Conclusion: We demonstrated that renal parenchymal stiffness increases with SWE in post-long COVID-19 patients.

Background: Currently, coronavirus disease 2019 (COVID-19) continues to remain in the pandemic stage, leading to severe challenges in the global public healthcare system. Magnetic resonance imaging (MRI) methods have played an important role in the diagnosis of COVID-19 and the structural evaluation of the affected organs. Reviewing and summarizing the application of MRI has significant clinical implications for COVID-19.

Objective: The study aimed to analyze literature related to the application of MRI in COVID-19 using bibliometric tools, to explore the research status, hotspots, and developmental trends in this field, and to provide a reference for the application of MRI in the clinical diagnosis and evaluation of COVID-19.

Methods: We used the Web of Science Core Collection database to search and collect relevant literature on the use of MRI in COVID-19. The authors, institutes, countries, journals, and keyword modules of the bibliometric analysis software CiteSpace and VOSviewer were used to analyze and plot the network map.

Results: A total of 1506 relevant articles were shortlisted through the search; the earliest study was published in 2019, showing an overall upward trend every year. The research was mainly presented as published articles. Clinical neurology was found to be the primary discipline. The United States had the highest publication volume and influence in this field. Countries around the world cooperated more closely. The Cureus Journal of Medical Science was the main periodical to publish articles. Institutes, such as Harvard Medical School, Mayo Clinic, and Massachusetts General Hospital, have published a large number of papers. Some of the high-frequency keywords were "COVID-19", "SARS-CoV-2", "magnetic resonance", "myocarditis", and "cardiac magnetic resonance imaging". The keyword clustering study showed that the current research mainly focuses on five "hot" directions.

Conclusion: There is a need to strengthen cross-teamwork and multidisciplinary collaboration in the future to completely explore the positive role of MRI in COVID-19 and to discover breakthroughs for the challenges in the clinical diagnosis and treatment of COVID-19.

Background: At present, there are some problems in multimodal medical image fusion, such as texture detail loss, leading to edge contour blurring and image energy loss, leading to contrast reduction.

Objective: To solve these problems and obtain higher-quality fusion images, this study proposes an image fusion method based on local saliency energy and multi-scale fractal dimension.

Methods: First, by using a non-subsampled contourlet transform, the medical image was divided into 4 layers of high-pass subbands and 1 layer of low-pass subband. Second, in order to fuse the high-pass subbands of layers 2 to 4, the fusion rules based on a multi-scale morphological gradient and an activity measure were used as external stimuli in pulse coupled neural network. Third, a fusion rule based on the improved multi-scale fractal dimension and new local saliency energy was proposed, respectively, for the low-pass subband and the 1st closest to the low-pass subband. Layerhigh pass sub-bands were fused. Lastly, the fused image was created by performing the inverse non-subsampled contourlet transform on the fused sub-bands.

Results: On three multimodal medical image datasets, the proposed method was compared with 7 other fusion methods using 5 common objective evaluation metrics.

Conclusion: Experiments showed that this method can protect the contrast and edge of fusion image well and has strong competitiveness in both subjective and objective evaluation.

Introduction: Fallopian tube leiomyoma is an uncommon, benign gynecologic tumor that originates from the smooth muscle of the fallopian tube or vascular cells supplying the fallopian tube.

Case presentation: In this study, we report a case of a patient with fallopian tube leiomyoma. What makes this instance even more unique is the association of the leiomyoma with cystic degeneration, manifesting as a large abdominopelvic cystic mass. CT scan suspected that the mass might be an ovarian cystadenoma. However, ultrasonography, a widely used diagnostic tool, effectively assisted the clinicians in confidently ruling out the possibility that the tumor was originating from the ovaries. Ultimately, the patient underwent exploratory laparoscopy and the pathologic diagnosis was fallopian tube leiomyoma with cystic degeneration. To our knowledge, no instance of a fallopian tube leiomyoma of this size with cystic degeneration has been reported. Thus, it is worth mentioning.

Conclusion: In summary, fallopian tube leiomyomas are classified as uncommon benign gynecologic tumors, which pose challenges in clinical diagnosis. The combined use of multiple imaging modalities may be more helpful in the proper diagnosis of this disease entity.

Background: Whole-body bone scanning is a nuclear medicine technique with high sensitivity used for the diagnosis of bone-related diseases [e.g., bone metastases] that can be obtained by positron emission tomography[PET] or single-photon emission computed tomography[SPECT] imaging, depending on the different radiopharmaceuticals used. In contrast to the high sensitivity of the bone scan, it has low specificity, which leads to misinterpretation, causing adverse effects of unwarranted intervention or interruption to timely treatment.

Objective: To address this problem, this paper proposes a joint model called mSegResRF-SPECT, which accomplishes for the first time the task of classifying whole-body bone scan images on a public SPECT dataset [BS-80K] for the diagnosis of bone metastases.

Methods: The mSegResRF-SPECT adopts a multi-bone region segmentation algorithm to segment the whole body image into 13 regions, ResNet34 as an extractor to extract the regional features, and a random forest algorithm as a classifier.

Results: The experimental results of the proposed model show that the average accuracy, sensitivity, and F1 score of the model on the BS-80K dataset reached SOTA.

Conclusion: The proposed method presents a promising solution for better bone scan classification methods.

Objective: This study aims to comprehensively assess the characteristics of patent foramen ovale (PFO) in relation to Cryptogenic Strok (CS) by utilizing transesophageal echocardiography (TEE) and contrast transthoracic echocardiography (c-TTE) and to identify high-risk factors associated with PFO-related CS.

Background: Transcatheter PFO closure has demonstrated its effectiveness in preventing PFO-related CS. Therefore, understanding the specific structural attributes of PFO associated with CS is imperative.

Methods: Enrollment comprised 113 test patients who experienced CS in conjunction with PFO and 117 control patients diagnosed with migraine with PFO but without a history of stroke. The characteristics of the PFO were observed by TEE and c-TTE. A comparative analysis was undertaken to assess the variations in PFO characteristics between the test patients and controls, and to uncover the independent factors relevant to CS.

Results: The patients in the test group were older than the controls. Both the height and length of the PFO during Valsalva exhibited greater dimensions in the test group when contrasted with controls. Notably, the test group presented higher incidence rates of low-angle PFO (defined as an angle between the inferior vena cava (IVC) and PFO ≤ 10°) and atrial septal aneurysm (ASA) as contrasted with the control group. Right-to-left shunt (RLS) III during Valsalva demonstrated a significantly elevated occurrence within the test group as opposed to the controls. Conversely, RLS II during Valsalva exhibited a significantly higher frequency in the controls in contrast to the tests. No significant disparities were observed between the two groups with respect to RLS I during Valsalva and all grades of RLS at rest. Multivariate analysis revealed that the length of the PFO during Valsalva, the presence of ASA, RLS III during Valsalva and low-angle PFO were independent relevant factors associated with CS.

Conclusions: The length of the PFO tunnel, low-angle PFO, RLS III during Valsalva and the presence of ASA were independent risk factors for CS. The combined utilization of TEE and c-TTE may prove valuable in identifying PFO patients at a heightened risk of CS and in facilitating the screening process for transcatheter PFO closure.

Objective: We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging.

Methods: We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice.

Results: The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs.

Conclusion: In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

Introduction: The diagnosis and characterization of vertebral compression fractures are very important for clinical management. In this evaluation, which is usually performed with diagnostic (conventional) imaging, the findings are not always typical or diagnostic. Therefore, it is important to have new information to support imaging findings. Texture analysis is a method that can evaluate information contained in diagnostic images and is not visually noticeable. This study aimed to evaluate the magnetic resonance images of cases diagnosed with vertebral compression fractures by the texture analysis method, compare them with histopathological data, and investigate the effectiveness of this method in the differentiation of benign and malignant vertebral compression fractures.

Methods: Fifty-five patients with a total of 56 vertebral compression fractures were included in the study. Magnetic resonance images were examined and segmented using Local Image Feature Extraction (LIFEx) software, which is an open-source program for texture analysis. The results were compared with the histopathological diagnosis.

Results: The application of the Decision Tree algorithm to the dataset yielded impressively accurate predictions (≈95% in accuracy, precision, and recall).

Conclusion: Interpreting tissue analysis parameters together with conventional magnetic resonance imaging findings can improve the abilities of radiologists, lead to accurate diagnoses, and prevent unnecessary invasive procedures. Further prospective trials in larger populations are needed to verify the role and performance of texture analysis in patients with vertebral compression fractures.

Background: Atypical carcinoid (AC) is one of the rarest lung neuroendocrine tumors (NETs) that rarely metastasize to the breast, and only a few cases have been reported in the literature. Positron emission tomography/computed tomography (PET/CT) with somatostatin analogs (SSAs) labeled with Gallium-68 (68Ga) now represents the gold standard for diagnosis and management of NETs. A case of an unusual metastasis to the breast from an AC detected by 68Ga-DOTATATE PET/CT was reported.

Case presentation: A 73-year-old woman was presented with a right breast lesion found on mammography screening, which revealed a metastatic neuroendocrine tumor by histopathological analysis with a tru-cut biopsy. Subsequently, 68Ga-DOTATATE PET/CT imaging performed for the initial evaluation showed increased radiotracer uptake in the lesion in the right breast as well as the nodular lesion in the middle lobe of the right lung, which was histologically confirmed to be AC.

Conclusion: Metastasis of uncommon AC of the lung to the breast is extremely rare. However, it is essential to properly differentiate metastatic tumors from primary disease due to differences in clinical management and prognosis, and 68Ga-DOTATATE PET/CT is a unique diagnostic tool with the advantage of whole-body imaging.

Objective: This study explored whether breast MRI manifestations could be used to predict the stroma distribution of breast cancer (BC) and the role of tumor stroma-based MRI manifestations in molecular subtype prediction.

Methods: 57 patients with pathologically confirmed invasive BC (non-special type) who had lumpy BC on MRI within one week before surgery were retrospectively collected in the study. Stroma distributions were classified according to their characteristics in the pathological sections. The stromal distribution patterns among molecular subtypes were compared with the MRI manifestations of BC with different stroma distribution types (SDTs).

Results: SDTs were significantly different and depended on the BC hormone receptor (HR) (P<0.001). There were also significant differences among five SDTs on T2WI, ADC map, internal delayed enhanced features (IDEF), marginal delayed enhanced features (MDEF), and time signal intensity (TSI) curves. Spiculated margin and the absence of type-I TSI were independent predictors for BC with star grid type stroma. The appearance frequency of hypo-intensity on T2WI in HR- BCs was significantly lower (P=0.043) than in HR+ BCs. Star grid stroma and spiculated margin were key factors in predicting HR+ BCs, and the AUC was 0.927 (95% CI: 0.867-0.987).

Conclusion: Breast MRI can be used to predict BC's stromal distribution and molecular subtypes.