PET-CT based automated lung nodule detection

An automatic method is presented in order to detect lung nodules in PET-CT studies. Using the foreground and background mean ratio independently in every nodule, we can detect the region of the nodules properly. The size and intensity of the lesions do not affect the result of the algorithm, although size constraints are present in the final classification step. The CT image is also used to classify the found lesions built on lung segmentation. We also deal with those cases when nearby and similar nodules are merged into one by a split-up post-processing step. With our method the time of the localization can be decreased from more than one hour to maximum five minutes. The method had been implemented and validated on real clinical cases in Interview Fusion clinical evaluation software (Mediso). Results indicate that our approach is very effective in detecting lung nodules and can be a valuable aid for physicians working in the daily routine of oncology.

New workflows and algorithms of bone scintigraphy based on SPECT-CT

Gold standard bone scintigraphy workflow contains acquisition of planar anterior and posterior images and if necessary, additional SPECTs as well. Planar acquisitions are time consuming and not enough for accurately locating hotspots. Current paper proposes a novel workflow for fast whole body bone SPECT scintigraphy. We present a novel stitching method to generate a whole body SPECT based on the SPECT projections. Our stitching method is performed on the projection series not on the reconstructed SPECTs, thus stitching artifacts are greatly reduced. Our workflow does not require any anterior-posterior image pairs, since these images are derived from the reconstructed whole body SPECT automatically. Our stitching method has been validated on real clinical data performed by medical physicians. Results show that our method is very effective for whole body SPECT generations leaving no signs of artifacts. Our workflow required overall 16 minutes to acquire a whole body SPECT which is comparable to the 60 minutes acquisition time required for gold standard techniques including planar images and additional SPECT acquisitions.

Automated material map generation from MRI scout pairs for preclinical PET attenuation correction

A novel method is presented to perform material map segmentation from preclinical MRI for corresponding PET attenuation correction. MRI does not provide attenuation ratio, hence segmenting a material map from it is challenging. Furthermore the MRI images often suffer from ghost artifacts. On the contrary MRI has no radiation dose. Our method operated with fast spin echo scout pairs that had perpendicular frequency directions. This way the direction of the ghost artifacts were perpendicular as well. Our body-air segmentation method built on this a priori information and successfully erased the ghost artifacts from the final binary mask. Visual and quantitative validation was performed by two preclinical specialists. Results indicate that our method is effective against MRI scout ghost artifacts and that PET attenuation correction based on MRI makes sense even on preclinical images.

An extended registration framework for the triple registration of IBZM SPECT, DATSCAN SPECT and MRI brain images to support the evaluation of brain dopamine receptor scintigraphies

An extended registration model is presented to register medical image triples acquired for brain dopamine receptor scintigraphies. The model operates with rigid and nonlinear transformations in parallel, where all transformation parameters are optimized by one optimization method. The concept of the transformation-sampling-similarity measurement minimizes the memory usage of a real implementation. A partial-fine sampling method is proposed to decrease the processing time of the registration. Real medical data was collected to compare our method with well-known prior ones. The first tests show that the model outperforms the classic registration methods in both speed and accuracy.

Hextuple registration of interim and follow-up PET-CT images for the accurate tracking of patient recovery after therapy

An extended registration framework is presented to accurately register follow-up PET-CT study triples. Since there are six images to register, sophisticated feature extraction and similarity measurement methods are proposed. An irregular sampling method is introduced to decrease the processing speed of the hextuple registration. The similarity measurement is based on a normalized hybrid extended SSD (Sum of Squared Differences) and and extended NMI (Normalized mutual Information). The method has been tested on a huge amount of simulated data to avoid observer specific results. Based on the validation, our method outperforms prior solutions in both speed and accuracy, hence it should be the subject of further investigations.

Automated lymph node detection and classification on breast and prostate cancer SPECT-CT images

We present a novel detection and classification method to process SPECT-CT images representing breast and prostate lymph nodes. Lymph nodes are those nodes that are near the primer tumor and may become cancerous in time, hence their early detection is a key factor for the successful treatment of the patient. Prior methods focus on the visual aid to manually detect the lymph nodes which still makes the process time-consuming. Other solutions segment the lymph nodes only on CT, where the small lymph nodes may not be located accurately. Our solution processed both SPECT and CT data to provide an accurate classification of all SPECT hot spots. The method has been validated on a huge amount of medical data. Results show that our method is a very effective tool to support physicians working with related images in the field of nuclear medicine.

Automated patient couch removal algorithm on CT images

The current paper proposes a novel automated patient couch removal method on Computed Tomography (CT) images. Patient couch is often considered to be an unnecessary artifact especially when 3D rendered techniques are used. The method is based on measuring similarity between selected axial slices and the assumption that the bed object is constant on different slices. Due to the weight of the patient the couch could bend which is identifiable as sagittal movement on consecutive axial slices. Therefore the method focuses on finding this movement after an initial segmentation. According to initial validation performed on real medical data, our method is an effective tool to remove patient couch without user interaction.

Parallel registration of multi-modal medical image triples having unknown inter-image geometry

A method is proposed to register three multimodal medical data, where none of the images are superimposed. Contrary to previously presented solutions that perform more simultaneous registrations after one-by-one, present method registers all images in parallel. The method minimizes the registration error by seeking the optimum of a vector including rigid transformation parameters of both reslice images. To measure the similarity among all three images, a higher dimensional extended normalized mutual information have been adopted. Comparison with simultaneous methods have been performed on brain and femoral multi-modal image triples. Based on the comparative results, presented parallel method significantly outperforms the simultaneous methods in both translation and rotation registration error minimizations. On the contrary, the simultaneous methods need less computational time to converge.