Application of MLP neural network to predict X-ray spectrum from tube voltage, filter material, and filter thickness used in medical imaging systems.

The X-ray energy spectrum is crucial for image quality and dosage assessment in mammography, radiography, fluoroscopy, and CT which are frequently used for the diagnosis of many diseases including but not limited to patients with cardiovascular and cerebrovascular diseases. X-ray tubes have an electron filament (cathode), a tungsten/rubidium target (anode) oriented at an angle, and a metal filter (aluminum, beryllium, etc.) that may be placed in front of an exit window. When cathode electrons meet the anode, they generate X-rays with varied energies, creating a spectrum from zero to the electrons' greatest energy. In general, the energy spectrum of X-rays depends on the electron beam's energy (tube voltage), target angle, material, filter thickness, etc. Thus, each imaging system's X-ray energy spectrum is unique to its tubes. The primary goal of the current study is to develop a clever method for quickly estimating the X-ray energy spectrum for a variety of tube voltages, filter materials, and filter thickness using a small number of unique spectra. In this investigation, two distinct filters made of beryllium and aluminum with thicknesses of 0.4, 0.8, 1.2, 1.6, and 2 mm were employed to obtain certain limited X-ray spectra for tube voltages of 20, 30, 40, 50, 60, 80, 100, 130, and 150 kV. The three inputs of 150 Multilayer Perceptron (MLP) neural networks were tube voltage, filter type, and filter thickness to forecast the X-ray spectra point by point. After training, the MLP neural networks could predict the X-ray spectra for tubes with voltages between 20 and 150 kV and two distinct filters made of aluminum and beryllium with thicknesses between 0 and 2 mm. The presented methodology can be used as a suitable, fast, accurate and reliable alternative method for predicting X-ray spectrum in medical applications. Although a technique was put out in this work for a particular system that was the subject of Monte Carlo simulations, it may be applied to any genuine system.

Proposing an intelligent technique based on radial basis function neural network to forecast the energy spectrum of diagnostic X-ray imaging systems.

In digital subtraction angiography (digital subtraction total cerebral angiography), cardiac and macrovascular cardiography, anorectal radiology, fluoroscopy, and computed tomography (CT), a prior knowledge to X-ray energy spectrum is crucial for assessing the image quality and also calculating patient X-ray dosage. The present investigation's main objective is to propose an intelligent technique for faster calculating X-ray energy spectrum of medical imaging systems with different exposure settings of tube voltage, filter material, and thickness based on limited specific spectra. In this study, Monte Carlo N Particle (MCNP) simulation code was initially used to generate some limited X-ray spectra for tube voltages of 20, 30, 40, 50, 80, 100, 130, and 150 kV for two different filters of beryllium and aluminum with thicknesses of 0. 4, 0.8, 1.2, 1.6 and 2 mm. Tube voltage, type, and thickness of filter were used as the 3 inputs of 150 Radial Basis Function Neural Network (RBFNN) to forecast point by point of the X-ray spectrum. After training, the RBFNNs could forecast most of the X-ray spectra for tube voltages in the range of 20-150 kV and two various filters of aluminum and beryllium with thicknesses in the range of 0-2 mm.

Label-Free Visualization of Early Cancer Hepatic Micrometastasis and Intraoperative Image-Guided Surgery by Photoacoustic Imaging.

The detection of cancer micrometastasis for early diagnosis and treatment poses a great challenge for conventional imaging techniques. The aim of our study was to evaluate the performance of photoacoustic imaging (PAI) in detecting hepatic micrometastases from melanoma at a very early stage and in aiding tumor resection by intraoperative guidance. Methods: In vivo studies were performed by following protocols approved by the Ethical Committee for Animal Research at Xiamen University. First, a mouse model of B16 melanoma metastatic to the liver (n = 10) was established to study the development of micrometastases in vivo. Next, the mice were imaged by a scalable PAI instrument, ultrasound, 9.4-T high-resolution MRI, PET/CT, and bioluminescence imaging. PAI scans acquired with optical wavelengths of 680-850 nm were kept spectrally unmixed by using a linear least-squares method to differentiate various components. Differences in signal-to-background ratios among different modalities were determined with the 2-tailed paired t test. The diagnostic results were assessed with histologic examination. Excised liver samples from patients diagnosed with hepatic cancer were also examined to identify the tumor boundaries. Surgical removal of metastatic melanoma was precisely guided in vivo by the portable PAI system. Results: PAI was able to detect metastases as small as approximately 400 µm at a depth of up to 7 mm in vivo-a size that is smaller than can be detected with ultrasound and MRI. The tumor-to-liver ratio for PAI at 8 d (4.2 ± 0.2, n = 6) and 14 d (9.2 ± 0.4, n = 5) was significantly higher than for PET/CT (1.8 ± 0.1, n = 5, and 4.5 ± 0.2, n = 5, respectively; P < 0.001 for both). Functional PAI revealed dynamic oxygen saturation changes during tumor growth. The limit of detection was approximately 219 cells/µL in vitro. We successfully performed intraoperative PAI-guided surgery in vivo using the portable PAI system. Conclusion: Our findings offer a rapid and effective complementary clinical imaging application to noninvasively detect micrometastases and guide intraoperative resection.