## 1 Introduction  
Sufficient bone volume is necessary for the successful placement of dental implants. However, bone volume is often lacking owing to dental trauma, tooth loss, or periodontal diseases. Several different techniques have been used to reconstruct alveolar defects in the atrophic maxilla and mandible [1, 2]. Among these, autogenous bone grafts remain the gold standard on account of their osteeconductive, osteoinductive, and osteogenic properties [3, 4]. The standard donor sites for free autogenous bone graft techniques are the chin or mandibular ramus, iliac crest, tibia, and calvaria. The advantages of intraoral mandibular harvest sites include easy access, low morbidity, short healing periods, minimal graft resorption, and maintenance of high bone density. Side-effects include possible postoperative sensory disturbances and discomfort [4; 5]. There are several published studies on bone volume changes in recipient sites following mandibular bone block grafts; however, studies on changes in donor sites are scarce [6]. Some studies have described a second harvest from the mandibular ramus and symphysis [7; 8]; nevertheless, the exact amount of bone regeneration that is possible and certain aspects of healing are not yet fully understood. Thus, there is a limitation with the current method of assessing bone volume. In the absence of a standardized method, studies have adopted a variety of methods for the assessment of bone changes. Dental panoramic radiographs are occasionally used to estimate the vertical dimension of the jawbones [9], while some studies have used MDCT (multidetector computed tomography) [10]. [PERSON] et al. [11] used a special software to reconstruct a three-dimensional (3D) model using cone-beam computed tomography (CBCT) and the estimated dimensions, whereas [PERSON] et al. [6] and [PERSON] et al. [12] used a 3D superposition technique.  
Currently, CBCT is routinely performed in patients who require bone grafts in dentistry; therefore, it can be used for 3D analysis without exposing patients to extra radiation. Furthermore, 3D imaging can be performed without direct contact with the patient and can, therefore, be used to decrease the spread of COVID 19 infection [13]. However, CBCT images are typically acquired using a small number of X-ray projections and/or low beam intensities. This often leads to images with low contrast, low signal-to-noise ratio, and artefacts, thereby hampering the image registration process [14].  
Thus far, the 3D evaluation of CBCT images requires cropping and performing calculations for multiple slices, which involves extensive manual work. To overcome these limitations, recent studies have integrated new techniques in CBCT image processing, one of which consists of analyzing the bone volume using intensity-based registration [15]. In addition, the authors attempted to assess bone quality by pixel value, which is controversial owing to the cone-beam geometry of CBCT [16]. The quantitative use of pixel values in CBCT is generally avoided because of the variability in pixel values. However, some studies have used histogram matching to perform quality analysis using pixel values [17; 18]. The accuracy of these techniques has not been proven. Nevertheless, it is still worth attempting them to overcome the limitations of CBCT image processing by integrating new steps.  
This study aimed to perform a 3D analysis of bone volume changes using intensity-based automatic registration and histogram matching. Furthermore, the gray value was assessed to evaluate the change in bone quality of donor sites in the mandible.