X-ray computed tomography (XCT) has been shown to be an effective imaging technique for a variety of materials. Due to the relatively low differential attenuation of X-rays in biological tissue, a high density contrast agent is often required to obtain optimal contrast. The contrast agent, iodine potassium iodide (), has been used in several biological studies to augment the use of XCT scanning. Recently was used in XCT scans of animal hearts to study cardiac structure and to generate 3D anatomical computer models. However, to date there has been no thorough study into the optimal use of as a contrast agent in cardiac muscle with respect to the staining times required, which has been shown to impact significantly upon the quality of results. In this study we address this issue by systematically scanning samples at various stages of the staining process. To achieve this, mouse hearts were stained for up to 58 hours and scanned at regular intervals of 6–7 hours throughout this process. Optimal staining was found to depend upon the thickness of the tissue; a simple empirical exponential relationship was derived to allow calculation of the required staining time for cardiac samples of an arbitrary size.

This study was aimed to realize the automatic segmentation of communicating hydrocephalus lesions in brain CT after decompressive craniotomy (DC) in patients with traumatic brain injury (TBI) and discover correlation between cerebrospinal fluid changes and communicating hydrocephalus. Based on the traditional fuzzy C-means (FCM) algorithm, a new segmentation method filter-based FCM (FBFCM) algorithm was proposed. With 56 TBI patients as the research objects, the hydrocephalus lesions in CT images of patients after DC were segmented. The segmentation success rate (SSR), the segmentation coefficient Epc, the segmentation entropy Epe, and the number of iterations were indicators reflecting segmentation performance of FBFCM. The region of interest (ROI) on the segmented image was used to study the patient’s cerebral aqueduct, foramen magnum, and C2 level of the cerebrospinal fluid velocity and flow, to analyze the characteristics of the occurrence of communicative hydrocephalus. It was indicated that average Epc of FBFCM algorithm was 0.9321 ± 0.0144, Epe was 0.1126 ± 0.0081, the average number of iterations was 14.42 ± 3.79, and the segmentation success rate was 96%. Moreover, the above four indicators had statistically considerable differences compared with those of the FCM algorithm and the hard clustering algorithm (HCM) ( P  < 0.05). Analysis of the cerebrospinal fluid flow rate in patients with communicative hydrocephalus found that the cerebrospinal fluid flow rate at the midbrain aqueduct of the patient increased greatly. The net flow was 0.000 ± 0.004 mL/s in the aqueduct of the midbrain, 0.001 ± 0.006 mL/s in the foramen magnum, and 0.002 ± 0.004 mL/s in the C2 layer. In summary, the FBFCM algorithm is effective in the segmentation and processing of CT images, which can further improve the effect of this diagnosis. After examination, it is concluded that the cranial cerebrospinal fluid flow rate and flow of TBI patients are improved after DC treatment, so that the patient’s condition can be effectively relieved. It has promotion value with clinical application.

This study aimed to explore the application value of magnetic resonance imaging optimized by neural network segmentation algorithm in analyzing the relationship between cerebrospinal fluid changes after decompressive craniectomy and the occurrence of communicating hydrocephalus. 100 patients with craniocerebral injury undergoing decompressive craniectomy in hospital were selected as research subjects. The collected MRI images were processed using the OTSU algorithm, the cerebrospinal fluid flow rate was calculated based on the observation results, and the MRI based on the neural network segmentation algorithm was used to analyze the relationship between the occurrence of communicating hydrocephalus with the cerebrospinal fluid flow after decompressive craniectomy for craniocerebral injury. Additionally, the dynamics of the flow of cerebrospinal fluid in the midbrain aqueduct was analyzed. After decompressive craniectomy for craniocerebral injury, of the 24 cases of cerebrospinal fluid accumulation, 23 cases had hydrocephalus; of the 55 cases of cerebrospinal fluid flow disorder, hydrocephalus occurred in 47 cases; and of the 21 cases of normal cerebrospinal fluid, no patients had hydrocephalus. For patients with communicating hydrocephalus, the cerebrospinal fluid flow at the aqueduct was obviously accelerated and the flow was increased. From this, the differential diagnosis of cerebrospinal fluid and communicating hydrocephalus can be further confirmed. The results showed that the third ventricle of the study group was significantly reduced, and the flow of cerebrospinal fluid was similar to that of normal people. It suggested that decompressive craniectomy can relieve communicating hydrocephalus. In patients with communicating hydrocephalus, the cerebrospinal fluid flow at the aqueduct was significantly accelerated, the flow amount was increased, and the blocked flow of cerebrospinal fluid can also lead to hydrocephalus, which further clarified the relationship between the occurrence of communicating hydrocephalus with the flow of cerebrospinal fluid. In short, the neural network segmentation algorithm-based magnetic resonance imaging demonstrated a good value in the analysis of craniocerebral injury, from which the doctor observed that the cerebrospinal fluid flow at the aqueduct was significantly accelerated. Its detection of brain complications after decompressive craniectomy was also effective.

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