Acta radiologica最新文献

Background: The impact of excluding intrahepatic segmental vessels from regions of interest (ROIs) on liver stiffness measurement (LSM) via magnetic resonance elastography (MRE) remains uncertain.

Purpose: To determine the effect of excluding intrahepatic segmental vessels from ROIs on LSM obtained from MRE.

Material and methods: This retrospective analysis included 95 participants who underwent successful two-dimensional gradient recalled-echo MRE before hepatic tumor resection (n = 49) or living liver donation (n = 46). The conventional LSM was determined by manually drawing ROIs on the elastogram within the 95% confidence region, staying 1 cm within the liver capsule and excluding large hilar vessels, the gallbladder, hepatic lesions, and artifacts. In addition, the modified LSM was determined by excluding intrahepatic segmental vessels. LSMs obtained by the two methods were compared with paired sample signed-rank test. Diagnostic performance for advanced fibrosis was calculated and compared using McNemar's test and Delong's test. The stage of hepatic fibrosis was assessed using surgical specimens by the METAVIR system.

Results: The modified LSM was larger than the conventional LSM (2.4 kPa vs. 2.2 kPa in reader 1; 2.7 kPa vs. 2.4 kPa in reader 2; P < 0.001). The modified LSM showed superior sensitivity (0.841 vs. 0.659 in reader 1; 0.864 vs. 0.705 in reader 2; P < 0.05) and area under the curve (0.901 vs. 0.820 in reader 1; 0.912 vs. 0.843 in reader 2; P < 0.05) for detecting advanced fibrosis (≥F3) than conventional LSM.

Conclusion: The exclusion of intrahepatic segmental vessels from ROIs in MRE affected the LSM and enhanced diagnostic performance for advanced fibrosis.

Radiographic measurements play a crucial role in evaluating the alignment of distal radius fractures (DRFs). Various manual methods have been used to perform the measurements, but they are susceptible to inaccuracies. Recently, computer-aided methods have become available. This review explores the methods commonly used to assess DRFs. The review introduces the different measurement techniques, discusses the sources of measurement errors and measurement reliability, and provides a recommendation for their use. Radiographic measurements used in the evaluation of DRFs are not reliable. Standardizing the measurement techniques is crucial to address this and automated image analysis could help improve accuracy and reliability.

Background: The most compact portion of the corpus callosum (CC) is the corpus splenium (CS).

Purpose: To evaluate the connection between clinical and demographic features to determine whether neuroimaging findings will be permanent or temporary in CS patients.

Material and methods: We enrolled 93 patients (age range = 18-86 years) with CS lesions. Demographic and clinical information were recorded. We examined the lesions depending on the location. Group 1 (n = 20) had lesions limited to the CS (egg-shaped or round); group 2 (n = 15) had "boomerang sign" lesions; and group 3 (n = 58) had splenium involvement in conditions affecting the whole brain (Boomerang sign+ plus).

Results: Group 1 had a lower mean age, shorter disease duration, and fewer persistent lesions than others (P < 0.01, P < 0.001, and P < 0.001, respectively). The mean disease onset age (in years) in group 1 was higher than that of the other groups (P < 0.045). Group 2 had lower potassium (K) (P < 0.003) and red cell distribution width levels (P < 0.029) than the other groups. Age <41.5 years (P < 0.001), age at illness initiation <48.5 years (P < 0.002), disease duration <5.5 months (P < 0.001), and eosinophil level <0.29 uL (P 0.014) all point to temporary lesions.

Conclusion: Cases with limited CS lesions have younger onset ages, lower disease onset ages, and shorter disease durations. Age, age of disease onset, disease duration, and eosinophil level are risk variables that affect whether CS lesions are permanent or temporary.

Background: Positron emission tomography (PET) has been reported as effective in diagnosing peripheral nerve injury (PNI). However, there is a lack of studies evaluating different degrees of PNI using PET within the same individual to reduce errors due to interindividual differences.

Purpose: To evaluate the recovery process in the same rat after sciatic nerve injury using PET/magnetic resonance imaging (MRI).

Material and methods: Crushing nerve injuries were induced in the left sciatic nerves of six male rats, preserving the right ones. The degree of nerve damage was measured at one, two, three, four, and five weeks postoperatively using three assessment methods: paw withdrawal threshold test (RevWT); PET (SUVR); and MRI (MRSIR). All the representing values of each method are presented as ratio values of the right and left sides in each rat.

Results: Significant gradual recovery of all rats was observed over time in all the methods. No significant differences in RevWT and MRSIR were observed between before and more than four weeks after injury, whereas a significant difference in SUVR was still observed between before and five weeks after injury (P = 0.0007). The parameters of all methods decreased significantly over time (P = 0.000, all), and the explanatory power was significant in RevWT, SUVR, and MRSIR.

Conclusion: PET and MRI could be valuable non-invasive techniques for diagnosing neuropathic pain resulting from PNI. PET/MRI would be expected to be a more accurate and informative diagnostic tool for PNI than MRI alone.

Background: Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy.

Purpose: To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts.

Material and methods: An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed. The phantom underwent scanning at three radiation dose levels and two tube voltage settings. A set of conventional images (CIs), virtual monoenergetic images (VMIs), and iodine concentration maps (ICMs) were generated and measured for all the eight iodine inserts. Quantitative indicators of mean absolute percentage error (MAPE), artifact index (AI), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and standard deviation (SD) on CIs and VMIs were calculated for IC and HU. Subjective score evaluation was also conducted.

Results: The MAPE_iodine values of all regions of interest across different scanning configurations were all <5%. Almost all APE_iodine values were <5%, indicating that metal artifacts had little impact on IC measurements. When the tube voltage was fixed, the SD value of attenuation decreased with the increase of the tube current; this is also true when the tube current was fixed. The middle energy reconstructions seemed to give a good balance between reducing artifacts and improving contrast.

Conclusion: VMIs from DLCT can reduce metal artifacts, the accuracy of IC quantification is not sensitive to imaging parameters. In summary, metal implants exhibit minimal impact on image quality and IC quantification accuracy in reconstructed images from DLCT.

Background: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.

Purpose: To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI.

Material and methods: The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity.

Results: The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010).

Conclusion: DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

Background: The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method should be developed to assess the entire RC muscles.

Purpose: To develop a fully automated approach based on a deep neural network to segment RC muscles from clinical magnetic resonance imaging (MRI) scans.

Material and methods: In total, 94 shoulder MRI scans (mean age = 62.3 years) were utilized for the training and internal validation datasets, while an additional 20 MRI scans (mean age = 62.6 years) were collected from another institution for external validation. An orthopedic surgeon and a radiologist manually segmented muscles and bones as reference masks. Segmentation performance was evaluated using the Dice score, sensitivities, precision, and percent difference in muscle volume (%). In addition, the segmentation performance was assessed based on sex, age, and the presence of a RC tendon tear.

Results: The average Dice score, sensitivities, precision, and percentage difference in muscle volume of the developed algorithm were 0.920, 0.933, 0.912, and 4.58%, respectively, in external validation. There was no difference in the prediction of shoulder muscles, with the exception of teres minor, where significant prediction errors were observed (0.831, 0.854, 0.835, and 10.88%, respectively). The segmentation performance of the algorithm was generally unaffected by age, sex, and the presence of RC tears.

Conclusion: We developed a fully automated deep neural network for RC muscle and bone segmentation with excellent performance from clinical MRI scans.

Background: The optimal primary debulking surgery outcome of serous ovarian carcinoma (SOC) is greatly affected by primary ovarian neoplasm or metastatic lesion close to the rectum.

Purpose: To study the risk factors affecting postoperative residual primary ovarian neoplasm or metastatic lesion close to the rectum of SOC.

Material and methods: The clinical and MRI data of 164 patients with SOC eligible from institution A (training and test groups) and 36 patients with SOC eligible from institution B (external validation group) were collected and retrospectively analyzed. The clinical data included age, serum carbohydrate antigen 125 (CA-125), human epididymis protein 4, and neutrophil-to-lymphocyte ratio (NLR). Magnetic resonance imaging (MRI) data included ovarian mass distribution, maximum diameter of ovarian mass, ovarian mass features, degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion, and amount of ascites. A model was established using multivariate logistic regression.

Results: By univariate and multivariate logistic regressions, CA-125 (P = 0.024, odds ratio [OR] = 3.798, 95% confidence interval [CI] = 1.24-13.32), NLR (P = 0.037, OR = 3.543, 95% CI = 1.13-12.72), and degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion (P < 0.001, OR = 37.723, 95% CI = 7.46-266.88) were screened as independent predictors. The area under the curve values of the model in the training, test, and external validation groups were 0.860, 0.764, and 0.778, respectively.

Conclusion: The clinical-radiological model based on T1-weighted dual-echo MRI can be used non-invasively to predict postoperative residual ovarian neoplasm or metastasis close to SOC in the rectum.

Pseudolesions in bone and muscle are encountered mostly incidentally in routine imaging studies, especially due to the recent advancements on many different imaging modalities. These lesions can be categorized into the following categories: normal variants; congenital; iatrogenic; degenerative; and postoperative. In this review, we discuss the many different radiological characteristics of musculoskeletal pseudolesions that appear on imaging, which can prevent non-essential additional studies.