Deformable image registration between pathological images and MR image via an optical macro image

Computed tomography (CT) and magnetic resonance (MR) imaging have been widely used for visualizing the inside of the human body. However, in many cases, pathological diagnosis is conducted through a biopsy or resection of an organ to evaluate the condition of tissues as definitive diagnosis. To provide more advanced information onto CT or MR image, it is necessary to reveal the relationship between tissue information and image signals. We propose a registration scheme for a set of PT images of divided specimens and a 3D-MR image by reference to an optical macro image (OM image) captured by an optical camera. We conducted a fundamental study using a resected human brain after the death of a brain cancer patient. We constructed two kinds of registration processes using the OM image as the base for both registrations to make conversion parameters between the PT and MR images. The aligned PT images had shapes similar to the OM image. On the other hand, the extracted cross-sectional MR image was similar to the OM image. From these resultant conversion parameters, the corresponding region on the PT image could be searched and displayed when an arbitrary pixel on the MR image was selected. The relationship between the PT and MR images of the whole brain can be analyzed using the proposed method. We confirmed that same regions between the PT and MR images could be searched and displayed using resultant information obtained by the proposed method. In terms of the accuracy of proposed method, the TREs were 0.56 ± 0.39 mm and 0.87 ± 0.42 mm. We can analyze the relationship between tissue information and MR signals using the proposed method

Stage-specific meniscal features predict progression of osteoarthritis of the knee: a retrospective cohort study using data from the osteoarthritis initiative

Abstract Background In the progression of osteoarthritis (OA) of the knee, a correlation between meniscal posterior segment injuries and medial meniscal extrusion has been reported, but there have been few reports on the relationship with the meniscal shape. The purpose of this study was to clarify the features of the meniscal shape involved in the progression of knee OA. Methods Data were obtained from the Osteoarthritis Initiative (OAI) database. We defined two sets of subjects. One set included 455 knees of subjects whose OA grade on the Kellgren Lawrence (KL) scale progressed in 24 months from baseline and the other set consisted of 455 knees with no progression. The OA progressed subjects were divided to three groups: the “OA change group”, KL0 and KL1 knees that progressed to KL2 and KL3; the “mild change group”, KL2 knees that progressed to KL3; and the “severe change group”, KL2 and KL3 knees that progressed to KL4. The no progression set was divided into three groups whose OA grade remained unchanged. We used magnetic resonance imaging data and manually measured seven items (longitudinal diameter [LD], anterior wedge thickness, anterior wedge width, posterior wedge width, posterior wedge thickness, anterior wedge angle, posterior wedge angle) from the sagittal slice and the extrusion from the coronal slice. These measurements were compared between knees with and without OA progression. Results In the “OA change group” and “mild change group”, the anterior and posterior wedge widths and the extrusion were significantly larger, but the anterior and the posterior wedge angles were significantly smaller. In the “severe change group,” the LD and the extrusion were significantly larger. In each group, there was no uniform tendency for the correlation coefficient of the parameters evaluated. Conclusions Our findings suggested (1) a larger meniscal LD at the baseline predicted progression of knee OA after 24 months and (2) a larger meniscal width and smaller meniscal angle predicted progression of knee OA after 24 months

Time-delay correction method for PET-based tumor tracking

The world’s first open-type positron emission tomography (PET), named OpenPET, is being developed at the National Institute of Radiological Sciences, Japan. We are aiming to employ the OpenPET in radiotherapy and to track the respiratory motion of a tumor in the thoracoabdominal region of a patient. Byusing PET images, we expect that the tumor itself can be directly visualizedwithout using radio-opaque markers. Our demonstration results using a small prototype OpenPET system showed that the system can output reconstructed images at about two frames per second with about a 2-s delay; this delay ismainly due to the reconstruction calculation time. In this paper, we developed a time-delay correction method for tumor tracking for the OpenPET. Since itis difficult to correct the time delay using only the tumor location at 2 s before, we assumed the introduction of another high-speed sensor to acquire the respiration phase for correction. In the proposed method, the relationship between the tumor motion and the additional sensor output signal is calculated by support vector regression and the time delay is corrected by using the obtained regression line.We simulated this time-delay correction method withcomputer-generated PET images which had actual respiration motionsobtained from clinical magnetic resonance imaging. As a result, we could track the tumor with 1.20± 0.91 mm mean error when we assumed the use of a belt-type respiratory motion sensor

Real-time identification of blood regions for hemostasis support in laparoscopic surgery

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