Pemanfaatan Limbah Cangkang Telur Ayam Dan Bebek Sebagai Sumber Kalsium Untuk Sintesis Mineral Tulang

Penelitian ini bertujuan untuk membuat biomaterial substitusi tulang yang menyerupai komposisi tulang sebenarnya, yang terdiri dari mineral inorganik (apatit) dan bahan organik sebagai matriks. Pada penelitian ini metode presipitasi digunakan dalam pembuatan komposit apatit-kitosan dengan menggunakan cangkang telur ayam dan bebek sebagai sumber kalsium dan KH2PO4 sintetik sebagai sumber posfat. Cangkang telur dan KH2PO4 sebagai mineral inorganik tulang, sedangkan matriks organik yang digunakan adalah kitosan dari kulit udang. Cangkang telur ayam dan bebek dikalsinasi untuk menghilangkan semua komponen karbonat (CO3) sehingga didapatkan CaO sebagai sumber kalsium. Komposit apatit-kitosan dibuat dengan menumbuhkan senyawa kalsium posfat pada matriks dengan metode ek situ. Sampel yang dihasilkan selanjutnya dikeringkan pada suhu 50 oC. Karakteristik sampel selanjutnya dianalisis menggunakan X-ray Diffraction (XRD) dan Fourier Transform Infrared (FTIR) Spectroscopy. Pola XRD sampel memperlihatkan adanya puncak-puncak difraksi untuk kristal apatit. Data tersebut didukung oleh spektrum FTIR yang memperlihatkan puncak transmitansi dari fosfat dan karbonat dari kristal apatit. This study aimed to develop bone substituted biomaterial consisting of inorganic mineral (apatit) and organic material as matrix. In this study, a precipitation method of apatite-chitosan composite synthesis has been used using hen\u27s and duck\u27s eggshell as calcium source and synthetic KH2PO4 as phosphate source. The eggshell and KH2PO4 act as inorganic mineral, whereas organic matrix used was chitosan originated from shrimp shell. The eggshell was calcinated to decompose all the carbonate (CO3) phases. To produce the composite, calcium phosphates were grown on organic matrix of chitosan using ex situ method. The result samples were further dried at 50 oC. Characteristic of the samples were performed using X-ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) Spectroscopy. The XRD profile illustrated specific diffraction angles at peaks of apatite crystals. This data were supported by FTIR spectra that showed transmittance peak of phosphates and carbonates from apatites

Evaluation of Changes in Dose Estimation on Abdomen CT Scan with Automatic Tube Current Modulation Using In-House Phantom

This study evaluates the effect of the Automatic Tube Current Modulation (ATCM) technique on pitch and effective diameter variation in estimating dose values and noise levels for abdominal examination on Philips Ingenuity CT scan machine using in-house Phantoms. The in-house phantoms are oval in shape with three effective diameter sizes, namely 23.2 cm, 28.3 cm, and 33.3 cm to represent abdominal region. The three size Phantoms were scanned using an Ingenuity 128 Philips CT scan with the abdominal protocol exposure parameters of 120 kVp tube voltage, Dose Right Index (DRI) variations of 10,11,12,13, and 14, and pitch variations of 0.6; 0.8; 1.0; 1.2; and 1.49. The changes in mAs, CTDIvol, and noise to the Philips reference value were then verified (i.e. an addition of one DRI value increases mAs by 12 %). For evaluation, a metric to express the change in DRI is defined as ΔDRI. The study demonstrates that noise level is influenced by object size; size information of the object could be useful to predict the change of tube current and pitch due to ATCM with respect to selected DRI. The DRI value is proportional to the tube current, thus selecting the DRI at a certain pitch will directly determine tube current. The ΔDRI in general, according to Philips specifications, is verified to be approximately 10 % to 13 %, except for DRI 10 to 11 which is relatively high on average 15 % to 17 %. Increasing DRI increases the CTDIvol. The CTDI/mAs constantly ranges of 0.06 to 0.07. The value could serve as a characteristic parameter for quality assurance. The ATCM specifications of the Ingenuity 128 CT Scanner is according to Philips regulations

Noise Suppression of Computed Tomography (CT) Images Using Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

In this study, an in-house residual encoder-decoder convolutional neural network (RED-CNN)-based algorithm was composed and trained using images of cylindrical polymethyl-methacrylate (PMMA) phantom with a diameter of 26 cm at different simulated noise levels. The model was tested on 21 × 26 cm elliptical PMMA computed tomography (CT) phantom images with simulated noise to evaluate its denoising capability using signal to noise ratio (SNR), comparative peak signal-to-noise ratio (cPSNR), structural similarity (SSIM) index, modulation transfer function frequencies (MTF 10 %) and noise power spectra (NPS) values as parameters. Evaluation of a possible decrease of image quality was also performed by testing the model using homogenous water phantom and wire phantom images acquired using different mAs values. Results show that the model was able to consistently increase SNR, cPSNR, SSIM values, and decrease the integral noise power spectra (NPS). However, the noise level on either training or testing data affects the model’s final denoising performance. The lower noise level on testing data images tends to result in over-smoothed images, as indicated by the shift of the NPS curves. In contrast, higher simulated noise level tends to result in less satisfactory denoising performance, as indicated by lower SNR, cPSNR, and SSIM values. Meanwhile, the higher noise level on training data images tends to produce denoised images with reduced sharpness, as indicated by the decrease of the MTF 10 % values. Further studies are required to better understand the character of RED-CNN for CT noise suppression regarding the optimum parameters for best results

KARAKTERISASIGUGUSFOSFATDANKARBONATDALAMTULANGTIKUSDENGAN FOURIER TRANSFORM INFRARED (FT-IR) SPECTROSCOPY

KARAKTERISASIGUGUSFOSFATDANKARBONATDALAMTULANGTIKUSDENGAN FOURIER TRANSFORM INFRARED (FT-IR) SPECTROSCOPY. Telah dilakukan penelitian untuk mengetahui karakteristik kalsium fosfat dalam tulang tikus menggunakan spektroskopi FT-IR. Sampel tulang femur dan tibia diperoleh dari tikus jenis Sprague-Dawley. Variasi umur yang diambil yaitu 1bulan hingga 8 bulan. Untuk menghilangkan komponen organik, sampel diberi perlakuan menggunakan hidrazin. Maksimal pita absorpsi 3 fosfat spektrum FT-IR tulang tikus berada disekitar 1036 cm-1.Pita absorpsi 4 fosfat dalam mineral tulang tikus terpecah dengan puncak sekitar 566 cm-1 dan 599 cm-1. Keberadaan pita absorpsi 1, 3, dan 4 karbonat menunjukkan kalsium fosfat mineral tulang hadir dalam bentuk apatit karbonat. Peningkatan umur mengakibatkan penurunan kandungan fosfat dan karbonat dalam mineral tulang tikus

Comparison of SPECT quick QC between using in-house hot phantom and Jaszczak phantom: A preliminary study

Abstract Quality control in nuclear medicine imaging is very imprortant which regularly carried out before running exam. The most important in QC is to evaluate QC parameter on SPECT images. Phantoms can be useful for evaluate QC parameters. The phantom was designed to quick QC SPECT system. Hot image from in-house hot phantom and cold image from Jaszczak phantom were used in this study with increasing activity concentration in in-house and Jaszczak phantom. This research is aimed to compare measurement results QC parameters of local uniformity and contrast on SPECT image generated with in-house and Jaszczak phantom. Measurement results from in-house hot phantom showed that lowest activity (0.99 MBq/mL) generated hardly visualized hot image for small diameter object (4.2 and 5.6 mm), and starting to be visible for 8.6 mm object with contrast value of 46.44. The same trend occurred to cold images generated by Jaszczak phantom, at lowest activity (0.068 MBq/mL) the ROI 8.6 mm on diameter object of 15.9 mm give rise to nearly the same contrast of that in hot image. No significant difference was found between local uniformity measured with in-house hot phantom and Jaszczak phantom. In-house hot phantom was robust and can be used as quick QC tool for SPECT system.</jats:p

Computer-Aided Detection of Mediastinal Lymph Nodes using Simple Architectural Convolutional Neural Network

Abstract Lung cancer is the most common and the deadliest cancer in the world. Lung cancer staging usually was done by radiologist by detecting mediastinal lymph node (LN) enlargement. Mediastinal LN is difficult to be detected visually due to its low contrast to the surrounding tissues, various size and shape, and sparse location. Therefore, computer-aided detection (CADe) system has been developed as a tool for radiologist to detect mediastinal LN automatically. The state of the art mediastinal LN CADe system use complex architectural convolutional neural network (CNN). However, more simple architecture of the CNN is needed to reduce the computational complexity of the CADe system, especially if the system was intended to be used in a regular computer. Therefore, in this experiment we used simple architectural 2D CNN which is converted to fully convolutional network (FCN) to detect mediastinal LN candidate in a stack of CT images. Then, the mediastinal LN candidates were classified using 3D CNN to reduce the false positive (FP). The best performance of this CADe system was 65% of sensitivity at 5 FP/patient.</jats:p

Automatic Detection of Breast Calcification in Ultrasound Imaging with Convolutional Neural Network

Abstract Breast cancer is a common type of cancer that leading death causes of female in the worldwide. Breast calcification can be one of indicator that can be used to detect the breast cancer early. One of the preferred methods used by radiologist to detect breast cancer is ultrasound imaging. Ultrasound imaging is much safer than mammography that followed by radiological effect. However, ultrasound imaging contaminated with speckle noise that looks similar to breast calcification. It can be the cause of the long time diagnosis process. It encourages so many methods of computed aided diagnosis (CADx) that can detect abnormalities automatically. One of them is Convolutional Neural Network (CNN). CNN can be used to classify the normal breast and breast with abnormalities. In this paper, CNN has been proposed for the classification of the ultrasound images into normal breasts and breasts with calcification. Experimental results classification accuracy was 76 % and a sensitivity of 84.61%.</jats:p

Computer-Aided Diagnosis (CAD) to Detect Abnormality on CT Image of Liver

Abstract Liver cancer on CT-scan image has different shapes, locations and textures in every image. The contrast difference between abnormal and healthy liver is often indistinguishable, making it difficult to evaluate. Liver abnormalities are such as swelling, fibrosis, and the presence of benign or malignant tumor. The difference of low contrast with wide size on the image is easily known as abnormality, but it is very hard to evaluate for small mass and low contrast. In this research, CAD was conducted to help the evaluation on liver abnormality, especially abnormality in small size. The research method used was active contour-based segmentation method. The research data were secondary data, the abdomen image was produced from the modality of Computed Tomography Scanner (CT-Scan) in Regional Public Hospital of Cibinong, Bogor. The data collection techniques were through observation on the image data of abnormal liver from either liver cancer patients, normal liver patients, as well as patients of other diseases as diagnosed by the doctor. Meanwhile, the data was processed through feature extraction process using the texture analysis of Gray-Level Co-occurrence Matrix (GLCM) with machine learning of Artificial Neural Network (ANN) to detect abnormality on image. The research stated that ANN can be used to categorize the images into normal and abnormal groups at 89% accuracy, 86% sensitivity, 92% specificity, 91% precision, and 10% overall error.</jats:p