There is a concern that image lag may reduce accuracy of real-time target tracking in radiotherapy. This study was
performed to investigate influence of image lag on the accuracy of target tracking in radiotherapy. Fluoroscopic images
were obtained using a direct type of dynamic flat-panel detector (FPD) system under conditions of target tracking during
radiotherapy. The images continued to be read out after
X-irradiations and cutoff, and image lag properties in the system
were then determined. Subsequently, a tungsten materials plate with a precision edge was mounted on to a motor control
device, which provided a constant velocity. The plate was moved into the center of the detector at movement rate of 10
and 20 mm/s, covering lung tumor movement of normal breathing, and MTF and profile curves were measured on the
edges covering and uncovering the detector. A lung tumor with blurred edge due to image lag was simulated using the
results and then superimposed on breathing chest radiographs of a patient. The moving target with and without image lag
was traced using a template-matching technique. In the results, the target could be traced within a margin for error in
external radiotherapy. The results indicated that there was no effect of image lag on target tracking in usual breathing
speed in a radiotherapy situation. Further studies are required to investigate influence by the other factors, such as
exposure dose, target size and shape, imaging rate, and thickness of a patient's body.
Acquisition and analysis of three-dimensional movement of knee joint is desired in orthopedic surgery. We have developed two methods to obtain dynamic volume images of knee joint. One is a 2D/3D registration method combining a bi-plane dynamic X-ray fluoroscopy and a static three-dimensional CT, the other is a method using so-called 4D-CT that uses a cone-beam and a wide 2D detector. In this paper, we present two analyses of knee joint movement obtained by these methods: (1) transition of the nearest points between femur and tibia (2) principal component analysis (PCA) of six parameters representing the three dimensional movement of knee. As a preprocessing for the analysis, at first the femur and tibia regions are extracted from volume data at each time frame and then the registration of the tibia between different frames by an affine transformation consisting of rotation and translation are performed. The same transformation is applied femur as well. Using those image data, the movement of femur relative to tibia can be analyzed. Six movement parameters of femur consisting of three translation parameters and three rotation parameters are obtained from those images. In the analysis (1), axis of each bone is first found and then the flexion angle of the knee joint is calculated. For each flexion angle, the minimum distance between femur and tibia and the location giving the minimum distance are found in both lateral condyle and medial condyle. As a result, it was observed that the movement of lateral condyle is larger than medial condyle. In the analysis (2), it was found that the movement of the knee can be represented by the first three principal components with precision of 99.58% and those three components seem to strongly relate to three major movements of femur in the knee bend known in orthopedic surgery.
The conventional respiratory-gated CT scan technique includes anatomic motion induced artifacts due to the low temporal resolution. They are a significant source of error in radiotherapy treatment planning for the thorax and upper abdomen. Temporal resolution and image quality are important factors to minimize planning target volume margin due to the respiratory motion. To achieve high temporal resolution and high signal-to-noise ratio, we developed a respiratory gated segment reconstruction algorithm and adapted it to Feldkamp-Davis-Kress algorithm (FDK) with a 256-detector row CT. The 256-detector row CT could scan approximately 100 mm in the cranio-caudal direction with 0.5 mm slice thickness in one rotation. Data acquisition for the RS-FDK relies on the assistance of the respiratory sensing system by a cine scan mode (table remains stationary). We evaluated RS-FDK in phantom study with the 256-detector row CT and compared it with full scan (FS-FDK) and HS-FDK results with regard to volume accuracy and image noise, and finally adapted the RS-FDK to an animal study. The RS-FDK gave a more accurate volume than the others and it had the same signal-to-noise ratio as the FS-FDK. In the animal study, the RS-FDK visualized the clearest edges of the liver and pulmonary vessels of all the algorithms. In conclusion, the RS-FDK algorithm has a capability of high temporal resolution and high signal-to-noise ratio. Therefore it will be useful when combined with new radiotherapy techniques including image guided radiation therapy (IGRT) and 4D radiation therapy.
We have developed a four-dimensional CT (4D CT) using continuous rotation of cone-beam x-ray. The maximum
nominal beam width of the 4D CT is 128 mm at the center of rotation in the longitudinal direction. In
order to obtain appropriate estimations of exposure dose, detailed single-slice dose profi les perpendicular to the
rotation axis including scattered radiation were measured in PMMA cylindrical phantoms, which were cylindrical
lucite phantoms of 160 mm and 320 mm diameter and 900 mm length. Dose profi les were measured with
a pin photodiode detector at the center and a peripheral point of 10 mm depth. A pin silicon photodiode sensor
with 3 × 3 × 3 mm sensitive region was used as an x-ray detector, which was scanned along longitudinal direction
in the phantom for beam widths of 20, 42, 74, 106 and 138 mm. The dose profi les had long tails caused
by scattered radiation more than 200 mm out of the beam width edge. The exposure dose covered 95 % was
distributed along about 360 mm length at the center and about 310 mm at the periphery, which was independent
of the beam width. Before the advent of multi-detector CT, CTDI100 was used to approximate integral dose for
clinical scan conditions. However, for 4D CT employing a variable beam width, the standard CTDI was not a
good estimation. This work was carried out to establish a method of the dose measurements including scattered
radiation for cone-beam CT such as 4D CT. In order to perform the dose assessment including scattered radiation,
dose measured length should be recommended to measure integral dose over beam widths plus at least 230
mm, which covered 95 % total exposure dose.
We have developed a prototype of 4-dimensional (4D) CT-scanner that employs continuous rotation of cone-beam. Because a cone-beam scan along a circle orbit did not collect a complete set of data to make rigid reconstruction of volume (3D image), it might bring disadvantages or artifacts. To examine effects of the cone-beam data collection on image quality, we have evaluated basic performances of the prototype and compared them to those of a state-of-the-art multi-detector (MD) CT-scanner. As the results image characteristics such as noise, uniformity, high contrast and low contrast detectability of 4D CT were independent of z-coordinate, and comparable to those of MD CT. The transverse spatial resolution of 4D CT was independent of z-coordinate, and showed slightly better performance than that of MD CT, while the longitudinal spatial resolution of 4D CT was the same as the transverse one, and much better than that of MD CT in the present scan conditions. Isotropic resolving power of 0.5mm was achieved for 4D CT. A Feldkamp artifact was observed in distortion measurement though its clinical meaning has not been clarified. Exposure dose measured with CT dose index (CTDI) for 4D CT was comparable to that for MD CT. As a whole our first model of 4D CT-scanner was successful to take a volume data of 10cm long along longitudinal direction in a single rotation scan with comparable image quality and exposure dose to the state-of-the-art MD CT-scanner.
The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.