diff --git a/packaging_ventral_rootlets/run_inference_single_subject.py b/packaging_ventral_rootlets/run_inference_single_subject.py
index 14ec7aaf..56a91587 100644
--- a/packaging_ventral_rootlets/run_inference_single_subject.py
+++ b/packaging_ventral_rootlets/run_inference_single_subject.py
@@ -117,14 +117,31 @@ def main():
     parser = get_parser()
     args = parser.parse_args()
 
-    fname_file = args.i
-    fname_file_out = args.o
+    fname_file = os.path.expanduser(args.i)
+    fname_file_out = os.path.expanduser(args.o)
     print(f'\nFound {fname_file} file.')
 
+    # If the fname_file is .nii, gzip it
+    # This is needed, because the filename suffix must match the `file_ending` in `dataset.json`. And as the
+    # `file_ending` for the ventral model is `.nii.gz`, we gzip the input file if it is not already gzipped.
+    # Context: https://github.com/ivadomed/model-spinal-rootlets/issues/49
+    if not fname_file.endswith('.nii.gz'):
+        print('Compressing the input image...')
+        os.system('gzip -f {}'.format(fname_file))
+        fname_file = fname_file + '.gz'
+        print(f'Compressed {fname_file}')
+
+    # Add .gz suffix to the output file if not already present. This is needed because we gzip the input file.
+    if not fname_file_out.endswith('.gz'):
+        fname_file_out = fname_file_out + '.gz'
+
     # Create temporary directory in the temp to store the reoriented images
     tmpdir = tmp_create()
     # Copy the file to the temporary directory using shutil.copyfile
-    fname_file_tmp = os.path.join(tmpdir, os.path.basename(fname_file))
+    # NOTE: Add the `_0000` suffix, because nnUNet removes the last five characters:
+    # https://github.com/MIC-DKFZ/nnUNet/blob/master/nnunetv2/inference/predict_from_raw_data.py#L171C19-L172C51
+    # Context: https://github.com/ivadomed/model-spinal-rootlets/issues/49
+    fname_file_tmp = os.path.join(tmpdir, os.path.basename(add_suffix(fname_file, '_0000')))
     shutil.copyfile(fname_file, fname_file_tmp)
     print(f'Copied {fname_file} to {fname_file_tmp}')
 
@@ -138,8 +155,7 @@ def main():
         # reorient the image to LPI using SCT
         os.system('sct_image -i {} -setorient LPI -o {}'.format(fname_file_tmp, fname_file_tmp))
 
-    # NOTE: for individual images, the _0000 suffix is not needed.
-    # BUT, the images should be in a list of lists
+    # Note: even a single file must be in a list of lists
     fname_file_tmp_list = [[fname_file_tmp]]
 
     # Use fold_all (all train/val subjects were used for training) or specific fold(s)
@@ -169,7 +185,7 @@ def main():
 
     # initializes the network architecture, loads the checkpoint
     predictor.initialize_from_trained_model_folder(
-        join(args.path_model),
+        join(os.path.expanduser(args.path_model)),
         use_folds=folds_avail,
         checkpoint_name='checkpoint_final.pth' if not args.use_best_checkpoint else 'checkpoint_best.pth',
     )