A posteriori registration and subtraction of panoramic compared with intraoral radiography

OBJECTIVES: To demonstrate the feasibility of panoramic image subtraction for implant assessment. STUDY DESIGN: Three titanium implants were inserted into a fresh pig mandible. One intraoral and 2 panoramic images were obtained at baseline and after each of 6 incremental (0.3, 0.6, 1.0, 1.5, 2.0, 2.5 mm) removals of bone. For each incremental removal of bone, the mandible was removed from and replaced in the holding device. Images representing incremental bone removals were registered by computer with the baseline images and subtracted. Assessment of the subtraction images was based on visual inspection and analysis of structured noise. RESULTS: Incremental bone removals were more visible in intraoral than in panoramic subtraction images; however, computer-based registration of panoramic images reduced the structured noise and enhanced the visibility of incremental removals. CONCLUSION: The feasibility of panoramic image subtraction for implant assessment was demonstrated.

Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

A comparative study on manual and automatic slice-to-volume registration of CT images

In order to assess the clinical relevance of a slice-to-volume registration algorithm, this technique was compared to manual registration. Reformatted images obtained from a diagnostic CT examination of the lower abdomen were reviewed and manually registered by 41 individuals. The results were refined by the algorithm. Furthermore, a fully automatic registration of the single slices to the whole CT examination, without manual initialization, was also performed. The manual registration error for rotation and translation was found to be 2.7+/-2.8 degrees and 4.0+/-2.5 mm. The automated registration algorithm significantly reduced the registration error to 1.6+/-2.6 degrees and 1.3+/-1.6 mm (p = 0.01). In 3 of 41 (7.3%) registration cases, the automated registration algorithm failed completely. On average, the time required for manual registration was 213+/-197 s; automatic registration took 82+/-15 s. Registration was also performed without any human interaction. The resulting registration error of the algorithm without manual pre-registration was found to be 2.9+/-2.9 degrees and 1.1+/-0.2 mm. Here, a registration took 91+/-6 s, on average. Overall, the automated registration algorithm improved the accuracy of manual registration by 59% in rotation and 325% in translation. The absolute values are well within a clinically relevant range.

Are They Really Lost? "True" Status and Reasons for Treatment Discontinuation among HIV Infected Patients on Antiretroviral Therapy Considered Lost to Follow Up in Urban Malawi

INTRODUCTION Patients who are lost to follow-up (LTFU) while on antiretroviral therapy (ART) pose challenges to the long-term success of ART programs. We describe the extent to which patients considered LTFU are misclassified as true disengagement from care when they are still alive on ART and explain reasons for ART discontinuation using our active tracing program to further improve ART retention programs and policies. METHODS We identified adult ART patients who missed clinic appointment by more than 3 weeks between January 2006 and December 2010, assuming that such patients would miss their doses of antiretroviral drugs. Patients considered LTFU who consented during ART registration were traced by phone or home visits; true ART status after tracing was documented. Reasons for ART discontinuation were also recorded for those who stopped ART. RESULTS Of the 4,560 suspected LTFU cases, 1,384 (30%) could not be traced. Of the 3,176 successfully traced patients, 952 (30%) were dead and 2,224 (70%) were alive, of which 2,183 (99.5%) started ART according to phone-based self-reports or physical verification during in-person interviews. Of those who started ART, 957 (44%) stopped ART and 1,226 (56%) reported still taking ART at the time of interview by sourcing drugs from another clinic, using alternative ART sources or making brief ART interruptions. Among 940 cases with reasons for ART discontinuations, failure to remember (17%), too weak/sick (12%), travel (46%), and lack of transport to the clinic (16%) were frequently cited; reasons differed by gender. CONCLUSION The LTFU category comprises sizeable proportions of patients still taking ART that may potentially bias retention estimates and misdirect resources at the clinic and national levels if not properly accounted for. Clinics should consider further decentralization efforts, increasing drug allocations for frequent travels, and improving communication on patient transfers between clinics to increase retention and adherence.